Merge branch 'master' into nlp

.circleci/config.yml

@@ -73,7 +73,7 @@ jobs:
         steps:
             - checkout
             - run: sudo pip install .[tf,torch,docs]
-            - run: cd docs && make html SPHINXOPTS="-W"
+            - run: cd docs && make html
             - store_artifacts:
                 path: ./docs/_build
     deploy_doc:

.circleci/deploy.sh

@@ -17,8 +17,7 @@ function deploy_doc(){
 	fi
 }
 
-# You can find the commit for each tag on https://github.com/huggingface/transformers/tags
-deploy_doc "master" master
+deploy_doc "master"
 deploy_doc "b33a385" v1.0.0
 deploy_doc "fe02e45" v1.1.0
 deploy_doc "89fd345" v1.2.0
@@ -28,11 +27,3 @@ deploy_doc "3616209" v2.2.0
 deploy_doc "d0f8b9a" v2.3.0
 deploy_doc "6664ea9" v2.4.0
 deploy_doc "fb560dc" v2.5.0
-deploy_doc "b90745c" v2.5.1
-deploy_doc "fbc5bf1" v2.6.0
-deploy_doc "6f5a12a" v2.7.0
-deploy_doc "11c3257" v2.8.0
-deploy_doc "e7cfc1a" v2.9.0
-deploy_doc "7cb203f" v2.9.1
-deploy_doc "10d7239" v2.10.0 
-deploy_doc "b42586e" #v2.11.0 Latest stable release

CONTRIBUTING.md

@@ -65,8 +65,7 @@ Awesome! Please provide the following information:
 If you are willing to contribute the model yourself, let us know so we can best
 guide you.
 
-We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them 
-in the [`templates`](https://github.com/huggingface/transformers/templates) folder.
+We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder.
 
 ### Do you want a new feature (that is not a model)?
 
@@ -87,9 +86,7 @@ A world-class feature request addresses the following points:
 If your issue is well written we're already 80% of the way there by the time you
 post it.
 
-We have added **templates** to guide you in the process of adding a new example script for training or testing the 
-models in the library. You can find them in the [`templates`](https://github.com/huggingface/transformers/templates) 
-folder.
+We have added **templates** to guide you in the process of adding a new example script for training or testing the models in the library. You can find them in the [`templates`](./templates) folder.
 
 ## Start contributing! (Pull Requests)
 
@@ -209,21 +206,15 @@ Follow these steps to start contributing:
    to be merged;
 4. Make sure existing tests pass;
 5. Add high-coverage tests. No quality testing = no merge. 
-   - If you are adding a new model, make sure that you use 
-     `ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)`, which triggers the common tests.
-   - If you are adding new `@slow` tests, make sure they pass using 
-     `RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`. 
-   - If you are adding a new tokenizer, write tests, and make sure 
-     `RUN_SLOW=1 python -m pytest tests/test_tokenization_{your_model_name}.py` passes.
-   CircleCI does not run the slow tests. 
-6. All public methods must have informative docstrings that work nicely with sphinx. See `modeling_ctrl.py` for an 
-   example.
+ - If you are adding a new model, make sure that you use `ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)`, which triggers the common tests.
+ - If you are adding new `@slow` tests, make sure they pass using `RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`. 
+ - If you are adding a new tokenizer, write tests, and make sure `RUN_SLOW=1 python -m pytest tests/test_tokenization_{your_model_name}.py` passes.
+CircleCI does not run them. 
+6. All public methods must have informative docstrings that work nicely with sphinx. See `modeling_ctrl.py` for an example.
 
 ### Tests
 
-An extensive test suite is included to test the library behavior and several examples. Library tests can be found in 
-the [tests folder](https://github.com/huggingface/transformers/tree/master/tests) and examples tests in the 
-[examples folder](https://github.com/huggingface/transformers/tree/master/examples).
+You can run 🤗 Transformers tests with `unittest` or `pytest`.
 
 We like `pytest` and `pytest-xdist` because it's faster. From the root of the
 repository, here's how to run tests with `pytest` for the library:
@@ -270,8 +261,7 @@ $ python -m unittest discover -s examples -t examples -v
 
 ### Style guide
 
-For documentation strings, `transformers` follows the [google style](https://google.github.io/styleguide/pyguide.html).
-Check our [documentation writing guide](https://github.com/huggingface/transformers/tree/master/docs#writing-documentation---specification)
-for more information.
+For documentation strings, `transformers` follows the [google
+style](https://google.github.io/styleguide/pyguide.html).
 
 #### This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md)

README.md

@@ -59,7 +59,7 @@ Choose the right framework for every part of a model's lifetime
 | [Quick tour: Share your models ](#Quick-tour-of-model-sharing) | Upload and share your fine-tuned models with the community |
 | [Migrating from pytorch-transformers to transformers](#Migrating-from-pytorch-transformers-to-transformers) | Migrating your code from pytorch-transformers to transformers |
 | [Migrating from pytorch-pretrained-bert to pytorch-transformers](#Migrating-from-pytorch-pretrained-bert-to-transformers) | Migrating your code from pytorch-pretrained-bert to transformers |
-| Documentation [(master)](https://huggingface.co/transformers/master) [(stable)](https://huggingface.co/transformers/) [(v2.10.0)](https://huggingface.co/transformers/v2.10.0) [(v2.9.0/v2.9.1)](https://huggingface.co/transformers/v2.9.1) [(v2.8.0)](https://huggingface.co/transformers/v2.8.0) [(v2.7.0)](https://huggingface.co/transformers/v2.7.0) [(v2.6.0)](https://huggingface.co/transformers/v2.6.0) [(v2.5.0/v2.5.1)](https://huggingface.co/transformers/v2.5.1) [(v2.4.0/v2.4.1)](https://huggingface.co/transformers/v2.4.0)[(v2.3.0)](https://huggingface.co/transformers/v2.3.0)[(v2.2.0/v2.2.1/v2.2.2)](https://huggingface.co/transformers/v2.2.0) [(v2.1.1)](https://huggingface.co/transformers/v2.1.1) [(v2.0.0)](https://huggingface.co/transformers/v2.0.0) [(v1.2.0)](https://huggingface.co/transformers/v1.2.0) [(v1.1.0)](https://huggingface.co/transformers/v1.1.0) [(v1.0.0)](https://huggingface.co/transformers/v1.0.0) | Full API documentation and more |
+| [Documentation][(v2.5.0)](https://huggingface.co/transformers/v2.5.0)[(v2.4.0/v2.4.1)](https://huggingface.co/transformers/v2.4.0)[(v2.3.0)](https://huggingface.co/transformers/v2.3.0)[(v2.2.0/v2.2.1/v2.2.2)](https://huggingface.co/transformers/v2.2.0) [(v2.1.1)](https://huggingface.co/transformers/v2.1.1) [(v2.0.0)](https://huggingface.co/transformers/v2.0.0) [(v1.2.0)](https://huggingface.co/transformers/v1.2.0) [(v1.1.0)](https://huggingface.co/transformers/v1.1.0) [(v1.0.0)](https://huggingface.co/transformers/v1.0.0) [(master)](https://huggingface.co/transformers) | Full API documentation and more |
 
 ## Installation
 
@@ -169,7 +169,7 @@ At some point in the future, you'll be able to seamlessly move from pre-training
 22. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
 23. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.
 
-These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations (e.g. ~93 F1 on SQuAD for BERT Whole-Word-Masking, ~88 F1 on RocStories for OpenAI GPT, ~18.3 perplexity on WikiText 103 for Transformer-XL, ~0.916 Pearson R coefficient on STS-B for XLNet). You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/transformers/examples.html).
+These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations (e.g. ~93 F1 on SQuAD for BERT Whole-Word-Masking, ~88 F1 on RocStories for OpenAI GPT, ~18.3 perplexity on WikiText 103 for Transformer-XL, ~0.916 Peason R coefficient on STS-B for XLNet). You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/transformers/examples.html).
 
 ## Online demo
 
@@ -538,21 +538,20 @@ You can create `Pipeline` objects for the following down-stream tasks:
  - `translation_xx_to_yy`
 
 ```python
->>> from transformers import pipeline
+from transformers import pipeline
 
 # Allocate a pipeline for sentiment-analysis
->>> nlp = pipeline('sentiment-analysis')
->>> nlp('We are very happy to include pipeline into the transformers repository.')
-[{'label': 'POSITIVE', 'score': 0.9978193640708923}]
+nlp = pipeline('sentiment-analysis')
+nlp('We are very happy to include pipeline into the transformers repository.')
+>>> {'label': 'POSITIVE', 'score': 0.99893874}
 
 # Allocate a pipeline for question-answering
->>> nlp = pipeline('question-answering')
->>> nlp({
-...     'question': 'What is the name of the repository ?',
-...     'context': 'Pipeline have been included in the huggingface/transformers repository'
-... })
-{'score': 0.5135612454720828, 'start': 35, 'end': 59, 'answer': 'huggingface/transformers'}
-
+nlp = pipeline('question-answering')
+nlp({
+    'question': 'What is the name of the repository ?',
+    'context': 'Pipeline have been included in the huggingface/transformers repository'
+})
+>>> {'score': 0.28756016668193496, 'start': 35, 'end': 59, 'answer': 'huggingface/transformers'}
 ```
 
 ## Migrating from pytorch-transformers to transformers

docs/README.md

@@ -42,14 +42,20 @@ pip install recommonmark
 
 ## Building the documentation
 
+Make sure that there is a symlink from the `example` file (in /examples) inside the source folder. Run the following
+command to generate it:
+
+```bash
+ln -s ../../examples/README.md examples.md
+```
+
 Once you have setup `sphinx`, you can build the documentation by running the following command in the `/docs` folder:
 
 ```bash
 make html
 ```
 
-A folder called ``_build/html`` should have been created. You can now open the file ``_build/html/index.html`` in your
-browser. 
+A folder called ``_build/html`` should have been created. You can now open the file ``_build/html/index.html`` in your browser. 
 
 ---
 **NOTE**
@@ -126,8 +132,8 @@ XXXConfig
     :members:
 ```
 
-This will include every public method of the configuration. If for some reason you wish for a method not to be
-displayed in the documentation, you can do so by specifying which methods should be in the docs:
+This will include every public method of the configuration. If for some reason you wish for a method not to be displayed
+in the documentation, you can do so by specifying which methods should be in the docs:
 
 ```
 XXXTokenizer
@@ -141,8 +147,8 @@ XXXTokenizer
 
 ### Writing source documentation
 
-Values that should be put in `code` should either be surrounded by double backticks: \`\`like so\`\` or be written as
-an object using the :obj: syntax: :obj:\`like so\`.
+Values that should be put in `code` should either be surrounded by double backticks: \`\`like so\`\` or be written as an object
+using the :obj: syntax: :obj:\`like so\`.
 
 When mentionning a class, it is recommended to use the :class: syntax as the mentioned class will be automatically
 linked by Sphinx: :class:\`transformers.XXXClass\`

docs/source/_static/css/huggingface.css

@@ -1,45 +1,5 @@
 /* Our DOM objects */
 
-/* Version control */
-
-.version-button {
-    background-color: #6670FF;
-    color: white;
-    border: none;
-    padding: 5px;
-    font-size: 15px;
-    cursor: pointer;
-}
-
-.version-button:hover, .version-button:focus {
-    background-color: #A6B0FF;
-}
- 
-.version-dropdown {
-    display: none;
-    background-color: #6670FF;
-    min-width: 160px;
-    overflow: auto;
-    font-size: 15px;
-}
-  
-.version-dropdown a {
-    color: white;
-    padding: 3px 4px;
-    text-decoration: none;
-    display: block;
-}
-  
-.version-dropdown a:hover {
-    background-color: #A6B0FF;
-}
-  
-.version-show {
-    display: block;
-}
-
-/* Framework selector */
-
 .framework-selector {
     display: flex;
     flex-direction: row;
@@ -78,7 +38,6 @@
 
 /* The research field on top of the toc tree */
 .wy-side-nav-search{
-    padding-top: 0;
     background-color: #6670FF;
 }
 

docs/source/_static/js/custom.js

@@ -1,26 +1,3 @@
-// These two things need to be updated at each release for the version selector.
-// Last stable version
-const stableVersion = "v2.11.0"
-// Dictionary doc folder to label
-const versionMapping = {
-    "master": "master",
-    "": "v2.11.0 (stable)",
-    "v2.10.0": "v2.10.0",
-    "v2.9.1": "v2.9.0/v2.9.1",
-    "v2.8.0": "v2.8.0",
-    "v2.7.0": "v2.7.0",
-    "v2.6.0": "v2.6.0",
-    "v2.5.1": "v2.5.0/v2.5.1",
-    "v2.4.0": "v2.4.0/v2.4.1",
-    "v2.3.0": "v2.3.0",
-    "v2.2.0": "v2.2.0/v2.2.1/v2.2.2",
-    "v2.1.1": "v2.1.1",
-    "v2.0.0": "v2.0.0",
-    "v1.2.0": "v1.2.0",
-    "v1.1.0": "v1.1.0",
-    "v1.0.0": "v1.0.0"
-}
-
 function addIcon() {
     const huggingFaceLogo = "https://huggingface.co/landing/assets/transformers-docs/huggingface_logo.svg";
     const image = document.createElement("img");
@@ -81,60 +58,6 @@ function addGithubButton() {
     document.querySelector(".wy-side-nav-search .icon-home").insertAdjacentHTML('afterend', div);
 }
 
-function addVersionControl() {
-    // To grab the version currently in view, we parse the url
-    const parts = location.toString().split('/');
-    let versionIndex = parts.length - 2
-    // Main classes and models are nested so we need to go deeper
-    if (parts[versionIndex] == "main_classes" || parts[versionIndex] == "model_doc") {
-        versionIndex = parts.length - 3
-    } 
-    const version = parts[versionIndex];
-
-    // Menu with all the links,
-    const versionMenu = document.createElement("div");
-
-    const htmlLines = [];
-    for (const [key, value] of Object.entries(versionMapping)) {
-        var urlParts = (key == "") ? [] : [key];
-        urlParts = urlParts.concat(parts.slice(versionIndex));
-        htmlLines.push(`<a href="${urlParts.join('/')}">${value}</a>`);
-    }
-
-    versionMenu.classList.add("version-dropdown");
-    versionMenu.innerHTML = htmlLines.join('\n');
-    
-    // Button for version selection
-    const versionButton = document.createElement("div");
-    versionButton.classList.add("version-button");
-    let label = (version == "transformers") ? stableVersion : version
-    versionButton.innerText = label.concat(" ▼");
-
-    // Toggle the menu when we click on the button
-    versionButton.addEventListener("click", () => {
-        versionMenu.classList.toggle("version-show");
-    });
-
-    // Hide the menu when we click elsewhere
-    window.addEventListener("click", (event) => {
-        if (event.target != versionButton){
-            versionMenu.classList.remove('version-show');
-        }
-    });
-
-    // Container
-    const div = document.createElement("div");
-    div.appendChild(versionButton);
-    div.appendChild(versionMenu);
-    div.style.paddingTop = '25px';
-    div.style.backgroundColor = '#6670FF';
-    div.style.display = 'block';
-    div.style.textAlign = 'center';
-
-    const scrollDiv = document.querySelector(".wy-side-scroll");
-    scrollDiv.insertBefore(div, scrollDiv.children[1]);
-}
-
 function addHfMenu() {
     const div = `
     <div class="menu">
@@ -226,7 +149,6 @@ function parseGithubButtons (){"use strict";var e=window.document,t=e.location,o
 
 function onLoad() {
     addIcon();
-    addVersionControl();
     addCustomFooter();
     addGithubButton();
     parseGithubButtons();

docs/source/conf.py

@@ -187,8 +187,8 @@ epub_title = project
 epub_exclude_files = ['search.html']
 
 def setup(app):
-    app.add_css_file('css/huggingface.css')
-    app.add_css_file('css/code-snippets.css')
+    app.add_stylesheet('css/huggingface.css')
+    app.add_stylesheet('css/code-snippets.css')
     app.add_js_file('js/custom.js')
 
 # -- Extension configuration -------------------------------------------------

docs/source/contributing.md

@@ -1 +0,0 @@
-../../CONTRIBUTING.md

docs/source/glossary.rst

@@ -1,41 +1,11 @@
 Glossary
-^^^^^^^^
-
-General terms
--------------
-
-- autoencoding models: see MLM
-- autoregressive models: see CLM
-- CLM: causal language modeling, a pretraining task where the model reads the texts in order and has to predict the
-  next word. It's usually done by reading the whole sentence but using a mask inside the model to hide the future 
-  tokens at a certain timestep.
-- MLM: masked language modeling, a pretraining task where the model sees a corrupted version of the texts, usually done
-  by masking some tokens randomly, and has to predict the original text.
-- multimodal: a task taht combines texts with another kind of inputs (for instance images).
-- NLG: natural language generation, all tasks related to generating text ( for instance talk with transformers,
-  translation)
-- NLP: natural language processing, a generic way to say "deal with texts".
-- NLU: natural language understanding, all tasks related to understanding what is in a text (for instance classifying
-  the whole text, individual words)
-- pretrained model: a model that has been pretrained on some data (for instance all of Wikipedia). Pretraining methods
-  involve a self-supervised objective, which can be reading the text and trying to predict the next word (see CLM) or 
-  masking some words and trying to predict them (see MLM).
-- RNN: recurrent neural network, a type of model that uses a loop over a layer to process texts.
-- seq2seq or sequence-to-sequence: models that generate a new sequence from an input, like translation models, or
-  summarization models (such as :doc:`Bart </model_doc/bart>` or :doc:`T5 </model_doc/t5>`).
-- token: a part of a sentence, usually a word, but can also be a subword (non-common words are often split in subwords)
-  or a punctuation symbol.
-
-Model inputs
-------------
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Every model is different yet bears similarities with the others. Therefore most models use the same inputs, which are
 detailed here alongside usage examples.
 
-.. _input-ids:
-
 Input IDs
-~~~~~~~~~
+--------------------------
 
 The input ids are often the only required parameters to be passed to the model as input. *They are token indices,
 numerical representations of tokens building the sequences that will be used as input by the model*.
@@ -54,52 +24,24 @@ The tokenizer takes care of splitting the sequence into tokens available in the
 
 ::
 
+    # Continuation of the previous script
     tokenized_sequence = tokenizer.tokenize(sequence)
-    print(tokenized_sequence)
+    assert tokenized_sequence == ['A', 'Titan', 'R', '##T', '##X', 'has', '24', '##GB', 'of', 'V', '##RA', '##M']
 
-The tokens are either words or subwords. Here for instance, "VRAM" wasn't in the model vocabulary, so it's been split
-in "V", "RA" and "M". To indicate those tokens are not separate words but parts of the same word, a double-dash is
-added for "RA" and "M":
-
-::
-
-    ['A', 'Titan', 'R', '##T', '##X', 'has', '24', '##GB', 'of', 'V', '##RA', '##M']
-
-These tokens can then be converted into IDs which are understandable by the model. This can be done by directly feeding
-the sentence to the tokenizer, which leverages the Rust implementation of
+These tokens can then be converted into IDs which are understandable by the model. Several methods are available for
+this, the recommended being `encode` or `encode_plus`, which leverage the Rust implementation of
 `huggingface/tokenizers <https://github.com/huggingface/tokenizers>`__ for peak performance.
 
 ::
 
-    encoded_sequence = tokenizer(sequence)["input_ids"]
-    print(encoded_sequence)
+    # Continuation of the previous script
+    encoded_sequence = tokenizer.encode(sequence)
+    assert encoded_sequence == [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102]
 
-The tokenizer returns a dictionary with all the arguments necessary for its corresponding model to work properly. The
-token indices are under the key "input_ids":
-
-::
-
-    [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102]
-
-Note that the tokenizer automatically adds "special tokens" (if the associated model rely on them) which are special
-IDs the model sometimes uses. If we decode the previous sequence of ids,
-
-::
-
-    tokenizer.decode(encoded_sequence)
-
-we will see 
-
-::
-
-    '[CLS] A Titan RTX has 24GB of VRAM [SEP]'
-
-because this is the way a :class:`~transformers.BertModel` is going to expect its inputs.
-
-.. _attention-mask:
+The `encode` and `encode_plus` methods automatically add "special tokens" which are special IDs the model uses.
 
 Attention mask
-~~~~~~~~~~~~~~
+--------------------------
 
 The attention mask is an optional argument used when batching sequences together. This argument indicates to the
 model which tokens should be attended to, and which should not.
@@ -114,107 +56,83 @@ For example, consider these two sequences:
     sequence_a = "This is a short sequence."
     sequence_b = "This is a rather long sequence. It is at least longer than the sequence A."
 
-    encoded_sequence_a = tokenizer(sequence_a)["input_ids"]
-    encoded_sequence_b = tokenizer(sequence_b)["input_ids"]
-    
-    len(encoded_sequence_a), len(encoded_sequence_b)
+    encoded_sequence_a = tokenizer.encode(sequence_a)
+    assert len(encoded_sequence_a) == 8
 
-The encoded versions have different lengths:
+    encoded_sequence_b = tokenizer.encode(sequence_b)
+    assert len(encoded_sequence_b) == 19
+
+These two sequences have different lengths and therefore can't be put together in a same tensor as-is. The first
+sequence needs to be padded up to the length of the second one, or the second one needs to be truncated down to
+the length of the first one.
+
+In the first case, the list of IDs will be extended by the padding indices:
 
 ::
 
-    (8, 19)
+    # Continuation of the previous script
+    padded_sequence_a = tokenizer.encode(sequence_a, max_length=19, pad_to_max_length=True)
 
-Therefore, we can't be put then together in a same tensor as-is. The first sequence needs to be padded up to the length
-of the second one, or the second one needs to be truncated down to the length of the first one.
+    assert padded_sequence_a == [101, 1188, 1110, 170, 1603, 4954,  119, 102,    0,    0,    0,    0,    0,    0,    0,    0,   0,   0,   0]
+    assert encoded_sequence_b == [101, 1188, 1110, 170, 1897, 1263, 4954, 119, 1135, 1110, 1120, 1655, 2039, 1190, 1103, 4954, 138, 119, 102]
 
-In the first case, the list of IDs will be extended by the padding indices. We can pass a list to the tokenizer and ask
-it to pad like this:
-
-::
-
-    padded_sequences = tokenizer([sequence_a, sequence_b], padding=True)
-    padded_sequences["input_ids"]
-
-We can see that 0s have been added on the right of the first sentence to make it the same length as the second one:
-
-::
-
-    [[101, 1188, 1110, 170, 1603, 4954, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-     [101, 1188, 1110, 170, 1897, 1263, 4954, 119, 1135, 1110, 1120, 1655, 2039, 1190, 1103, 4954, 138, 119, 102]]
-
-This can then be converted into a tensor in PyTorch or TensorFlow. The attention mask is a binary tensor indicating
+These can then be converted into a tensor in PyTorch or TensorFlow. The attention mask is a binary tensor indicating
 the position of the padded indices so that the model does not attend to them. For the
 :class:`~transformers.BertTokenizer`, :obj:`1` indicate a value that should be attended to while :obj:`0` indicate
-a padded value. This attention mask is in the dictionary returned by the tokenizer under the key "attention_mask":
+a padded value.
+
+The method :func:`~transformers.PreTrainedTokenizer.encode_plus` may be used to obtain the attention mask directly:
 
 ::
 
-    padded_sequences["attention_mask"]
+    # Continuation of the previous script
+    sequence_a_dict = tokenizer.encode_plus(sequence_a, max_length=19, pad_to_max_length=True)
 
-will give back
+    assert sequence_a_dict['input_ids'] == [101, 1188, 1110, 170, 1603, 4954, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    assert sequence_a_dict['attention_mask'] == [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
 
-::
-
-    [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
-
-.. _token-type-ids:
 
 Token Type IDs
-~~~~~~~~~~~~~~
+--------------------------
 
 Some models' purpose is to do sequence classification or question answering. These require two different sequences to
 be encoded in the same input IDs. They are usually separated by special tokens, such as the classifier and separator
 tokens. For example, the BERT model builds its two sequence input as such:
 
-::
-
-   # [CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]
-
-We can use our tokenizer to automatically generate such a sentence by passing the two sequences as two arguments (and
-not a list like before) like this:
-
 ::
 
     from transformers import BertTokenizer
     tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+
+    # [CLS] SEQ_A [SEP] SEQ_B [SEP]
+
     sequence_a = "HuggingFace is based in NYC"
     sequence_b = "Where is HuggingFace based?"
 
-    encoded_dict = tokenizer(sequence_a, sequence_b)
-    tokenizer.decode(encoded_dict["input_ids"])
-
-which will return:
-
-::
-
-    "[CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]"
+    encoded_sequence = tokenizer.encode(sequence_a, sequence_b)
+    assert tokenizer.decode(encoded_sequence) == "[CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]"
 
 This is enough for some models to understand where one sequence ends and where another begins. However, other models
-such as BERT have an additional mechanism, which are the token type IDs (also called segment IDs). They are a binary
-mask identifying the different sequences in the model.
+such as BERT have an additional mechanism, which are the segment IDs. The Token Type IDs are a binary mask identifying
+the different sequences in the model.
 
-The tokenizer returns in the dictionary under the key "token_type_ids":
+We can leverage :func:`~transformers.PreTrainedTokenizer.encode_plus` to output the Token Type IDs for us:
 
 ::
 
-    encoded_dict['token_type_ids']
+    # Continuation of the previous script
+    encoded_dict = tokenizer.encode_plus(sequence_a, sequence_b)
 
-will return
-
-::
-
-    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+    assert encoded_dict['input_ids'] == [101, 20164, 10932, 2271, 7954, 1110, 1359, 1107, 17520, 102, 2777, 1110, 20164, 10932, 2271, 7954, 1359, 136, 102]
+    assert encoded_dict['token_type_ids'] == [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
 
 The first sequence, the "context" used for the question, has all its tokens represented by :obj:`0`, whereas the
 question has all its tokens represented by :obj:`1`. Some models, like :class:`~transformers.XLNetModel` use an
 additional token represented by a :obj:`2`.
 
-.. _position-ids:
 
 Position IDs
-~~~~~~~~~~~~
+--------------------------
 
 The position IDs are used by the model to identify which token is at which position. Contrary to RNNs that have the
 position of each token embedded within them, transformers are unaware of the position of each token. The position
@@ -226,24 +144,13 @@ positional embeddings.
 Absolute positional embeddings are selected in the range ``[0, config.max_position_embeddings - 1]``. Some models
 use other types of positional embeddings, such as sinusoidal position embeddings or relative position embeddings.
 
-.. _feed-forward-chunking:
 
 Feed Forward Chunking
-~~~~~~~~~~~~~~~~~~~~~
+--------------------------
 
-In transformers two feed forward layers usually follows the self attention layer in each residual attention block.
-The intermediate embedding size of the feed forward layers is often bigger than the hidden size of the model (e.g.,
-for ``bert-base-uncased``). 
+In transformers two feed forward layers usually follows the self attention layer in each residual attention block. The intermediate embedding size of the feed forward layers is often bigger than the hidden size of the model (*e.g.* for ``bert-base-uncased``). 
 
-For an input of size ``[batch_size, sequence_length]``, the memory required to store the intermediate feed forward
-embeddings ``[batch_size, sequence_length, config.intermediate_size]`` can account for a large fraction of the memory
-use. The authors of `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_ noticed that since the
-computation is independent of the ``sequence_length`` dimension, it is mathematically equivalent to compute the output
-embeddings of both feed forward layers ``[batch_size, config.hidden_size]_0, ..., [batch_size, config.hidden_size]_n`` 
-individually and concat them afterward to ``[batch_size, sequence_length, config.hidden_size]`` with
-``n = sequence_length``, which trades increased computation time against reduced memory use, but yields a
-mathematically **equivalent** result.
+For an input of size ``[batch_size, sequence_length]``, the memory required to store the intermediate feed forward embeddings ``[batch_size, sequence_length, config.intermediate_size]`` can account for a large fraction of the memory use. The authors of `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_ noticed that since the computation is independent of the ``sequence_length`` dimension, it is mathematically equivalent to compute the output embeddings of both feed forward layers ``[batch_size, config.hidden_size]_0, ..., [batch_size, config.hidden_size]_n``  individually and concat them afterward to ``[batch_size, sequence_length, config.hidden_size]`` with ``n = sequence_length``, which trades increased computation time against reduced memory use, but yields a mathematically **equivalent** result.
 
-For models employing the function :func:`~.transformers.apply_chunking_to_forward`, the ``chunk_size`` defines the
-number of output embeddings that are computed in parallel and thus defines the trade-off between memory and time
-complexity.  If ``chunk_size`` is set to 0, no feed forward chunking is done.
+For models employing the function :func:`~.transformers.apply_chunking_to_forward`, the ``chunk_size`` defines the number of output embeddings that are computed in parallel and thus defines the trade-off between memory and time complexity. 
+If ``chunk_size`` is set to 0, no feed forward chunking is done.

docs/source/index.rst

@@ -1,18 +1,17 @@
 Transformers
 ================================================================================================================================================
 
-State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
+🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides general-purpose architectures
+(BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural Language Understanding (NLU) and Natural Language Generation
+(NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
 
-🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides general-purpose 
-architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural Language Understanding (NLU) and Natural 
-Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between 
-TensorFlow 2.0 and PyTorch.
-
-This is the documentation of our repository `transformers <https://github.com/huggingface/transformers>`_.
+This is the documentation of our repository `transformers <https://github.com/huggingface/transformers>`__.
 
 Features
 ---------------------------------------------------
 
+- As easy to use as pytorch-transformers
+- As powerful and concise as Keras
 - High performance on NLU and NLG tasks
 - Low barrier to entry for educators and practitioners
 
@@ -38,133 +37,46 @@ Choose the right framework for every part of a model's lifetime:
 Contents
 ---------------------------------
 
-The documentation is organized in five parts:
+The library currently contains PyTorch and Tensorflow implementations, pre-trained model weights, usage scripts and conversion utilities for the following models:
 
-- **GET STARTED** contains a quick tour, the installation instructions and some useful information about our philosophy
-  and a glossary.
-- **USING 🤗 TRANSFORMERS** contains general tutorials on how to use the library.
-- **ADVANCED GUIDES** contains more advanced guides that are more specific to a given script or part of the library.
-- **RESEARCH** focuses on tutorials that have less to do with how to use the library but more about general resarch in
-  transformers model
-- **PACKAGE REFERENCE** contains the documentation of each public class and function.
-
-The library currently contains PyTorch and Tensorflow implementations, pre-trained model weights, usage scripts and
-conversion utilities for the following models:
-
-1. `BERT <https://github.com/google-research/bert>`_ (from Google) released with the paper `BERT: Pre-training of Deep
-   Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`_ by Jacob Devlin, Ming-Wei
-   Chang, Kenton Lee, and Kristina Toutanova.
-2. `GPT <https://github.com/openai/finetune-transformer-lm>`_ (from OpenAI) released with the paper `Improving Language
-   Understanding by Generative Pre-Training <https://blog.openai.com/language-unsupervised>`_ by Alec Radford, Karthik
-   Narasimhan, Tim Salimans, and Ilya Sutskever.
-3. `GPT-2 <https://blog.openai.com/better-language-models>`_ (from OpenAI) released with the paper `Language Models are
-   Unsupervised Multitask Learners <https://blog.openai.com/better-language-models>`_ by Alec Radford, Jeffrey Wu,
-   Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever.
-4. `Transformer-XL <https://github.com/kimiyoung/transformer-xl>`_ (from Google/CMU) released with the paper
-   `Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`_ by
-   Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, and Ruslan Salakhutdinov.
-5. `XLNet <https://github.com/zihangdai/xlnet>`_ (from Google/CMU) released with the paper `​XLNet: Generalized
-   Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_ by Zhilin Yang, Zihang
-   Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V. Le.
-6. `XLM <https://github.com/facebookresearch/XLM>`_ (from Facebook) released together with the paper `Cross-lingual
-   Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_ by Guillaume Lample and Alexis Conneau.
-7. `RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_ (from Facebook), released together with
-   the paper a `Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_ by Yinhan Liu, Myle
-   Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin
-   Stoyanov.
-8. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`_ (from HuggingFace) released together
-   with the paper `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
-   <https://arxiv.org/abs/1910.01108>`_ by Victor Sanh, Lysandre Debut, and Thomas Wolf. The same method has been
-   applied to compress GPT2 into
-   `DistilGPT2 <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
-9. `CTRL <https://github.com/pytorch/fairseq/tree/master/examples/ctrl>`_ (from Salesforce), released together with the
-   paper `CTRL: A Conditional Transformer Language Model for Controllable Generation
-   <https://www.github.com/salesforce/ctrl>`_ by Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong,
-   and Richard Socher.
-10. `CamemBERT <https://huggingface.co/transformers/model_doc/camembert.html>`_ (from FAIR, Inria, Sorbonne Université)
-    released together with the paper `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`_ by
-    Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la
-    Clergerie, Djame Seddah, and Benoît Sagot.
-11. `ALBERT <https://github.com/google-research/ALBERT>`_ (from Google Research), released together with the paper
-    `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_
-    by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut.
-12. `T5 <https://github.com/google-research/text-to-text-transfer-transformer>`_ (from Google) released with the paper
-    `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
-    <https://arxiv.org/abs/1910.10683>`_ by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang,
-    Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu.
-13. `XLM-RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`_ (from Facebook AI), released together
-    with the paper `Unsupervised Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`_ by
-    Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard
-    Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov.
-14. `MMBT <https://github.com/facebookresearch/mmbt/>`_ (from Facebook), released together with the paper a `Supervised
-    Multimodal Bitransformers for Classifying Images and Text <https://arxiv.org/pdf/1909.02950.pdf>`_ by Douwe Kiela,
-    Suvrat Bhooshan, Hamed Firooz, and Davide Testuggine.
-15. `FlauBERT <https://github.com/getalp/Flaubert>`_ (from CNRS) released with the paper `FlauBERT: Unsupervised
-    Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`_ by Hang Le, Loïc Vial, Jibril Frej,
-    Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, and
-    Didier Schwab.
-16. `BART <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_ (from Facebook) released with the paper
-    `BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
-    <https://arxiv.org/pdf/1910.13461.pdf>`_ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman
-    Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer.
-17. `ELECTRA <https://github.com/google-research/electra>`_ (from Google Research/Stanford University) released with
-    the paper `ELECTRA: Pre-training text encoders as discriminators rather than generators
-    <https://arxiv.org/abs/2003.10555>`_ by Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning.
-18. `DialoGPT <https://github.com/microsoft/DialoGPT>`_ (from Microsoft Research) released with the paper `DialoGPT:
-    Large-Scale Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`_ by
-    Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu,
-    and Bill Dolan.
-19. `Reformer <https://github.com/google/trax/tree/master/trax/models/reformer>`_ (from Google Research) released with
-    the paper `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_ by Nikita Kitaev, Łukasz
-    Kaiser, and Anselm Levskaya.
-20. `MarianMT <https://marian-nmt.github.io/>`_ (developed by the Microsoft Translator Team) machine translation models
-    trained using `OPUS <http://opus.nlpl.eu/>`_ pretrained_models data by Jörg Tiedemann.
-21. `Longformer <https://github.com/allenai/longformer>`_ (from AllenAI) released with the paper `Longformer: The
-    Long-Document Transformer <https://arxiv.org/abs/2004.05150>`_ by Iz Beltagy, Matthew E. Peters, and Arman Cohan.
-22. `Other community models <https://huggingface.co/models>`_, contributed by the `community
-    <https://huggingface.co/users>`_.
+1. `BERT <https://github.com/google-research/bert>`_ (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`_ by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
+2. `GPT <https://github.com/openai/finetune-transformer-lm>`_ (from OpenAI) released with the paper `Improving Language Understanding by Generative Pre-Training <https://blog.openai.com/language-unsupervised>`_ by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
+3. `GPT-2 <https://blog.openai.com/better-language-models>`_ (from OpenAI) released with the paper `Language Models are Unsupervised Multitask Learners <https://blog.openai.com/better-language-models>`_ by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
+4. `Transformer-XL <https://github.com/kimiyoung/transformer-xl>`_ (from Google/CMU) released with the paper `Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`_ by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
+5. `XLNet <https://github.com/zihangdai/xlnet>`_ (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_ by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
+6. `XLM <https://github.com/facebookresearch/XLM>`_ (from Facebook) released together with the paper `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_ by Guillaume Lample and Alexis Conneau.
+7. `RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_ (from Facebook), released together with the paper a `Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
+8. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`_ (from HuggingFace) released together with the paper `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`_ by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into `DistilGPT2 <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
+9. `CTRL <https://github.com/pytorch/fairseq/tree/master/examples/ctrl>`_ (from Salesforce), released together with the paper `CTRL: A Conditional Transformer Language Model for Controllable Generation <https://www.github.com/salesforce/ctrl>`_ by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
+10. `CamemBERT <https://huggingface.co/transformers/model_doc/camembert.html>`_ (from FAIR, Inria, Sorbonne Université) released together with the paper `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`_ by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la Clergerie, Djame Seddah, and Benoît Sagot.
+11. `ALBERT <https://github.com/google-research/ALBERT>`_ (from Google Research), released together with the paper a `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_ by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
+12. `XLM-RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`_ (from Facebook AI), released together with the paper `Unsupervised Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`_ by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
+13. `FlauBERT <https://github.com/getalp/Flaubert>`_ (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`_ by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
 
 .. toctree::
     :maxdepth: 2
-    :caption: Get started
+    :caption: Notes
 
-    quicktour
     installation
-    philosophy
+    quickstart
     glossary
-
-.. toctree::
-    :maxdepth: 2
-    :caption: Using 🤗 Transformers
-
-    task_summary
-    model_summary
-    serialization
-    model_sharing
-    multilingual
-
-.. toctree::
-    :maxdepth: 2
-    :caption: Advanced guides
-
+    summary
     pretrained_models
+    usage
+    model_sharing
     examples
     notebooks
+    serialization
     converting_tensorflow_models
     migration
-    torchscript
-    contributing
-
-.. toctree::
-    :maxdepth: 2
-    :caption: Research
-
     bertology
+    torchscript
+    multilingual
     benchmarks
 
 .. toctree::
     :maxdepth: 2
-    :caption: Package Reference
+    :caption: Main classes
 
     main_classes/configuration
     main_classes/model
@@ -172,6 +84,11 @@ conversion utilities for the following models:
     main_classes/pipelines
     main_classes/optimizer_schedules
     main_classes/processors
+
+.. toctree::
+    :maxdepth: 2
+    :caption: Package Reference
+
     model_doc/auto
     model_doc/encoderdecoder
     model_doc/bert
@@ -194,5 +111,3 @@ conversion utilities for the following models:
     model_doc/reformer
     model_doc/marian
     model_doc/longformer
-    model_doc/retribert
-    model_doc/mobilebert

docs/source/installation.md

@@ -1,102 +1,51 @@
 # Installation
 
-🤗 Transformers is tested on Python 3.6+, and PyTorch 1.1.0+ or TensorFlow 2.0+.
+Transformers is tested on Python 3.6+ and PyTorch 1.1.0
 
-You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're
-unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). Create a virtual environment with the version of Python you're going 
-to use and activate it.
+## With pip
 
-Now, if you want to use 🤗 Transformers, you can install it with pip. If you'd like to play with the examples, you
-must install it from source.
+PyTorch Transformers can be installed using pip as follows:
 
-## Installation with pip
-
-First you need to install one of, or both, TensorFlow 2.0 and PyTorch.
-Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) 
-and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific 
-install command for your platform.
-
-When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:
-
-```bash
+``` bash
 pip install transformers
 ```
 
-Alternatively, for CPU-support only, you can install 🤗 Transformers and PyTorch in one line with
+## From source
 
-```bash
-pip install transformers[torch]
-```
-
-or 🤗 Transformers and TensorFlow 2.0 in one line with
-
-```bash
-pip install transformers[tf-cpu]
-```
-
-To check 🤗 Transformers is properly installed, run the following command:
-
-```bash
-python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I hate you'))"
-```
-
-It should download a pretrained model then print something like
-
-```bash
-[{'label': 'NEGATIVE', 'score': 0.9991129040718079}]
-```
-
-(Note that TensorFlow will print additional stuff before that last statement.)
-
-## Installing from source
-
-To install from source, clone the repository and install with the following commands:
+To install from source, clone the repository and install with:
 
 ``` bash
 git clone https://github.com/huggingface/transformers.git
 cd transformers
-pip install -e .
+pip install .
 ```
 
-Again, you can run 
+## Tests
 
-```bash
-python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I hate you'))"
+An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests folder](https://github.com/huggingface/transformers/tree/master/tests) and examples tests in the [examples folder](https://github.com/huggingface/transformers/tree/master/examples).
+
+Refer to the [contributing guide](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#tests) for details about running tests.
+
+## OpenAI GPT original tokenization workflow
+
+If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install `ftfy` and `SpaCy`:
+
+``` bash
+pip install spacy ftfy==4.4.3
+python -m spacy download en
 ```
 
-to check 🤗 Transformers is properly installed.
+If you don't install `ftfy` and `SpaCy`, the `OpenAI GPT` tokenizer will default to tokenize using BERT's `BasicTokenizer` followed by Byte-Pair Encoding (which should be fine for most usage, don't worry).
 
-## Caching models
+## Note on model downloads (Continuous Integration or large-scale deployments)
 
-This library provides pretrained models that will be downloaded and cached locally. Unless you specify a location with
-`cache_dir=...` when you use methods like `from_pretrained`, these models will automatically be downloaded in the
-folder given by the shell environment variable ``TRANSFORMERS_CACHE``. The default value for it will be the PyTorch
-cache home followed by ``/transformers/`` (even if you don't have PyTorch installed). This is (by order of priority):
-
-  * shell environment variable ``ENV_TORCH_HOME``
-  * shell environment variable ``ENV_XDG_CACHE_HOME`` + ``/torch/``
-  * default: ``~/.cache/torch/``
-
-So if you don't have any specific environment variable set, the cache directory will be at
-``~/.cache/torch/transformers/``.
-
-**Note:** If you have set a shell enviromnent variable for one of the predecessors of this library
-(``PYTORCH_TRANSFORMERS_CACHE`` or ``PYTORCH_PRETRAINED_BERT_CACHE``), those will be used if there is no shell
-enviromnent variable for ``TRANSFORMERS_CACHE``.
-
-### Note on model downloads (Continuous Integration or large-scale deployments)
-
-If you expect to be downloading large volumes of models (more than 1,000) from our hosted bucket (for instance through
-your CI setup, or a large-scale production deployment), please cache the model files on your end. It will be way
-faster, and cheaper. Feel free to contact us privately if you need any help.
+If you expect to be downloading large volumes of models (more than 1,000) from our hosted bucket (for instance through your CI setup, or a large-scale production deployment), please cache the model files on your end. It will be way faster, and cheaper. Feel free to contact us privately if you need any help.
 
 ## Do you want to run a Transformer model on a mobile device?
 
 You should check out our [swift-coreml-transformers](https://github.com/huggingface/swift-coreml-transformers) repo.
 
-It contains a set of tools to convert PyTorch or TensorFlow 2.0 trained Transformer models (currently contains `GPT-2`, 
-`DistilGPT-2`, `BERT`, and `DistilBERT`) to CoreML models that run on iOS devices.
+It contains a set of tools to convert PyTorch or TensorFlow 2.0 trained Transformer models (currently contains `GPT-2`, `DistilGPT-2`, `BERT`, and `DistilBERT`) to CoreML models that run on iOS devices.
 
-At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models in PyTorch or
-TensorFlow 2.0 to productizing them in CoreML, or prototype a model or an app in CoreML then research its
-hyperparameters or architecture from PyTorch or TensorFlow 2.0. Super exciting!
+At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models in PyTorch to productizing them in CoreML,
+or prototype a model or an app in CoreML then research its hyperparameters or architecture from PyTorch. Super exciting!

docs/source/main_classes/optimizer_schedules.rst

@@ -17,6 +17,7 @@ The ``.optimization`` module provides:
 ~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AdamWeightDecay
+    :members:
 
 .. autofunction:: transformers.create_optimizer
 

docs/source/main_classes/pipelines.rst

@@ -7,7 +7,7 @@ Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction an
 
 There are two categories of pipeline abstractions to be aware about:
 
-- The :func:`~transformers.pipeline` which is the most powerful object encapsulating all other pipelines
+- The :class:`~transformers.pipeline` which is the most powerful object encapsulating all other pipelines
 - The other task-specific pipelines, such as :class:`~transformers.TokenClassificationPipeline`
   or :class:`~transformers.QuestionAnsweringPipeline`
 
@@ -17,7 +17,8 @@ The pipeline abstraction
 The `pipeline` abstraction is a wrapper around all the other available pipelines. It is instantiated as any
 other pipeline but requires an additional argument which is the `task`.
 
-.. autofunction:: transformers.pipeline
+.. autoclass:: transformers.pipeline
+    :members:
 
 
 The task specific pipelines

docs/source/migration.md

@@ -1,8 +1,8 @@
 # Migrating from previous packages
 
-## Migrating from pytorch-transformers to 🤗 Transformers
+## Migrating from pytorch-transformers to transformers
 
-Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to 🤗 Transformers.
+Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to `transformers`.
 
 ### Positional order of some models' keywords inputs (`attention_mask`, `token_type_ids`...) changed
 
@@ -14,17 +14,17 @@ If you used to call the models with positional inputs for keyword arguments, e.g
 
 ## Migrating from pytorch-pretrained-bert
 
-Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to 🤗 Transformers
+Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to `transformers`
 
 ### Models always output `tuples`
 
-The main breaking change when migrating from `pytorch-pretrained-bert` to 🤗 Transformers is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters.
+The main breaking change when migrating from `pytorch-pretrained-bert` to `transformers` is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters.
 
 The exact content of the tuples for each model are detailled in the models' docstrings and the [documentation](https://huggingface.co/transformers/).
 
 In pretty much every case, you will be fine by taking the first element of the output as the output you previously used in `pytorch-pretrained-bert`.
 
-Here is a `pytorch-pretrained-bert` to 🤗 Transformers conversion example for a `BertForSequenceClassification` classification model:
+Here is a `pytorch-pretrained-bert` to `transformers` conversion example for a `BertForSequenceClassification` classification model:
 
 ```python
 # Let's load our model
@@ -33,11 +33,11 @@ model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
 # If you used to have this line in pytorch-pretrained-bert:
 loss = model(input_ids, labels=labels)
 
-# Now just use this line in 🤗 Transformers to extract the loss from the output tuple:
+# Now just use this line in transformers to extract the loss from the output tuple:
 outputs = model(input_ids, labels=labels)
 loss = outputs[0]
 
-# In 🤗 Transformers you can also have access to the logits:
+# In transformers you can also have access to the logits:
 loss, logits = outputs[:2]
 
 # And even the attention weights if you configure the model to output them (and other outputs too, see the docstrings and documentation)
@@ -109,7 +109,7 @@ for batch in train_data:
     loss.backward()
     optimizer.step()
 
-### In 🤗 Transformers, optimizer and schedules are splitted and instantiated like this:
+### In Transformers, optimizer and schedules are splitted and instantiated like this:
 optimizer = AdamW(model.parameters(), lr=lr, correct_bias=False)  # To reproduce BertAdam specific behavior set correct_bias=False
 scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)  # PyTorch scheduler
 ### and used like this:

docs/source/model_doc/auto.rst

@@ -1,15 +1,11 @@
 AutoModels
 -----------
 
-In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you
-are supplying to the ``from_pretrained`` method.
+In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you are supplying to the ``from_pretrained`` method.
 
-AutoClasses are here to do this job for you so that you automatically retrieve the relevant model given the name/path
-to the pretrained weights/config/vocabulary:
+AutoClasses are here to do this job for you so that you automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary:
 
-Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will directly create a class of the relevant
-architecture (ex: ``model = AutoModel.from_pretrained('bert-base-cased')`` will create a instance of 
-:class:`~transformers.BertModel`).
+Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will directly create a class of the relevant architecture (ex: ``model = AutoModel.from_pretrained('bert-base-cased')`` will create a instance of ``BertModel``).
 
 
 ``AutoConfig``
@@ -34,76 +30,36 @@ architecture (ex: ``model = AutoModel.from_pretrained('bert-base-cased')`` will
 
 
 ``AutoModelForPreTraining``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelForPreTraining
     :members:
 
 
 ``AutoModelWithLMHead``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelWithLMHead
     :members:
 
 
 ``AutoModelForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelForSequenceClassification
     :members:
 
 
 ``AutoModelForQuestionAnswering``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelForQuestionAnswering
     :members:
 
 
 ``AutoModelForTokenClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelForTokenClassification
     :members:
 
-``TFAutoModel``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModel
-    :members:
-
-
-``TFAutoModelForPreTraining``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForPreTraining
-    :members:
-
-
-``TFAutoModelWithLMHead``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelWithLMHead
-    :members:
-
-
-``TFAutoModelForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForSequenceClassification
-    :members:
-
-
-``TFAutoModelForQuestionAnswering``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForQuestionAnswering
-    :members:
-
-
-``TFAutoModelForTokenClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForTokenClassification
-    :members:

docs/source/model_doc/encoderdecoder.rst

@@ -1,5 +1,5 @@
 Encoder Decoder Models
-------------------------
+-----------
 
 This class can wrap an encoder model, such as ``BertModel`` and a decoder modeling with a language modeling head, such as ``BertForMaskedLM`` into a encoder-decoder model.
 
@@ -10,7 +10,7 @@ An application of this architecture could be *summarization* using two pretraine
 
 
 ``EncoderDecoderConfig``
-~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.EncoderDecoderConfig
     :members:

docs/source/model_doc/gpt.rst

@@ -38,17 +38,6 @@ Hugging Face showcasing the generative capabilities of several models. GPT is on
 
 The original code can be found `here <https://github.com/openai/finetune-transformer-lm>`_.
 
-Note:
-
-If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install 
-``ftfy`` and ``SpaCy``::
-
-    pip install spacy ftfy==4.4.3
-    python -m spacy download en
-
-If you don't install ``ftfy`` and ``SpaCy``, the :class:`transformers.OpenAIGPTTokenizer` will default to tokenize using 
-BERT's :obj:`BasicTokenizer` followed by Byte-Pair Encoding (which should be fine for most usage, don't 
-worry).
 
 OpenAIGPTConfig
 ~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/mobilebert.rst

@@ -1,169 +0,0 @@
-MobileBERT
-----------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~
-
-The MobileBERT model was proposed in `MobileBERT: a Compact Task-Agnostic BERT
-for Resource-Limited Devices <https://arxiv.org/abs/2004.02984>`__
-by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou. It's a bidirectional transformer
-based on the BERT model, which is compressed and accelerated using several approaches.
-
-The abstract from the paper is the following:
-
-*Natural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds
-of millions of parameters. However, these models suffer from heavy model sizes and high latency such that they cannot
-be deployed to resource-limited mobile devices. In this paper, we propose MobileBERT for compressing and accelerating
-the popular BERT model. Like the original BERT, MobileBERT is task-agnostic, that is, it can be generically applied
-to various downstream NLP tasks via simple fine-tuning. Basically, MobileBERT is a thin version of BERT_LARGE, while
-equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward
-networks. To train MobileBERT, we first train a specially designed teacher model, an inverted-bottleneck incorporated
-BERT_LARGE model. Then, we conduct knowledge transfer from this teacher to MobileBERT. Empirical studies show that
-MobileBERT is 4.3x smaller and 5.5x faster than BERT_BASE while achieving competitive results on well-known
-benchmarks. On the natural language inference tasks of GLUE, MobileBERT achieves a GLUEscore o 77.7
-(0.6 lower than BERT_BASE), and 62 ms latency on a Pixel 4 phone. On the SQuAD v1.1/v2.0 question answering task,
-MobileBERT achieves a dev F1 score of 90.0/79.2 (1.5/2.1 higher than BERT_BASE).*
-
-Tips:
-
-- MobileBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on
-  the right rather than the left.
-- MobileBERT is similar to BERT and therefore relies on the masked language modeling (MLM) objective.
-  It is therefore efficient at predicting masked tokens and at NLU in general, but is not optimal for
-  text generation. Models trained with a causal language modeling (CLM) objective are better in that regard.
-
-The original code can be found `here <https://github.com/google-research/mobilebert>`_.
-
-MobileBertConfig
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MobileBertConfig
-    :members:
-
-
-MobileBertTokenizer
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MobileBertTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-
-MobileBertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MobileBertTokenizerFast
-    :members:
-
-
-MobileBertModel
-~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MobileBertModel
-    :members:
-
-
-MobileBertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MobileBertForPreTraining
-    :members:
-
-
-MobileBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MobileBertForMaskedLM
-    :members:
-
-
-MobileBertForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MobileBertForNextSentencePrediction
-    :members:
-
-
-MobileBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MobileBertForSequenceClassification
-    :members:
-
-
-MobileBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MobileBertForMultipleChoice
-    :members:
-
-
-MobileBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MobileBertForTokenClassification
-    :members:
-
-
-MobileBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MobileBertForQuestionAnswering
-    :members:
-
-
-TFMobileBertModel
-~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMobileBertModel
-    :members:
-
-
-TFMobileBertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMobileBertForPreTraining
-    :members:
-
-
-TFMobileBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMobileBertForMaskedLM
-    :members:
-
-
-TFMobileBertForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMobileBertForNextSentencePrediction
-    :members:
-
-
-TFMobileBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMobileBertForSequenceClassification
-    :members:
-
-
-TFMobileBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMobileBertForMultipleChoice
-    :members:
-
-
-TFMobileBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMobileBertForTokenClassification
-    :members:
-
-
-TFMobileBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMobileBertForQuestionAnswering
-    :members:
-

docs/source/model_doc/reformer.rst

@@ -4,7 +4,7 @@ Reformer
 file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`_
 
 Overview
-~~~~~~~~~~
+~~~~~
 The Reformer model was presented in `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451.pdf>`_ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
 Here the abstract: 
 
@@ -13,7 +13,7 @@ Here the abstract:
 The Authors' code can be found `here <https://github.com/google/trax/tree/master/trax/models/reformer>`_ .
 
 Axial Positional Encodings
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~
 Axial Positional Encodings were first implemented in Google's `trax library <https://github.com/google/trax/blob/4d99ad4965bab1deba227539758d59f0df0fef48/trax/layers/research/position_encodings.py#L29>`_ and developed by the authors of this model's paper. In models that are treating very long input sequences, the conventional position id encodings store an embedings vector of size :math:`d` being the ``config.hidden_size`` for every position :math:`i, \ldots, n_s`, with :math:`n_s` being ``config.max_embedding_size``. *E.g.*, having a sequence length of :math:`n_s = 2^{19} \approx 0.5M` and a ``config.hidden_size`` of :math:`d = 2^{10} \approx 1000` would result in a position encoding matrix:
 
 .. math::

docs/source/model_doc/retribert.rst

@@ -1,39 +0,0 @@
-RetriBERT
-----------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~
-
-The RetriBERT model was proposed in the blog post
-`Explain Anything Like I'm Five: A Model for Open Domain Long Form Question Answering <https://yjernite.github.io/lfqa.html>`__,
-RetriBERT is a small model that uses either a single or pair of Bert encoders with lower-dimension projection for dense semantic indexing of text.
-
-Code to train and use the model can be found `here <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
-
-
-RetriBertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RetriBertConfig
-    :members:
-
-
-RetriBertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RetriBertTokenizer
-    :members:
-
-
-RetriBertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RetriBertTokenizerFast
-    :members:
-
-
-RetriBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RetriBertModel
-    :members:

docs/source/model_doc/t5.rst

@@ -31,7 +31,7 @@ Training
 
 T5 is an encoder-decoder model and converts all NLP problems into a text-to-text format. It is trained using teacher forcing.
 This means that for training we always need an input sequence and a target sequence. 
-The input sequence is fed to the model using ``input_ids``. The target sequence is shifted to the right, *i.e.* prepended by a start-sequence token and fed to the decoder using the `decoder_input_ids`. In teacher-forcing style, the target sequence is then appended by the EOS token and corresponds to the ``labels``. The PAD token is hereby used as the start-sequence token.
+The input sequence is fed to the model using ``input_ids``. The target sequence is shifted to the right, *i.e.* prepended by a start-sequence token and fed to the decoder using the `decoder_input_ids`. In teacher-forcing style, the target sequence is then appended by the EOS token and corresponds to the ``lm_labels``. The PAD token is hereby used as the start-sequence token.
 T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.
 
 - Unsupervised denoising training
@@ -44,9 +44,9 @@ T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.
 ::
 
   input_ids = tokenizer.encode('The <extra_id_1> walks in <extra_id_2> park', return_tensors='pt')
-  labels = tokenizer.encode('<extra_id_1> cute dog <extra_id_2> the <extra_id_3> </s>', return_tensors='pt')
+  lm_labels = tokenizer.encode('<extra_id_1> cute dog <extra_id_2> the <extra_id_3> </s>', return_tensors='pt')
   # the forward function automatically creates the correct decoder_input_ids
-  model(input_ids=input_ids, labels=labels)
+  model(input_ids=input_ids, lm_labels=lm_labels)
 
 - Supervised training
 
@@ -57,9 +57,9 @@ T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.
 ::
 
   input_ids = tokenizer.encode('translate English to German: The house is wonderful. </s>', return_tensors='pt')
-  labels = tokenizer.encode('Das Haus ist wunderbar. </s>', return_tensors='pt')
+  lm_labels = tokenizer.encode('Das Haus ist wunderbar. </s>', return_tensors='pt')
   # the forward function automatically creates the correct decoder_input_ids
-  model(input_ids=input_ids, labels=labels)
+  model(input_ids=input_ids, lm_labels=lm_labels)
 
 
 T5Config

docs/source/philosophy.rst

@@ -1,73 +0,0 @@
-Philosophy
-==========
-
-🤗 Transformers is an opinionated library built for:
-
-- NLP researchers and educators seeking to use/study/extend large-scale transformers models
-- hands-on practitioners who want to fine-tune those models and/or serve them in production
-- engineers who just want to download a pretrained model and use it to solve a given NLP task.
-
-The library was designed with two strong goals in mind:
-
-- Be as easy and fast to use as possible:
-
-    - We strongly limited the number of user-facing abstractions to learn, in fact, there are almost no abstractions,
-      just three standard classes required to use each model: :doc:`configuration <main_classes/configuration>`, 
-      :doc:`models <main_classes/model>` and :doc:`tokenizer <main_classes/tokenizer>`.
-    - All of these classes can be initialized in a simple and unified way from pretrained instances by using a common
-      :obj:`from_pretrained()` instantiation method which will take care of downloading (if needed), caching and
-      loading the related class instance and associated data (configurations' hyper-parameters, tokenizers' vocabulary, 
-      and models' weights) from a pretrained checkpoint provided on 
-      `Hugging Face Hub <https://huggingface.co/models>`__ or your own saved checkpoint.
-    - On top of those three base classes, the library provides two APIs: :func:`~transformers.pipeline` for quickly
-      using a model (plus its associated tokenizer and configuration) on a given task and 
-      :func:`~transformers.Trainer`/:func:`~transformers.TFTrainer` to quickly train or fine-tune a given model.
-    - As a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to
-      extend/build-upon the library, just use regular Python/PyTorch/TensorFlow/Keras modules and inherit from the base
-      classes of the library to reuse functionalities like model loading/saving.
-
-- Provide state-of-the-art models with performances as close as possible to the original models:
-
-    - We provide at least one example for each architecture which reproduces a result provided by the official authors
-      of said architecture.
-    - The code is usually as close to the original code base as possible which means some PyTorch code may be not as
-      *pytorchic* as it could be as a result of being converted TensorFlow code and vice versa.
-
-A few other goals:
-
-- Expose the models' internals as consistently as possible:
-
-    - We give access, using a single API, to the full hidden-states and attention weights.
-    - Tokenizer and base model's API are standardized to easily switch between models.
-
-- Incorporate a subjective selection of promising tools for fine-tuning/investigating these models:
-
-    - A simple/consistent way to add new tokens to the vocabulary and embeddings for fine-tuning.
-    - Simple ways to mask and prune transformer heads.
-
-- Switch easily between PyTorch and TensorFlow 2.0, allowing training using one framwork and inference using another.
-
-Main concepts
-~~~~~~~~~~~~~
-
-The library is build around three types of classes for each model:
-
-- **Model classes**  such as :class:`~transformers.BertModel`, which are 30+ PyTorch models 
-  (`torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__) or Keras models 
-  (`tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__) that work with the pretrained
-  weights provided in the library.
-- **Configuration classes** such as :class:`~transformers.BertConfig`, which store all the parameters required to build
-  a model. You don't always need to instantiate these yourself. In particular, if you are using a pretrained model
-  without any modification, creating the model will automatically take care of instantiating the configuration (which
-  is part of the model).
-- **Tokenizer classes** such as :class:`~transformers.BertTokenizer`, which store the vocabulary for each model and
-  provide methods for encoding/decoding strings in a list of token embeddings indices to be fed to a model.
-
-All these classes can be instantiated from pretrained instances and saved locally using two methods:
-
-- :obj:`from_pretrained()` let you instantiate a model/configuration/tokenizer from a pretrained version either
-  provided by the library itself (the suported models are provided in the list :doc:`here <pretrained_models>`
-  or stored locally (or on a server) by the user,
-- :obj:`save_pretrained()` let you save a model/configuration/tokenizer locally so that it can be reloaded using
-  :obj:`from_pretrained()`.
-

docs/source/pretrained_models.rst

@@ -22,12 +22,10 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-base-multilingual-uncased``                         | | (Original, not recommended) 12-layer, 768-hidden, 12-heads, 110M parameters.                                                        |
 |                   |                                                            | | Trained on lower-cased text in the top 102 languages with the largest Wikipedias                                                    |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__).                                              |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-base-multilingual-cased``                           | | (New, **recommended**) 12-layer, 768-hidden, 12-heads, 110M parameters.                                                             |
 |                   |                                                            | | Trained on cased text in the top 104 languages with the largest Wikipedias                                                          |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__).                                              |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-base-chinese``                                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
@@ -35,79 +33,64 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-base-german-cased``                                 | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on cased German text by Deepset.ai                                                                                          |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details on deepset.ai website <https://deepset.ai/german-bert>`__).                                                             |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-large-uncased-whole-word-masking``                  | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
 |                   |                                                            | | Trained on lower-cased English text using Whole-Word-Masking                                                                        |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/google-research/bert/#bert>`__).                                                                    |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-large-cased-whole-word-masking``                    | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
 |                   |                                                            | | Trained on cased English text using Whole-Word-Masking                                                                              |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/google-research/bert/#bert>`__).                                                                    |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-large-uncased-whole-word-masking-finetuned-squad``  | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
 |                   |                                                            | | The ``bert-large-uncased-whole-word-masking`` model fine-tuned on SQuAD                                                             |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see details of fine-tuning in the `example section <https://github.com/huggingface/transformers/tree/master/examples>`__).           |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-large-cased-whole-word-masking-finetuned-squad``    | | 24-layer, 1024-hidden, 16-heads, 340M parameters                                                                                    |
 |                   |                                                            | | The ``bert-large-cased-whole-word-masking`` model fine-tuned on SQuAD                                                               |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                           |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-base-cased-finetuned-mrpc``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | The ``bert-base-cased`` model fine-tuned on MRPC                                                                                    |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                           |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-base-german-dbmdz-cased``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on cased German text by DBMDZ                                                                                               |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-base-german-dbmdz-uncased``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on uncased German text by DBMDZ                                                                                             |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``cl-tohoku/bert-base-japanese``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece.                                                               |
 |                   |                                                            | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization.                                                          |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``cl-tohoku/bert-base-japanese-whole-word-masking``        | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized with MeCab and WordPiece.                                      |
 |                   |                                                            | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization.                                                          |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``cl-tohoku/bert-base-japanese-char``                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on Japanese text. Text is tokenized into characters.                                                                        |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``cl-tohoku/bert-base-japanese-char-whole-word-masking``   | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized into characters.                                               |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``TurkuNLP/bert-base-finnish-cased-v1``                    | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on cased Finnish text.                                                                                                      |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``TurkuNLP/bert-base-finnish-uncased-v1``                  | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on uncased Finnish text.                                                                                                    |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``wietsedv/bert-base-dutch-cased``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on cased Dutch text.                                                                                                        |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details on wietsedv repository <https://github.com/wietsedv/bertje/>`__).                                                       |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | GPT               | ``openai-gpt``                                             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
@@ -166,67 +149,54 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | RoBERTa           | ``roberta-base``                                           | | 12-layer, 768-hidden, 12-heads, 125M parameters                                                                                     |
 |                   |                                                            | | RoBERTa using the BERT-base architecture                                                                                            |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``roberta-large``                                          | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
 |                   |                                                            | | RoBERTa using the BERT-large architecture                                                                                           |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``roberta-large-mnli``                                     | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
 |                   |                                                            | | ``roberta-large`` fine-tuned on `MNLI <http://www.nyu.edu/projects/bowman/multinli/>`__.                                            |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``distilroberta-base``                                     | | 6-layer, 768-hidden, 12-heads, 82M parameters                                                                                       |
 |                   |                                                            | | The DistilRoBERTa model distilled from the RoBERTa model `roberta-base` checkpoint.                                                 |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``roberta-base-openai-detector``                           | | 12-layer, 768-hidden, 12-heads, 125M parameters                                                                                     |
 |                   |                                                            | | ``roberta-base`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model.                                             |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__)                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``roberta-large-openai-detector``                          | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
 |                   |                                                            | | ``roberta-large`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model.                                            |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__)                                               |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | DistilBERT        | ``distilbert-base-uncased``                                | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
 |                   |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint                                                   |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``distilbert-base-uncased-distilled-squad``                | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
 |                   |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint, with an additional linear layer.                 |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``distilbert-base-cased``                                  | | 6-layer, 768-hidden, 12-heads, 65M parameters                                                                                       |
 |                   |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint                                                     |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``distilbert-base-cased-distilled-squad``                  | | 6-layer, 768-hidden, 12-heads, 65M parameters                                                                                       |
 |                   |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint, with an additional question answering layer.       |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``distilgpt2``                                             | | 6-layer, 768-hidden, 12-heads, 82M parameters                                                                                       |
 |                   |                                                            | | The DistilGPT2 model distilled from the GPT2 model `gpt2` checkpoint.                                                               |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``distilbert-base-german-cased``                           | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
 |                   |                                                            | | The German DistilBERT model distilled from the German DBMDZ BERT model `bert-base-german-dbmdz-cased` checkpoint.                   |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``distilbert-base-multilingual-cased``                     | | 6-layer, 768-hidden, 12-heads, 134M parameters                                                                                      |
 |                   |                                                            | | The multilingual DistilBERT model distilled from the Multilingual BERT model `bert-base-multilingual-cased` checkpoint.             |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | CTRL              | ``ctrl``                                                   | | 48-layer, 1280-hidden, 16-heads, 1.6B parameters                                                                                    |
@@ -234,47 +204,38 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | CamemBERT         | ``camembert-base``                                         | | 12-layer, 768-hidden, 12-heads, 110M parameters                                                                                     |
 |                   |                                                            | | CamemBERT using the BERT-base architecture                                                                                          |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/camembert>`__)                                                 |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | ALBERT            | ``albert-base-v1``                                         | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters                                                            |
 |                   |                                                            | | ALBERT base model                                                                                                                   |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``albert-large-v1``                                        | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters                                                           |
 |                   |                                                            | | ALBERT large model                                                                                                                  |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``albert-xlarge-v1``                                       | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters                                                           |
 |                   |                                                            | | ALBERT xlarge model                                                                                                                 |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``albert-xxlarge-v1``                                      | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters                                                           |
 |                   |                                                            | | ALBERT xxlarge model                                                                                                                |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``albert-base-v2``                                         | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters                                                            |
 |                   |                                                            | | ALBERT base model with no dropout, additional training data and longer training                                                     |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``albert-large-v2``                                        | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters                                                           |
 |                   |                                                            | | ALBERT large model with no dropout, additional training data and longer training                                                    |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``albert-xlarge-v2``                                       | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters                                                           |
 |                   |                                                            | | ALBERT xlarge model with no dropout, additional training data and longer training                                                   |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``albert-xxlarge-v2``                                      | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters                                                           |
 |                   |                                                            | | ALBERT xxlarge model with no dropout, additional training data and longer training                                                  |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | T5                | ``t5-small``                                               | | ~60M parameters with 6-layers, 512-hidden-state, 2048 feed-forward hidden-state, 8-heads,                                           |
@@ -300,26 +261,21 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | FlauBERT          | ``flaubert/flaubert_small_cased``                          | | 6-layer, 512-hidden, 8-heads, 54M parameters                                                                                        |
 |                   |                                                            | | FlauBERT small architecture                                                                                                         |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``flaubert/flaubert_base_uncased``                         | | 12-layer, 768-hidden, 12-heads, 137M parameters                                                                                     |
 |                   |                                                            | | FlauBERT base architecture with uncased vocabulary                                                                                  |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``flaubert/flaubert_base_cased``                           | | 12-layer, 768-hidden, 12-heads, 138M parameters                                                                                     |
 |                   |                                                            | | FlauBERT base architecture with cased vocabulary                                                                                    |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``flaubert/flaubert_large_cased``                          | | 24-layer, 1024-hidden, 16-heads, 373M parameters                                                                                    |
 |                   |                                                            | | FlauBERT large architecture                                                                                                         |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | Bart              | ``facebook/bart-large``                                    | | 24-layer, 1024-hidden, 16-heads, 406M parameters                                                                                    |
-|                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_)                                                       |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``facebook/bart-base``                                     | | 12-layer, 768-hidden, 16-heads, 139M parameters                                                                                     |

docs/source/quickstart.md

@@ -0,0 +1,222 @@
+# Quickstart
+
+## Philosophy
+
+Transformers is an opinionated library built for NLP researchers seeking to use/study/extend large-scale transformers models.
+
+The library was designed with two strong goals in mind:
+
+- be as easy and fast to use as possible:
+
+  - we strongly limited the number of user-facing abstractions to learn, in fact, there are almost no abstractions, just three standard classes required to use each model: configuration, models and tokenizer,
+  - all of these classes can be initialized in a simple and unified way from pretrained instances by using a common `from_pretrained()` instantiation method which will take care of downloading (if needed), caching and loading the related class from a pretrained instance supplied in the library or your own saved instance.
+  - as a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to extend/build-upon the library, just use regular Python/PyTorch modules and inherit from the base classes of the library to reuse functionalities like model loading/saving.
+
+- provide state-of-the-art models with performances as close as possible to the original models:
+
+  - we provide at least one example for each architecture which reproduces a result provided by the official authors of said architecture,
+  - the code is usually as close to the original code base as possible which means some PyTorch code may be not as *pytorchic* as it could be as a result of being converted TensorFlow code.
+
+A few other goals:
+
+- expose the models' internals as consistently as possible:
+
+  - we give access, using a single API to the full hidden-states and attention weights,
+  - tokenizer and base model's API are standardized to easily switch between models.
+
+- incorporate a subjective selection of promising tools for fine-tuning/investigating these models:
+
+  - a simple/consistent way to add new tokens to the vocabulary and embeddings for fine-tuning,
+  - simple ways to mask and prune transformer heads.
+
+## Main concepts
+
+The library is build around three types of classes for each model:
+
+- **model classes**  e.g., `BertModel` which are 20+ PyTorch models (`torch.nn.Modules`) that work with the pretrained weights provided in the library. In TF2, these are `tf.keras.Model`.
+- **configuration classes** which store all the parameters required to build a model, e.g., `BertConfig`. You don't always need to instantiate these your-self. In particular, if you are using a pretrained model without any modification, creating the model will automatically take care of instantiating the configuration (which is part of the model)
+- **tokenizer classes** which store the vocabulary for each model and provide methods for encoding/decoding strings in a list of token embeddings indices to be fed to a model, e.g., `BertTokenizer`
+
+All these classes can be instantiated from pretrained instances and saved locally using two methods:
+
+- `from_pretrained()` let you instantiate a model/configuration/tokenizer from a pretrained version either provided by the library itself (currently 27 models are provided as listed [here](https://huggingface.co/transformers/pretrained_models.html)) or stored locally (or on a server) by the user,
+- `save_pretrained()` let you save a model/configuration/tokenizer locally so that it can be reloaded using `from_pretrained()`.
+
+We'll finish this quickstart tour by going through a few simple quick-start examples to see how we can instantiate and use these classes. The rest of the documentation is organized into two parts:
+
+- the **MAIN CLASSES** section details the common functionalities/method/attributes of the three main type of classes (configuration, model, tokenizer) plus some optimization related classes provided as utilities for training,
+- the **PACKAGE REFERENCE** section details all the variants of each class for each model architectures and, in particular, the input/output that you should expect when calling each of them.
+
+## Quick tour: Usage
+
+Here are two examples showcasing a few `Bert` and `GPT2` classes and pre-trained models.
+
+See the full API reference for examples of each model class.
+
+### BERT example
+
+Let's start by preparing a tokenized input (a list of token embeddings indices to be fed to Bert) from a text string using `BertTokenizer`
+
+```python
+import torch
+from transformers import BertTokenizer, BertModel, BertForMaskedLM
+
+# OPTIONAL: if you want to have more information on what's happening under the hood, activate the logger as follows
+import logging
+logging.basicConfig(level=logging.INFO)
+
+# Load pre-trained model tokenizer (vocabulary)
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+
+# Tokenize input
+text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
+tokenized_text = tokenizer.tokenize(text)
+
+# Mask a token that we will try to predict back with `BertForMaskedLM`
+masked_index = 8
+tokenized_text[masked_index] = '[MASK]'
+assert tokenized_text == ['[CLS]', 'who', 'was', 'jim', 'henson', '?', '[SEP]', 'jim', '[MASK]', 'was', 'a', 'puppet', '##eer', '[SEP]']
+
+# Convert token to vocabulary indices
+indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
+# Define sentence A and B indices associated to 1st and 2nd sentences (see paper)
+segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]
+
+# Convert inputs to PyTorch tensors
+tokens_tensor = torch.tensor([indexed_tokens])
+segments_tensors = torch.tensor([segments_ids])
+```
+
+Let's see how we can use `BertModel` to encode our inputs in hidden-states:
+
+```python
+# Load pre-trained model (weights)
+model = BertModel.from_pretrained('bert-base-uncased')
+
+# Set the model in evaluation mode to deactivate the DropOut modules
+# This is IMPORTANT to have reproducible results during evaluation!
+model.eval()
+
+# If you have a GPU, put everything on cuda
+tokens_tensor = tokens_tensor.to('cuda')
+segments_tensors = segments_tensors.to('cuda')
+model.to('cuda')
+
+# Predict hidden states features for each layer
+with torch.no_grad():
+    # See the models docstrings for the detail of the inputs
+    outputs = model(tokens_tensor, token_type_ids=segments_tensors)
+    # Transformers models always output tuples.
+    # See the models docstrings for the detail of all the outputs
+    # In our case, the first element is the hidden state of the last layer of the Bert model
+    encoded_layers = outputs[0]
+# We have encoded our input sequence in a FloatTensor of shape (batch size, sequence length, model hidden dimension)
+assert tuple(encoded_layers.shape) == (1, len(indexed_tokens), model.config.hidden_size)
+```
+
+And how to use `BertForMaskedLM` to predict a masked token:
+
+```python
+# Load pre-trained model (weights)
+model = BertForMaskedLM.from_pretrained('bert-base-uncased')
+model.eval()
+
+# If you have a GPU, put everything on cuda
+tokens_tensor = tokens_tensor.to('cuda')
+segments_tensors = segments_tensors.to('cuda')
+model.to('cuda')
+
+# Predict all tokens
+with torch.no_grad():
+    outputs = model(tokens_tensor, token_type_ids=segments_tensors)
+    predictions = outputs[0]
+
+# confirm we were able to predict 'henson'
+predicted_index = torch.argmax(predictions[0, masked_index]).item()
+predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
+assert predicted_token == 'henson'
+```
+
+### OpenAI GPT-2
+
+Here is a quick-start example using `GPT2Tokenizer` and `GPT2LMHeadModel` class with OpenAI's pre-trained model to predict the next token from a text prompt.
+
+First let's prepare a tokenized input from our text string using `GPT2Tokenizer`
+
+```python
+import torch
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+# OPTIONAL: if you want to have more information on what's happening, activate the logger as follows
+import logging
+logging.basicConfig(level=logging.INFO)
+
+# Load pre-trained model tokenizer (vocabulary)
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+
+# Encode a text inputs
+text = "Who was Jim Henson ? Jim Henson was a"
+indexed_tokens = tokenizer.encode(text)
+
+# Convert indexed tokens in a PyTorch tensor
+tokens_tensor = torch.tensor([indexed_tokens])
+```
+
+Let's see how to use `GPT2LMHeadModel` to generate the next token following our text:
+
+```python
+# Load pre-trained model (weights)
+model = GPT2LMHeadModel.from_pretrained('gpt2')
+
+# Set the model in evaluation mode to deactivate the DropOut modules
+# This is IMPORTANT to have reproducible results during evaluation!
+model.eval()
+
+# If you have a GPU, put everything on cuda
+tokens_tensor = tokens_tensor.to('cuda')
+model.to('cuda')
+
+# Predict all tokens
+with torch.no_grad():
+    outputs = model(tokens_tensor)
+    predictions = outputs[0]
+
+# get the predicted next sub-word (in our case, the word 'man')
+predicted_index = torch.argmax(predictions[0, -1, :]).item()
+predicted_text = tokenizer.decode(indexed_tokens + [predicted_index])
+assert predicted_text == 'Who was Jim Henson? Jim Henson was a man'
+```
+
+Examples for each model class of each model architecture (Bert, GPT, GPT-2, Transformer-XL, XLNet and XLM) can be found in the [documentation](#documentation).
+
+#### Using the past
+
+GPT-2, as well as some other models (GPT, XLNet, Transfo-XL, CTRL), make use of a `past` or `mems` attribute which can be used to prevent re-computing the key/value pairs when using sequential decoding. It is useful when generating sequences as a big part of the attention mechanism benefits from previous computations.
+
+Here is a fully-working example using the `past` with `GPT2LMHeadModel` and argmax decoding (which should only be used as an example, as argmax decoding introduces a lot of repetition):
+
+```python
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
+
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+model = GPT2LMHeadModel.from_pretrained('gpt2')
+
+generated = tokenizer.encode("The Manhattan bridge")
+context = torch.tensor([generated])
+past = None
+
+for i in range(100):
+    print(i)
+    output, past = model(context, past=past)
+    token = torch.argmax(output[..., -1, :])
+
+    generated += [token.tolist()]
+    context = token.unsqueeze(0)
+
+sequence = tokenizer.decode(generated)
+
+print(sequence)
+```
+
+The model only requires a single token as input as all the previous tokens' key/value pairs are contained in the `past`.

docs/source/quicktour.rst

@@ -1,378 +0,0 @@
-Quick tour
-==========
-
-Let's have a quick look at the 🤗 Transformers library features. The library downloads pretrained models for
-Natural Language Understanding (NLU) tasks, such as analyzing the sentiment of a text, and Natural Language Generation (NLG),
-such as completing a prompt with new text or translating in another language.
-
-First we will see how to easily leverage the pipeline API to quickly use those pretrained models at inference. Then, we
-will dig a little bit more and see how the library gives you access to those models and helps you preprocess your data.
-
-.. note::
-
-    All code examples presented in the documentation have a switch on the top left for Pytorch versus TensorFlow. If
-    not, the code is expected to work for both backends without any change needed.
-
-Getting started on a task with a pipeline
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The easiest way to use a pretrained model on a given task is to use :func:`~transformers.pipeline`. 🤗 Transformers
-provides the following tasks out of the box:
-
-- Sentiment analysis: is a text positive or negative?
-- Text generation (in English): provide a prompt and the model will generate what follows.
-- Name entity recognition (NER): in an input sentence, label each word with the entity it represents (person, place,
-  etc.)
-- Question answering: provide the model with some context and a question, extract the answer from the context.
-- Filling masked text: given a text with masked words (e.g., replaced by ``[MASK]``), fill the blanks.
-- Summarization: generate a summary of a long text.
-- Translation: translate a text in another language.
-- Feature extraction: return a tensor representation of the text.
-
-Let's see how this work for sentiment analysis (the other tasks are all covered in the
-:doc:`task summary </task_summary>`):
-
-::
-
-    from transformers import pipeline
-    classifier = pipeline('sentiment-analysis')
-
-When typing this command for the first time, a pretrained model and its tokenizer are downloaded and cached. We will
-look at both later on, but as an introduction the tokenizer's job is to preprocess the text for the model, which is
-then responsible for making predictions. The pipeline groups all of that together, and post-process the predictions to
-make them readable. For instance
-
-::
-
-    classifier('We are very happy to show you the 🤗 Transformers library.')
-
-will return something like this:
-
-::
-
-    [{'label': 'POSITIVE', 'score': 0.9997795224189758}]
-
-That's encouraging! You can use it on a list of sentences, which will be preprocessed then fed to the model as a
-`batch`:
-
-::
-
-    classifier(["We are very happy to show you the 🤗 Transformers library.",
-                "We hope you don't hate it."])
-
-returning a list of dictionaries like this one:
-
-::
-
-    [{'label': 'POSITIVE', 'score': 0.9997795224189758},
-     {'label': 'NEGATIVE', 'score': 0.5308589935302734}]
-
-You can see the second sentence has been classified as negative (it needs to be positive or negative) but its score is
-fairly neutral.
-
-By default, the model downloaded for this pipeline is called "distilbert-base-uncased-finetuned-sst-2-english". We can
-look at its `model page <https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english>`__ to get more
-information about it. It uses the :doc:`DistilBERT architecture </model_doc/distilbert>` and has been fine-tuned on a
-dataset called SST-2 for the sentiment analysis task.
-
-Let's say we want to use another model; for instance, one that has been trained on French data. We can search through
-the `model hub <https://huggingface.co/models>`__ that gathers models pretrained on a lot of data by research labs, but
-also community models (usually fine-tuned versions of those big models on a specific dataset). Applying the tags
-"French" and "text-classification" gives back a suggestion "nlptown/bert-base-multilingual-uncased-sentiment". Let's
-see how we can use it. 
-
-You can directly pass the name of the model to use to :func:`~transformers.pipeline`:
-
-::
-
-    classifier = pipeline('sentiment-analysis', model="nlptown/bert-base-multilingual-uncased-sentiment")
-
-This classifier can now deal with texts in English, French, but also Dutch, German, Italian and Spanish! You can also
-replace that name by a local folder where you have saved a pretrained model (see below). You can also pass a model
-object and its associated tokenizer.
-
-We will need two classes for this. The first is :class:`~transformers.AutoTokenizer`, which we will use to download the
-tokenizer associated to the model we picked and instantiate it. The second is
-:class:`~transformers.AutoModelForSequenceClassification` (or
-:class:`~transformers.TFAutoModelForSequenceClassification` if you are using TensorFlow), which we will use to download
-the model itself. Note that if we were using the library on an other task, the class of the model would change. The
-:doc:`task summary </task_summary>` tutorial summarizes which class is used for which task.
-
-::
-
-    ## PYTORCH CODE
-    from transformers import AutoTokenizer, AutoModelForSequenceClassification
-    ## TENSORFLOW CODE
-    from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
-
-Now, to download the models and tokenizer we found previously, we just have to use the 
-:func:`~transformers.AutoModelForSequenceClassification.from_pretrained` method (feel free to replace ``model_name`` by
-any other model from the model hub):
-
-::
-
-    ## PYTORCH CODE
-    model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    pipe = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
-    ## TENSORFLOW CODE
-    model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
-    model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
-
-If you don't find a model that has been pretrained on some data similar to yours, you will need to fine-tune a
-pretrained model on your data. We provide :doc:`example scripts </examples>` to do so. Once you're done, don't forget
-to share your fine-tuned model on the hub with the community, using :doc:`this tutorial </model_sharing>`.
-
-.. _pretrained-model:
-
-Under the hood: pretrained models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Let's now see what happens beneath the hood when using those pipelines. As we saw, the model and tokenizer are created
-using the :obj:`from_pretrained` method:
-
-::
-
-    ## PYTORCH CODE
-    from transformers import AutoTokenizer, AutoModelForSequenceClassification
-    model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    ## TENSORFLOW CODE
-    from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
-    model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-    model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-Using the tokenizer
-^^^^^^^^^^^^^^^^^^^
-
-We mentioned the tokenizer is responsible for the preprocessing of your texts. First, it will split a given text in
-words (or part of words, punctuation symbols, etc.) usually called `tokens`. There are multiple rules that can govern
-that process, which is why we need to instantiate the tokenizer using the name of the model, to make sure we use the
-same rules as when the model was pretrained.
-
-The second step is to convert those `tokens` into numbers, to be able to build a tensor out of them and feed them to
-the model. To do this, the tokenizer has a `vocab`, which is the part we download when we instantiate it with the
-:obj:`from_pretrained` method, since we need to use the same `vocab` as when the model was pretrained.
-
-To apply these steps on a given text, we can just feed it to our tokenizer:
-
-::
-
-    input = tokenizer("We are very happy to show you the 🤗 Transformers library.")
-    print(input)
-
-This returns a dictionary string to list of ints. It contains the `ids of the tokens <glossary.html#input-ids>`__,
-as mentioned before, but also additional arguments that will be useful to the model. Here for instance, we also have an
-`attention mask <glossary.html#attention-mask>`__ that the model will use to have a better understanding of the sequence:
-
-
-::
-    {'input_ids': [101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 100, 19081, 3075, 1012, 102],
-     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
-
-You can pass a list of sentences directly to your tokenizer. If your goal is to send them through your model as a
-batch, you probably want to pad them all to the same length, truncate them to the maximum length the model can accept
-and get tensors back. You can specify all of that to the tokenizer:
-
-::
-
-    ## PYTORCH CODE
-    batch = tokenizer(
-        ["We are very happy to show you the 🤗 Transformers library.",
-         "We hope you don't hate it."],
-        padding=True, truncation=True, return_tensors="pt")
-    print(batch)
-    ## TENSORFLOW CODE
-    batch = tokenizer(
-        ["We are very happy to show you the 🤗 Transformers library.",
-         "We hope you don't hate it."],
-        padding=True, truncation=True, return_tensors="tf")
-    print(batch)
-
-The padding is automatically applied on the side the model expect it (in this case, on the right), with the
-padding token the model was pretrained with. The attention mask is also adapted to take the padding into account:
-
-::
-
-    {'input_ids': tensor([[  101,  2057,  2024,  2200,  3407,  2000,  2265,  2017,  1996,   100, 19081,  3075,  1012,   102],
-                          [  101,  2057,  3246,  2017,  2123,  1005,  1056,  5223,  2009,  1012,   102,     0,     0,     0]]), 
-     'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
-                               [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]])}
-
-You can learn more about tokenizers on their :doc:`doc page <main_classes/tokenizer>` (tutorial coming soon).
-
-Using the model
-^^^^^^^^^^^^^^^
-
-Once your input has been preprocessed by the tokenizer, you can directly send it to the model. As we mentioned, it will
-contain all the relevant information the model needs. If you're using a TensorFlow model, you can directly pass the
-dictionary keys to tensor, for a PyTorch model, you need to unpack the dictionary by adding :obj:`**`.
-
-::
-
-    ## PYTORCH CODE
-    outputs = model(**batch)
-    ## TENSORFLOW CODE
-    outputs = model(batch)
-
-In 🤗 Transformers, all outputs are tuples (with only one element potentially). Here, we get a tuple with just the
-final activations of the model.
-
-::
-
-    (tensor([[-4.1329,  4.3811],
-             [ 0.0818, -0.0418]]),)
-
-.. note::
-
-    All 🤗 Transformers models (PyTorch or TensorFlow) return the activations of the model *before* the final
-    activation function (like SoftMax) since this final activation function is often fused with the loss.
-
-Let's apply the SoftMax activation to get predictions.
-
-::
-
-    ## PYTORCH CODE
-    import torch.nn.functional as F
-    predictions = F.softmax(outputs[0], dim=-1)
-    print(predictions)
-    ## TENSORFLOW CODE
-    predictions = tf.nn.softmax(outputs[0], axis=-1)
-    print(predictions)
-
-We can see we get the numbers from before:
-
-::
-
-    tensor([[2.0060e-04, 9.9980e-01],
-            [5.3086e-01, 4.6914e-01]])
-
-If you have labels, you can provide them to the model, it will return a tuple with the loss and the final activations.
-
-::
-
-    ## PYTORCH CODE
-    import torch
-    outputs = model(**batch, labels = torch.tensor([1, 0])
-    ## TENSORFLOW CODE
-    import tensorflow as tf
-    outputs = model(batch, labels = tf.constant([1, 0])
-
-Models are standard `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__ or
-`tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ so you can use them in your usual
-training loop. 🤗 Transformers also provides a :class:`~transformers.Trainer` (or :class:`~transformers.TFTrainer` if
-you are using TensorFlow) class to help with your training (taking care of things such as distributed training, mixed
-precision, etc.). See the training tutorial (coming soon) for more details.
-
-Once your model is fine-tuned, you can save it with its tokenizer the following way:
-
-::
-
-    tokenizer.save_pretrained(save_directory)
-    model.save_pretrained(save_directory)
-
-You can then load this model back using the :func:`~transformers.AutoModel.from_pretrained` method by passing the
-directory name instead of the model name. One cool feature of 🤗 Transformers is that you can easily switch between
-PyTorch and TensorFlow: any model saved as before can be loaded back either in PyTorch or TensorFlow. If you are
-loading a saved PyTorch model in a TensorFlow model, use :func:`~transformers.TFAutoModel.from_pretrained` like this:
-
-::
-
-    tokenizer = AutoTokenizer.from_pretrained(save_directory)
-    model = TFAutoModel.from_pretrained(save_directory, from_pt=True)
-
-and if you are loading a saved TensorFlow model in a PyTorch model, you should use the following code:
-
-::
-
-    tokenizer = AutoTokenizer.from_pretrained(save_directory)
-    model = AutoModel.from_pretrained(save_directory, from_tf=True)
-
-Lastly, you can also ask the model to return all hidden states and all attention weights if you need them:
-
-
-::
-
-    ## PYTORCH CODE
-    outputs = model(**batch, output_hidden_states=True, output_attentions=True)
-    all_hidden_states, all_attentions = outputs[-2:]
-    ## TENSORFLOW CODE
-    outputs = model(batch, output_hidden_states=True, output_attentions=True)
-    all_hidden_states, all_attentions = outputs[-2:]
-
-Accessing the code
-^^^^^^^^^^^^^^^^^^
-
-The :obj:`AutoModel` and :obj:`AutoTokenizer` classes are just shortcuts that will automatically work with any
-pretrained model. Behind the scenes, the library has one model class per combination of architecture plus class, so the
-code is easy to access and tweak if you need to.
-
-In our previous example, the model was called "distilbert-base-uncased-finetuned-sst-2-english", which means it's
-using the :doc:`DistilBERT </model_doc/distilbert>` architecture. The model automatically created is then a
-:class:`~transformers.DistilBertForSequenceClassification`. You can look at its documentation for all details relevant
-to that specific model, or browse the source code. This is how you would directly instantiate model and tokenizer
-without the auto magic:
-
-::
-
-    ## PYTORCH CODE
-    from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
-    model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-    model = DistilBertForSequenceClassification.from_pretrained(model_name)
-    tokenizer = DistilBertTokenizer.from_pretrained(model_name)
-    ## TENSORFLOW CODE
-    from transformers import DistilBertTokenizer, TFDistilBertForSequenceClassification
-    model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-    model = TFDistilBertForSequenceClassification.from_pretrained(model_name)
-    tokenizer = DistilBertTokenizer.from_pretrained(model_name)
-
-Customizing the model
-^^^^^^^^^^^^^^^^^^^^^
-
-If you want to change how the model itself is built, you can define your custom configuration class. Each architecture
-comes with its own relevant configuration (in the case of DistilBERT, :class:`~transformers.DistilBertConfig`) which
-allows you to specify any of the hidden dimension, dropout rate etc. If you do core modifications, like changing the
-hidden size, you won't be able to use a pretrained model anymore and will need to train from scratch. You would then
-instantiate the model directly from this configuration.
-
-Here we use the predefined vocabulary of DistilBERT (hence load the tokenizer with the
-:func:`~transformers.DistilBertTokenizer.from_pretrained` method) and initialize the model from scratch (hence
-instantiate the model from the configuration instead of using the
-:func:`~transformers.DistilBertForSequenceClassification.from_pretrained` method).
-
-::
-
-    ## PYTORCH CODE
-    from transformers import DistilBertConfig, DistilBertTokenizer, DistilBertForSequenceClassification
-    config = DistilBertConfig(n_heads=8, dim=512, hidden_dim=4*512)
-    tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
-    model = DistilBertForSequenceClassification(config)
-    ## TENSORFLOW CODE
-    from transformers import DistilBertConfig, DistilBertTokenizer, TFDistilBertForSequenceClassification
-    config = DistilBertConfig(n_heads=8, dim=512, hidden_dim=4*512)
-    tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
-    model = TFDistilBertForSequenceClassification(config)
-
-For something that only changes the head of the model (for instance, the number of labels), you can still use a
-pretrained model for the body. For instance, let's define a classifier for 10 different labels using a pretrained body.
-We could create a configuration with all the default values and just change the number of labels, but more easily, you
-can directly pass any argument a configuration would take to the :func:`from_pretrained` method and it will update the
-default configuration with it:
-
-::
-
-    ## PYTORCH CODE
-    from transformers import DistilBertConfig, DistilBertTokenizer, DistilBertForSequenceClassification
-    model_name = "distilbert-base-uncased"
-    model = DistilBertForSequenceClassification.from_pretrained(model_name, num_labels=10)
-    tokenizer = DistilBertTokenizer.from_pretrained(model_name)
-    ## TENSORFLOW CODE
-    from transformers import DistilBertConfig, DistilBertTokenizer, TFDistilBertForSequenceClassification
-    model_name = "distilbert-base-uncased"
-    model = TFDistilBertForSequenceClassification.from_pretrained(model_name, num_labels=10)
-    tokenizer = DistilBertTokenizer.from_pretrained(model_name)

docs/source/serialization.rst

@@ -1,3 +1,104 @@
+Loading Google AI or OpenAI pre-trained weights or PyTorch dump
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+``from_pretrained()`` method
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+To load one of Google AI's, OpenAI's pre-trained models or a PyTorch saved model (an instance of ``BertForPreTraining`` saved with ``torch.save()``\ ), the PyTorch model classes and the tokenizer can be instantiated using the ``from_pretrained()`` method:
+
+.. code-block:: python
+
+   model = BERT_CLASS.from_pretrained(PRE_TRAINED_MODEL_NAME_OR_PATH, cache_dir=None, from_tf=False, state_dict=None, *input, **kwargs)
+
+where
+
+
+* ``BERT_CLASS`` is either a tokenizer to load the vocabulary (\ ``BertTokenizer`` or ``OpenAIGPTTokenizer`` classes) or one of the eight BERT or three OpenAI GPT PyTorch model classes (to load the pre-trained weights): ``BertModel``\ , ``BertForMaskedLM``\ , ``BertForNextSentencePrediction``\ , ``BertForPreTraining``\ , ``BertForSequenceClassification``\ , ``BertForTokenClassification``\ , ``BertForMultipleChoice``\ , ``BertForQuestionAnswering``\ , ``OpenAIGPTModel``\ , ``OpenAIGPTLMHeadModel`` or ``OpenAIGPTDoubleHeadsModel``\ , and
+*
+  ``PRE_TRAINED_MODEL_NAME_OR_PATH`` is either:
+
+
+  *
+    the shortcut name of a Google AI's or OpenAI's pre-trained model selected in the list:
+
+
+    * ``bert-base-uncased``: 12-layer, 768-hidden, 12-heads, 110M parameters
+    * ``bert-large-uncased``: 24-layer, 1024-hidden, 16-heads, 340M parameters
+    * ``bert-base-cased``: 12-layer, 768-hidden, 12-heads , 110M parameters
+    * ``bert-large-cased``: 24-layer, 1024-hidden, 16-heads, 340M parameters
+    * ``bert-base-multilingual-uncased``: (Orig, not recommended) 102 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
+    * ``bert-base-multilingual-cased``: **(New, recommended)** 104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
+    * ``bert-base-chinese``: Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M parameters
+    * ``bert-base-german-cased``: Trained on German data only, 12-layer, 768-hidden, 12-heads, 110M parameters `Performance Evaluation <https://deepset.ai/german-bert>`__
+    * ``bert-large-uncased-whole-word-masking``: 24-layer, 1024-hidden, 16-heads, 340M parameters - Trained with Whole Word Masking (mask all of the the tokens corresponding to a word at once)
+    * ``bert-large-cased-whole-word-masking``: 24-layer, 1024-hidden, 16-heads, 340M parameters - Trained with Whole Word Masking (mask all of the the tokens corresponding to a word at once)
+    * ``bert-large-uncased-whole-word-masking-finetuned-squad``: The ``bert-large-uncased-whole-word-masking`` model finetuned on SQuAD (using the ``run_bert_squad.py`` examples). Results: *exact_match: 86.91579943235573, f1: 93.1532499015869*
+    * ``bert-base-german-dbmdz-cased``: Trained on German data only, 12-layer, 768-hidden, 12-heads, 110M parameters `Performance Evaluation <https://github.com/dbmdz/german-bert>`__
+    * ``bert-base-german-dbmdz-uncased``: Trained on (uncased) German data only, 12-layer, 768-hidden, 12-heads, 110M parameters `Performance Evaluation <https://github.com/dbmdz/german-bert>`__
+    * ``openai-gpt``: OpenAI GPT English model, 12-layer, 768-hidden, 12-heads, 110M parameters
+    * ``gpt2``: OpenAI GPT-2 English model, 12-layer, 768-hidden, 12-heads, 117M parameters
+    * ``gpt2-medium``: OpenAI GPT-2 English model, 24-layer, 1024-hidden, 16-heads, 345M parameters
+    * ``transfo-xl-wt103``: Transformer-XL English model trained on wikitext-103, 18-layer, 1024-hidden, 16-heads, 257M parameters
+
+  *
+    a path or url to a pretrained model archive containing:
+
+
+    * ``bert_config.json`` or ``openai_gpt_config.json`` a configuration file for the model, and
+    * ``pytorch_model.bin`` a PyTorch dump of a pre-trained instance of ``BertForPreTraining``\ , ``OpenAIGPTModel``\ , ``TransfoXLModel``\ , ``GPT2LMHeadModel`` (saved with the usual ``torch.save()``\ )
+
+  If ``PRE_TRAINED_MODEL_NAME_OR_PATH`` is a shortcut name, the pre-trained weights will be downloaded from AWS S3 (see the links `here <https://github.com/huggingface/transformers/blob/master/transformers/modeling_bert.py>`__\ ) and stored in a cache folder to avoid future download (the cache folder can be found at ``~/.pytorch_pretrained_bert/``\ ).
+
+*
+  ``cache_dir`` can be an optional path to a specific directory to download and cache the pre-trained model weights. This option is useful in particular when you are using distributed training: to avoid concurrent access to the same weights you can set for example ``cache_dir='./pretrained_model_{}'.format(args.local_rank)`` (see the section on distributed training for more information).
+
+* ``from_tf``\ : should we load the weights from a locally saved TensorFlow checkpoint
+* ``state_dict``\ : an optional state dictionary (collections.OrderedDict object) to use instead of Google pre-trained models
+* ``*inputs``\ , `**kwargs`: additional input for the specific Bert class (ex: num_labels for BertForSequenceClassification)
+
+``Uncased`` means that the text has been lowercased before WordPiece tokenization, e.g., ``John Smith`` becomes ``john smith``. The Uncased model also strips out any accent markers. ``Cased`` means that the true case and accent markers are preserved. Typically, the Uncased model is better unless you know that case information is important for your task (e.g., Named Entity Recognition or Part-of-Speech tagging). For information about the Multilingual and Chinese model, see the `Multilingual README <https://github.com/google-research/bert/blob/master/multilingual.md>`__ or the original TensorFlow repository.
+
+When using an ``uncased model``\ , make sure your tokenizer has ``do_lower_case=True`` (either in its configuration, or passed as an additional parameter).
+
+Examples:
+
+.. code-block:: python
+
+   # BERT
+   tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_basic_tokenize=True)
+   model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
+
+   # OpenAI GPT
+   tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
+   model = OpenAIGPTModel.from_pretrained('openai-gpt')
+
+   # Transformer-XL
+   tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')
+   model = TransfoXLModel.from_pretrained('transfo-xl-wt103')
+
+   # OpenAI GPT-2
+   tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+   model = GPT2Model.from_pretrained('gpt2')
+
+Cache directory
+~~~~~~~~~~~~~~~
+
+``pytorch_pretrained_bert`` save the pretrained weights in a cache directory which is located at (in this order of priority):
+
+
+* ``cache_dir`` optional arguments to the ``from_pretrained()`` method (see above),
+* shell environment variable ``PYTORCH_PRETRAINED_BERT_CACHE``\ ,
+* PyTorch cache home + ``/pytorch_pretrained_bert/``
+  where PyTorch cache home is defined by (in this order):
+
+  * shell environment variable ``ENV_TORCH_HOME``
+  * shell environment variable ``ENV_XDG_CACHE_HOME`` + ``/torch/``\ )
+  * default: ``~/.cache/torch/``
+
+Usually, if you don't set any specific environment variable, ``pytorch_pretrained_bert`` cache will be at ``~/.cache/torch/pytorch_pretrained_bert/``.
+
+You can alsways safely delete ``pytorch_pretrained_bert`` cache but the pretrained model weights and vocabulary files wil have to be re-downloaded from our S3.
+
 Serialization best-practices
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

docs/source/summary.rst

@@ -1,7 +1,7 @@
 Summary of the models
 ================================================
 
-This is a summary of the models available in 🤗 Transformers. It assumes you’re familiar with the original 
+This is a summary of the models available in the transformers library. It assumes you’re familiar with the original 
 `transformer model <https://arxiv.org/abs/1706.03762>`_. For a gentle introduction check the `annotated transformer 
 <http://nlp.seas.harvard.edu/2018/04/03/attention.html>`_. Here we focus on the high-level differences between the
 models. You can check them more in detail in their respective documentation. Also checkout the 

docs/source/torchscript.rst

@@ -12,7 +12,7 @@ According to Pytorch's documentation: "TorchScript is a way to create serializab
 Pytorch's two modules `JIT and TRACE <https://pytorch.org/docs/stable/jit.html>`_ allow the developer to export
 their model to be re-used in other programs, such as efficiency-oriented C++ programs.
 
-We have provided an interface that allows the export of 🤗 Transformers models to TorchScript so that they can
+We have provided an interface that allows the export of `transformers` models to TorchScript so that they can
 be reused in a different environment than a Pytorch-based python program. Here we explain how to use our models so that
 they can be exported, and what to be mindful of when using these models with TorchScript.
 

docs/source/usage.rst

@@ -1,4 +1,4 @@
-Summary of the tasks
+Usage
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 This page shows the most frequent use-cases when using the library. The models available allow for many different
@@ -217,9 +217,9 @@ Here is an example of question answering using a model and a tokenizer. The proc
     """
 
     questions = [
-        "How many pretrained models are available in 🤗 Transformers?",
-        "What does 🤗 Transformers provide?",
-        "🤗 Transformers provides interoperability between which frameworks?",
+        "How many pretrained models are available in Transformers?",
+        "What does Transformers provide?",
+        "Transformers provides interoperability between which frameworks?",
     ]
 
     for question in questions:
@@ -253,9 +253,9 @@ Here is an example of question answering using a model and a tokenizer. The proc
     """
 
     questions = [
-        "How many pretrained models are available in 🤗 Transformers?",
-        "What does 🤗 Transformers provide?",
-        "🤗 Transformers provides interoperability between which frameworks?",
+        "How many pretrained models are available in Transformers?",
+        "What does Transformers provide?",
+        "Transformers provides interoperability between which frameworks?",
     ]
 
     for question in questions:
@@ -280,13 +280,13 @@ This outputs the questions followed by the predicted answers:
 
 ::
 
-    Question: How many pretrained models are available in 🤗 Transformers?
+    Question: How many pretrained models are available in Transformers?
     Answer: over 32 +
 
-    Question: What does 🤗 Transformers provide?
+    Question: What does Transformers provide?
     Answer: general - purpose architectures
 
-    Question: 🤗 Transformers provides interoperability between which frameworks?
+    Question: Transformers provides interoperability between which frameworks?
     Answer: tensorflow 2 . 0 and pytorch
 
 
@@ -692,8 +692,7 @@ following array should be the output:
 
 ::
 
-    [('[CLS]', 'O'), ('Hu', 'I-ORG'), ('##gging', 'I-ORG'), ('Face', 'I-ORG'), ('Inc', 'I-ORG'), ('.', 'O'), ('is', 'O'), ('a', 'O'), ('company', 'O'), ('based', 'O'), ('in', 'O'), ('New', 'I-LOC'), ('York', 'I-LOC'), ('City', 'I-LOC'), ('.', 'O'), ('Its', 'O'), ('headquarters', 'O'), ('are', 'O'), ('in', 'O'), ('D', 'I-LOC'), ('##UM', 'I-LOC'), ('##BO', 'I-LOC'), (',', 'O'), ('therefore', 'O'), ('very', 'O'), ('##c', 'O'), ('##lose', 'O'), ('to', 'O'), ('the', 'O'), ('Manhattan', 'I-LOC'), ('Bridge', 'I-LOC'), ('.', 'O'), ('[SEP]', 'O')]
-
+    [('[CLS]', 'O'), ('Hu', 'I-ORG'), ('##gging', 'I-ORG'), ('Face', 'I-ORG'), ('Inc', 'I-ORG'), ('.', 'O'), ('is', 'O'), ('a', 'O'), ('company', 'O'), ('based', 'O'), ('in', 'O'), ('New', 'I-LOC'), ('York', 'I-LOC'), ('City', 'I-LOC'), ('.', 'O'), ('Its', 'O'), ('headquarters', 'O'), ('are', 'O'), ('in', 'O'), ('D', 'I-LOC'), ('##UM', 'I-LOC'), ('##BO', 'I-LOC'), (',', 'O'), ('therefore', 'O'), ('very', 'O'), ('##c', 'O'), ('##lose', 'O'), ('to', 'O'), ('the', 'O'), ('Manhattan', 'I-LOC'), ('Bridge', 'I-LOC'), ('.', 'O'), ('[SEP]', 'O')]   
 Summarization
 ----------------------------------------------------
 
@@ -770,8 +769,7 @@ Here Google`s T5 model is used that was only pre-trained on a multi-task mixed d
     # T5 uses a max_length of 512 so we cut the article to 512 tokens.
     inputs = tokenizer.encode("summarize: " + ARTICLE, return_tensors="tf", max_length=512)
     outputs = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
-    print(outputs)
-
+    print(outputs)  
 Translation
 ----------------------------------------------------
 

examples/README.md

@@ -1,6 +1,6 @@
 ## Examples
 
-Version 2.9 of 🤗 Transformers introduces a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2.
+Version 2.9 of `transformers` introduces a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2.
 Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.0+.
 
 Here is the list of all our examples:
@@ -27,7 +27,7 @@ This is still a work-in-progress – in particular documentation is still sparse
 | [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/summarization)     | CNN/Daily Mail    | -  | -  | -  | -
 | [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/translation)         | WMT               | -  | -  | -  | -
 | [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology)             | -                 | -  | -  | -  | -
-| [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial)         | HANS              | ✅ | -  | -  | -
+| [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial)         | HANS              | -  | -  | -  | -
 
 
 <br>

examples/adversarial/README.md

@@ -11,7 +11,7 @@ export HANS_DIR=path-to-hans
 export MODEL_TYPE=type-of-the-model-e.g.-bert-roberta-xlnet-etc
 export MODEL_PATH=path-to-the-model-directory-that-is-trained-on-NLI-e.g.-by-using-run_glue.py
 
-python run_hans.py \
+python examples/adversarial/test_hans.py \
         --task_name hans \
         --model_type $MODEL_TYPE \
         --do_eval \

examples/adversarial/run_hans.py

@@ -1,232 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Finetuning the library models for sequence classification on HANS."""
-
-import logging
-import os
-from dataclasses import dataclass, field
-from typing import Dict, List, Optional
-
-import numpy as np
-import torch
-
-from transformers import (
-    AutoConfig,
-    AutoModelForSequenceClassification,
-    AutoTokenizer,
-    HfArgumentParser,
-    Trainer,
-    TrainingArguments,
-    default_data_collator,
-    set_seed,
-)
-from utils_hans import HansDataset, InputFeatures, hans_processors
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
-    """
-
-    model_name_or_path: str = field(
-        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
-    )
-    config_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
-    )
-    tokenizer_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
-    )
-    cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
-    )
-
-
-@dataclass
-class DataTrainingArguments:
-    """
-    Arguments pertaining to what data we are going to input our model for training and eval.
-    """
-
-    task_name: str = field(
-        metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys())}
-    )
-    data_dir: str = field(
-        metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}
-    )
-    max_seq_length: int = field(
-        default=128,
-        metadata={
-            "help": "The maximum total input sequence length after tokenization. Sequences longer "
-            "than this will be truncated, sequences shorter will be padded."
-        },
-    )
-    overwrite_cache: bool = field(
-        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
-    )
-
-
-def hans_data_collator(features: List[InputFeatures]) -> Dict[str, torch.Tensor]:
-    """
-    Data collator that removes the "pairID" key if present.
-    """
-    batch = default_data_collator(features)
-    _ = batch.pop("pairID", None)
-    return batch
-
-
-def main():
-    # See all possible arguments in src/transformers/training_args.py
-    # or by passing the --help flag to this script.
-    # We now keep distinct sets of args, for a cleaner separation of concerns.
-
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
-    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
-    if (
-        os.path.exists(training_args.output_dir)
-        and os.listdir(training_args.output_dir)
-        and training_args.do_train
-        and not training_args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
-        )
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
-    )
-    logger.warning(
-        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
-        training_args.local_rank,
-        training_args.device,
-        training_args.n_gpu,
-        bool(training_args.local_rank != -1),
-        training_args.fp16,
-    )
-    logger.info("Training/evaluation parameters %s", training_args)
-
-    # Set seed
-    set_seed(training_args.seed)
-
-    try:
-        processor = hans_processors[data_args.task_name]()
-        label_list = processor.get_labels()
-        num_labels = len(label_list)
-    except KeyError:
-        raise ValueError("Task not found: %s" % (data_args.task_name))
-
-    # Load pretrained model and tokenizer
-    #
-    # Distributed training:
-    # The .from_pretrained methods guarantee that only one local process can concurrently
-    # download model & vocab.
-
-    config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
-        num_labels=num_labels,
-        finetuning_task=data_args.task_name,
-        cache_dir=model_args.cache_dir,
-    )
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-    )
-    model = AutoModelForSequenceClassification.from_pretrained(
-        model_args.model_name_or_path,
-        from_tf=bool(".ckpt" in model_args.model_name_or_path),
-        config=config,
-        cache_dir=model_args.cache_dir,
-    )
-
-    # Get datasets
-    train_dataset = (
-        HansDataset(
-            data_dir=data_args.data_dir,
-            tokenizer=tokenizer,
-            task=data_args.task_name,
-            max_seq_length=data_args.max_seq_length,
-            overwrite_cache=data_args.overwrite_cache,
-        )
-        if training_args.do_train
-        else None
-    )
-    eval_dataset = (
-        HansDataset(
-            data_dir=data_args.data_dir,
-            tokenizer=tokenizer,
-            task=data_args.task_name,
-            max_seq_length=data_args.max_seq_length,
-            overwrite_cache=data_args.overwrite_cache,
-            evaluate=True,
-        )
-        if training_args.do_eval
-        else None
-    )
-
-    # Initialize our Trainer
-    trainer = Trainer(
-        model=model,
-        args=training_args,
-        train_dataset=train_dataset,
-        eval_dataset=eval_dataset,
-        data_collator=hans_data_collator,
-    )
-
-    # Training
-    if training_args.do_train:
-        trainer.train(
-            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
-        )
-        trainer.save_model()
-        # For convenience, we also re-save the tokenizer to the same directory,
-        # so that you can share your model easily on huggingface.co/models =)
-        if trainer.is_world_master():
-            tokenizer.save_pretrained(training_args.output_dir)
-
-    # Evaluation
-    if training_args.do_eval:
-        logger.info("*** Evaluate ***")
-
-        output = trainer.predict(eval_dataset)
-        preds = output.predictions
-        preds = np.argmax(preds, axis=1)
-
-        pair_ids = [ex.pairID for ex in eval_dataset]
-        output_eval_file = os.path.join(training_args.output_dir, "hans_predictions.txt")
-        if trainer.is_world_master():
-            with open(output_eval_file, "w") as writer:
-                writer.write("pairID,gold_label\n")
-                for pid, pred in zip(pair_ids, preds):
-                    writer.write("ex" + str(pid) + "," + label_list[int(pred)] + "\n")
-
-        trainer._log(output.metrics)
-
-
-def _mp_fn(index):
-    # For xla_spawn (TPUs)
-    main()
-
-
-if __name__ == "__main__":
-    main()

examples/adversarial/test_hans.py

@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and Microsoft Corporation.
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,37 +13,47 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-""" Training and inference using the library models for sequence classification on GLUE (Bert, Albert) with PABEE."""
+""" Finetuning the library models for sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa)."""
 
+from __future__ import absolute_import, division, print_function
 
 import argparse
 import glob
-import json
 import logging
 import os
 import random
 
 import numpy as np
 import torch
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange
 
-from pabee.modeling_pabee_albert import AlbertForSequenceClassificationWithPabee
-from pabee.modeling_pabee_bert import BertForSequenceClassificationWithPabee
 from transformers import (
     WEIGHTS_NAME,
     AdamW,
     AlbertConfig,
+    AlbertForSequenceClassification,
     AlbertTokenizer,
     BertConfig,
+    BertForSequenceClassification,
     BertTokenizer,
+    DistilBertConfig,
+    DistilBertForSequenceClassification,
+    DistilBertTokenizer,
+    RobertaConfig,
+    RobertaForSequenceClassification,
+    RobertaTokenizer,
+    XLMConfig,
+    XLMForSequenceClassification,
+    XLMTokenizer,
+    XLNetConfig,
+    XLNetForSequenceClassification,
+    XLNetTokenizer,
+    default_data_collator,
     get_linear_schedule_with_warmup,
 )
-from transformers import glue_compute_metrics as compute_metrics
-from transformers import glue_convert_examples_to_features as convert_examples_to_features
-from transformers import glue_output_modes as output_modes
-from transformers import glue_processors as processors
+from utils_hans import HansDataset, hans_output_modes, hans_processors
 
 
 try:
@@ -54,9 +64,14 @@ except ImportError:
 
 logger = logging.getLogger(__name__)
 
+
 MODEL_CLASSES = {
-    "bert": (BertConfig, BertForSequenceClassificationWithPabee, BertTokenizer),
-    "albert": (AlbertConfig, AlbertForSequenceClassificationWithPabee, AlbertTokenizer),
+    "bert": (BertConfig, BertForSequenceClassification, BertTokenizer),
+    "xlnet": (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
+    "xlm": (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
+    "roberta": (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
+    "distilbert": (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer),
+    "albert": (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer),
 }
 
 
@@ -75,7 +90,9 @@ def train(args, train_dataset, model, tokenizer):
 
     args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
     train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
-    train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
+    train_dataloader = DataLoader(
+        train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=default_data_collator,
+    )
 
     if args.max_steps > 0:
         t_total = args.max_steps
@@ -97,15 +114,6 @@ def train(args, train_dataset, model, tokenizer):
     scheduler = get_linear_schedule_with_warmup(
         optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
     )
-
-    # Check if saved optimizer or scheduler states exist
-    if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
-        os.path.join(args.model_name_or_path, "scheduler.pt")
-    ):
-        # Load in optimizer and scheduler states
-        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
-        scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
-
     if args.fp16:
         try:
             from apex import amp
@@ -120,7 +128,7 @@ def train(args, train_dataset, model, tokenizer):
     # Distributed training (should be after apex fp16 initialization)
     if args.local_rank != -1:
         model = torch.nn.parallel.DistributedDataParallel(
-            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True,
+            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
         )
 
     # Train!
@@ -138,45 +146,15 @@ def train(args, train_dataset, model, tokenizer):
     logger.info("  Total optimization steps = %d", t_total)
 
     global_step = 0
-    epochs_trained = 0
-    steps_trained_in_current_epoch = 0
-    # Check if continuing training from a checkpoint
-    if os.path.exists(args.model_name_or_path):
-        # set global_step to gobal_step of last saved checkpoint from model path
-        global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
-        epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
-        steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
-
-        logger.info("  Continuing training from checkpoint, will skip to saved global_step")
-        logger.info("  Continuing training from epoch %d", epochs_trained)
-        logger.info("  Continuing training from global step %d", global_step)
-        logger.info(
-            "  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch,
-        )
-
     tr_loss, logging_loss = 0.0, 0.0
     model.zero_grad()
-    train_iterator = trange(
-        epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0],
-    )
-    set_seed(args)  # Added here for reproductibility
+    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
+    set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
     for _ in train_iterator:
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
-
-            # Skip past any already trained steps if resuming training
-            if steps_trained_in_current_epoch > 0:
-                steps_trained_in_current_epoch -= 1
-                continue
-
             model.train()
-            batch = tuple(t.to(args.device) for t in batch)
-            inputs = {
-                "input_ids": batch[0],
-                "attention_mask": batch[1],
-                "labels": batch[3],
-            }
-            inputs["token_type_ids"] = batch[2]
+            inputs = {k: t.to(args.device) for k, t in batch.items() if k != "pairID"}
             outputs = model(**inputs)
             loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
 
@@ -221,7 +199,7 @@ def train(args, train_dataset, model, tokenizer):
 
                     for key, value in logs.items():
                         tb_writer.add_scalar(key, value, global_step)
-                    print(json.dumps({**logs, **{"step": global_step}}))
+                    # print(json.dumps({**logs, **{'step': global_step}}))
 
                 if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                     # Save model checkpoint
@@ -232,15 +210,9 @@ def train(args, train_dataset, model, tokenizer):
                         model.module if hasattr(model, "module") else model
                     )  # Take care of distributed/parallel training
                     model_to_save.save_pretrained(output_dir)
-                    tokenizer.save_pretrained(output_dir)
-
                     torch.save(args, os.path.join(output_dir, "training_args.bin"))
                     logger.info("Saving model checkpoint to %s", output_dir)
 
-                    torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
-                    torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
-                    logger.info("Saving optimizer and scheduler states to %s", output_dir)
-
             if args.max_steps > 0 and global_step > args.max_steps:
                 epoch_iterator.close()
                 break
@@ -254,26 +226,21 @@ def train(args, train_dataset, model, tokenizer):
     return global_step, tr_loss / global_step
 
 
-def evaluate(args, model, tokenizer, prefix="", patience=0):
-
-    if args.model_type == "albert":
-        model.albert.set_regression_threshold(args.regression_threshold)
-        model.albert.set_patience(patience)
-        model.albert.reset_stats()
-    elif args.model_type == "bert":
-        model.bert.set_regression_threshold(args.regression_threshold)
-        model.bert.set_patience(patience)
-        model.bert.reset_stats()
-    else:
-        raise NotImplementedError()
-
+def evaluate(args, model, tokenizer, label_list, prefix=""):
     # Loop to handle MNLI double evaluation (matched, mis-matched)
     eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
     eval_outputs_dirs = (args.output_dir, args.output_dir + "-MM") if args.task_name == "mnli" else (args.output_dir,)
 
     results = {}
     for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
-        eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)
+        eval_dataset = HansDataset(
+            args.data_dir,
+            tokenizer,
+            args.task_name,
+            args.max_seq_length,
+            overwrite_cache=args.overwrite_cache,
+            evaluate=True,
+        )
 
         if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
             os.makedirs(eval_output_dir)
@@ -281,7 +248,9 @@ def evaluate(args, model, tokenizer, prefix="", patience=0):
         args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
         # Note that DistributedSampler samples randomly
         eval_sampler = SequentialSampler(eval_dataset)
-        eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
+        eval_dataloader = DataLoader(
+            eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=default_data_collator,
+        )
 
         # multi-gpu eval
         if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
@@ -297,15 +266,9 @@ def evaluate(args, model, tokenizer, prefix="", patience=0):
         out_label_ids = None
         for batch in tqdm(eval_dataloader, desc="Evaluating"):
             model.eval()
-            batch = tuple(t.to(args.device) for t in batch)
-
+            inputs = {k: t.to(args.device) for k, t in batch.items() if k != "pairID"}
+            pair_ids = batch.pop("pairID", None)
             with torch.no_grad():
-                inputs = {
-                    "input_ids": batch[0],
-                    "attention_mask": batch[1],
-                    "labels": batch[3],
-                }
-                inputs["token_type_ids"] = batch[2]
                 outputs = model(**inputs)
                 tmp_eval_loss, logits = outputs[:2]
 
@@ -314,88 +277,27 @@ def evaluate(args, model, tokenizer, prefix="", patience=0):
             if preds is None:
                 preds = logits.detach().cpu().numpy()
                 out_label_ids = inputs["labels"].detach().cpu().numpy()
+                pair_ids = pair_ids.detach().cpu().numpy()
             else:
                 preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
                 out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
+                pair_ids = np.append(pair_ids, pair_ids.detach().cpu().numpy(), axis=0)
 
         eval_loss = eval_loss / nb_eval_steps
         if args.output_mode == "classification":
             preds = np.argmax(preds, axis=1)
         elif args.output_mode == "regression":
             preds = np.squeeze(preds)
-        result = compute_metrics(eval_task, preds, out_label_ids)
-        results.update(result)
 
-        output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
+        output_eval_file = os.path.join(eval_output_dir, "hans_predictions.txt")
         with open(output_eval_file, "w") as writer:
-            logger.info("***** Eval results {} *****".format(prefix))
-            for key in sorted(result.keys()):
-                logger.info("  %s = %s", key, str(result[key]))
-                print("  %s = %s" % (key, str(result[key])))
-                writer.write("%s = %s\n" % (key, str(result[key])))
-
-    if args.eval_all_checkpoints and patience != 0:
-        if args.model_type == "albert":
-            model.albert.log_stats()
-        elif args.model_type == "bert":
-            model.bert.log_stats()
-        else:
-            raise NotImplementedError()
+            writer.write("pairID,gld_label\n")
+            for pid, pred in zip(pair_ids, preds):
+                writer.write("ex" + str(pid) + "," + label_list[int(pred)] + "\n")
 
     return results
 
 
-def load_and_cache_examples(args, task, tokenizer, evaluate=False):
-    if args.local_rank not in [-1, 0] and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
-
-    processor = processors[task]()
-    output_mode = output_modes[task]
-    # Load data features from cache or dataset file
-    cached_features_file = os.path.join(
-        args.data_dir,
-        "cached_{}_{}_{}_{}".format(
-            "dev" if evaluate else "train",
-            list(filter(None, args.model_name_or_path.split("/"))).pop(),
-            str(args.max_seq_length),
-            str(task),
-        ),
-    )
-    if os.path.exists(cached_features_file) and not args.overwrite_cache:
-        logger.info("Loading features from cached file %s", cached_features_file)
-        features = torch.load(cached_features_file)
-    else:
-        logger.info("Creating features from dataset file at %s", args.data_dir)
-        label_list = processor.get_labels()
-        if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta", "xlmroberta"]:
-            # HACK(label indices are swapped in RoBERTa pretrained model)
-            label_list[1], label_list[2] = label_list[2], label_list[1]
-        examples = (
-            processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
-        )
-        features = convert_examples_to_features(
-            examples, tokenizer, label_list=label_list, max_length=args.max_seq_length, output_mode=output_mode,
-        )
-        if args.local_rank in [-1, 0]:
-            logger.info("Saving features into cached file %s", cached_features_file)
-            torch.save(features, cached_features_file)
-
-    if args.local_rank == 0 and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
-
-    # Convert to Tensors and build dataset
-    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
-    all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
-    all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
-    if output_mode == "classification":
-        all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
-    elif output_mode == "regression":
-        all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
-
-    dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
-    return dataset
-
-
 def main():
     parser = argparse.ArgumentParser()
 
@@ -419,14 +321,14 @@ def main():
         default=None,
         type=str,
         required=True,
-        help="Path to pre-trained model or shortcut name.",
+        help="Path to pretrained model or model identifier from huggingface.co/models",
     )
     parser.add_argument(
         "--task_name",
         default=None,
         type=str,
         required=True,
-        help="The name of the task to train selected in the list: " + ", ".join(processors.keys()),
+        help="The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()),
     )
     parser.add_argument(
         "--output_dir",
@@ -435,16 +337,10 @@ def main():
         required=True,
         help="The output directory where the model predictions and checkpoints will be written.",
     )
-    parser.add_argument(
-        "--patience", default="0", type=str, required=False,
-    )
-    parser.add_argument(
-        "--regression_threshold", default=0, type=float, required=False,
-    )
 
     # Other parameters
     parser.add_argument(
-        "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name",
+        "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
     )
     parser.add_argument(
         "--tokenizer_name",
@@ -468,17 +364,15 @@ def main():
     parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
     parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
     parser.add_argument(
-        "--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step.",
+        "--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step."
     )
     parser.add_argument(
-        "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model.",
+        "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
     )
 
+    parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
     parser.add_argument(
-        "--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.",
-    )
-    parser.add_argument(
-        "--per_gpu_eval_batch_size", default=1, type=int, help="Batch size per GPU/CPU for evaluation.",
+        "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
     )
     parser.add_argument(
         "--gradient_accumulation_steps",
@@ -486,14 +380,12 @@ def main():
         default=1,
         help="Number of updates steps to accumulate before performing a backward/update pass.",
     )
-    parser.add_argument(
-        "--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.",
-    )
+    parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
     parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
     parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
     parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
     parser.add_argument(
-        "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.",
+        "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
     )
     parser.add_argument(
         "--max_steps",
@@ -503,10 +395,8 @@ def main():
     )
     parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
 
-    parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
-    parser.add_argument(
-        "--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.",
-    )
+    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
     parser.add_argument(
         "--eval_all_checkpoints",
         action="store_true",
@@ -514,10 +404,10 @@ def main():
     )
     parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
     parser.add_argument(
-        "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory",
+        "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
     )
     parser.add_argument(
-        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets",
+        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
     )
     parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
 
@@ -533,9 +423,7 @@ def main():
         help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
         "See details at https://nvidia.github.io/apex/amp.html",
     )
-    parser.add_argument(
-        "--local_rank", type=int, default=-1, help="For distributed training: local_rank",
-    )
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
     parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.")
     parser.add_argument("--server_port", type=str, default="", help="For distant debugging.")
     args = parser.parse_args()
@@ -564,7 +452,7 @@ def main():
     # Setup CUDA, GPU & distributed training
     if args.local_rank == -1 or args.no_cuda:
         device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-        args.n_gpu = torch.cuda.device_count()
+        args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
     else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
         torch.cuda.set_device(args.local_rank)
         device = torch.device("cuda", args.local_rank)
@@ -592,16 +480,13 @@ def main():
 
     # Prepare GLUE task
     args.task_name = args.task_name.lower()
-    if args.task_name not in processors:
+    if args.task_name not in hans_processors:
         raise ValueError("Task not found: %s" % (args.task_name))
-    processor = processors[args.task_name]()
-    args.output_mode = output_modes[args.task_name]
+    processor = hans_processors[args.task_name]()
+    args.output_mode = hans_output_modes[args.task_name]
     label_list = processor.get_labels()
     num_labels = len(label_list)
 
-    if args.patience != "0" and args.per_gpu_eval_batch_size != 1:
-        raise ValueError("The eval batch size must be 1 with PABEE inference on.")
-
     # Load pretrained model and tokenizer
     if args.local_rank not in [-1, 0]:
         torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
@@ -631,19 +516,13 @@ def main():
 
     model.to(args.device)
 
-    print("Total Model Parameters:", sum(param.numel() for param in model.parameters()))
-    output_layers_param_num = sum(param.numel() for param in model.classifiers.parameters())
-    print("Output Layers Parameters:", output_layers_param_num)
-    single_output_layer_param_num = sum(param.numel() for param in model.classifiers[0].parameters())
-    print(
-        "Added Output Layers Parameters:", output_layers_param_num - single_output_layer_param_num,
-    )
-
     logger.info("Training/evaluation parameters %s", args)
 
     # Training
     if args.do_train:
-        train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
+        train_dataset = HansDataset(
+            args.data_dir, tokenizer, args.task_name, args.max_seq_length, overwrite_cache=args.overwrite_cache
+        )
         global_step, tr_loss = train(args, train_dataset, model, tokenizer)
         logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
 
@@ -673,7 +552,6 @@ def main():
     # Evaluation
     results = {}
     if args.do_eval and args.local_rank in [-1, 0]:
-        patience_list = [int(x) for x in args.patience.split(",")]
         tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
         checkpoints = [args.output_dir]
         if args.eval_all_checkpoints:
@@ -682,20 +560,16 @@ def main():
             )
             logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
         logger.info("Evaluate the following checkpoints: %s", checkpoints)
-
         for checkpoint in checkpoints:
-
             global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
             prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
 
             model = model_class.from_pretrained(checkpoint)
             model.to(args.device)
+            result = evaluate(args, model, tokenizer, label_list, prefix=prefix)
+            result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
+            results.update(result)
 
-            print(f"Evaluation for checkpoint {prefix}")
-            for patience in patience_list:
-                result = evaluate(args, model, tokenizer, prefix=prefix, patience=patience)
-                result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
-                results.update(result)
     return results
 
 

examples/adversarial/utils_hans.py

@@ -22,7 +22,15 @@ from typing import List, Optional, Union
 import tqdm
 from filelock import FileLock
 
-from transformers import DataProcessor, PreTrainedTokenizer, is_tf_available, is_torch_available
+from transformers import (
+    DataProcessor,
+    PreTrainedTokenizer,
+    RobertaTokenizer,
+    RobertaTokenizerFast,
+    XLMRobertaTokenizer,
+    is_tf_available,
+    is_torch_available,
+)
 
 
 logger = logging.getLogger(__name__)
@@ -98,6 +106,7 @@ if is_torch_available():
             evaluate: bool = False,
         ):
             processor = hans_processors[task]()
+            output_mode = hans_output_modes[task]
 
             cached_features_file = os.path.join(
                 data_dir,
@@ -118,12 +127,22 @@ if is_torch_available():
                     logger.info(f"Creating features from dataset file at {data_dir}")
                     label_list = processor.get_labels()
 
+                    if task in ["mnli", "mnli-mm"] and tokenizer.__class__ in (
+                        RobertaTokenizer,
+                        RobertaTokenizerFast,
+                        XLMRobertaTokenizer,
+                    ):
+                        # HACK(label indices are swapped in RoBERTa pretrained model)
+                        label_list[1], label_list[2] = label_list[2], label_list[1]
                     examples = (
                         processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
                     )
 
                     logger.info("Training examples: %s", len(examples))
-                    self.features = hans_convert_examples_to_features(examples, label_list, max_seq_length, tokenizer)
+                    # TODO clean up all this to leverage built-in features of tokenizers
+                    self.features = hans_convert_examples_to_features(
+                        examples, label_list, max_seq_length, tokenizer, output_mode
+                    )
                     logger.info("Saving features into cached file %s", cached_features_file)
                     torch.save(self.features, cached_features_file)
 
@@ -155,10 +174,21 @@ if is_tf_available():
             evaluate: bool = False,
         ):
             processor = hans_processors[task]()
+            output_mode = hans_output_modes[task]
             label_list = processor.get_labels()
 
+            if task in ["mnli", "mnli-mm"] and tokenizer.__class__ in (
+                RobertaTokenizer,
+                RobertaTokenizerFast,
+                XLMRobertaTokenizer,
+            ):
+                # HACK(label indices are swapped in RoBERTa pretrained model)
+                label_list[1], label_list[2] = label_list[2], label_list[1]
+
             examples = processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
-            self.features = hans_convert_examples_to_features(examples, label_list, max_seq_length, tokenizer)
+            self.features = hans_convert_examples_to_features(
+                examples, label_list, max_seq_length, tokenizer, output_mode
+            )
 
             def gen():
                 for (ex_index, ex) in tqdm.tqdm(enumerate(self.features), desc="convert examples to features"):
@@ -210,6 +240,15 @@ if is_tf_available():
 class HansProcessor(DataProcessor):
     """Processor for the HANS data set."""
 
+    def get_example_from_tensor_dict(self, tensor_dict):
+        """See base class."""
+        return InputExample(
+            tensor_dict["idx"].numpy(),
+            tensor_dict["premise"].numpy().decode("utf-8"),
+            tensor_dict["hypothesis"].numpy().decode("utf-8"),
+            str(tensor_dict["label"].numpy()),
+        )
+
     def get_train_examples(self, data_dir):
         """See base class."""
         return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_train_set.txt")), "train")
@@ -238,7 +277,11 @@ class HansProcessor(DataProcessor):
 
 
 def hans_convert_examples_to_features(
-    examples: List[InputExample], label_list: List[str], max_length: int, tokenizer: PreTrainedTokenizer,
+    examples: List[InputExample],
+    label_list: List[str],
+    max_length: int,
+    tokenizer: PreTrainedTokenizer,
+    output_mode: str,
 ):
     """
     Loads a data file into a list of ``InputFeatures``
@@ -270,8 +313,19 @@ def hans_convert_examples_to_features(
             pad_to_max_length=True,
             return_overflowing_tokens=True,
         )
+        if "num_truncated_tokens" in inputs and inputs["num_truncated_tokens"] > 0:
+            logger.info(
+                "Attention! you are cropping tokens (swag task is ok). "
+                "If you are training ARC and RACE and you are poping question + options,"
+                "you need to try to use a bigger max seq length!"
+            )
 
-        label = label_map[example.label] if example.label in label_map else 0
+        if output_mode == "classification":
+            label = label_map[example.label] if example.label in label_map else 0
+        elif output_mode == "regression":
+            label = float(example.label)
+        else:
+            raise KeyError(output_mode)
 
         pairID = int(example.pairID)
 
@@ -292,3 +346,7 @@ hans_tasks_num_labels = {
 hans_processors = {
     "hans": HansProcessor,
 }
+
+hans_output_modes = {
+    "hans": "classification",
+}

examples/benchmarking/run_benchmark.py

@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2020 The HuggingFace Inc. team.
+# Copyright 2018 The HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/benchmarking/run_benchmark_tf.py

@@ -1,29 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The HuggingFace Inc. team.
-# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Benchmarking the library on inference and training in Tensorflow"""
-
-from transformers import HfArgumentParser, TensorflowBenchmark, TensorflowBenchmarkArguments
-
-
-def main():
-    parser = HfArgumentParser(TensorflowBenchmarkArguments)
-    benchmark_args = parser.parse_args_into_dataclasses()[0]
-    benchmark = TensorflowBenchmark(args=benchmark_args)
-    benchmark.run()
-
-
-if __name__ == "__main__":
-    main()

examples/bert-loses-patience/README.md

@@ -1,89 +0,0 @@
-# Patience-based Early Exit
-
-Patience-based Early Exit (PABEE) is a plug-and-play inference method for pretrained language models.
-We have already implemented it on BERT and ALBERT. Basically, you can make your LM faster and more robust with PABEE. It can even improve the performance of ALBERT on GLUE. The only sacrifice is that the batch size can only be 1.
-Learn more in the paper ["BERT Loses Patience: Fast and Robust Inference with Early Exit"](https://arxiv.org/abs/2006.04152) and the official [GitHub repo](https://github.com/JetRunner/PABEE).
-
-![PABEE](https://github.com/JetRunner/PABEE/raw/master/bert-loses-patience.png)
-
-## Training
-
-You can fine-tune a pretrained language model (you can choose from BERT and ALBERT) and train the internal classifiers by:
-```bash
-export GLUE_DIR=/path/to/glue_data
-export TASK_NAME=MRPC
-
-python ./run_glue_with_pabee.py \
-  --model_type albert \
-  --model_name_or_path bert-base-uncased/albert-base-v2 \
-  --task_name $TASK_NAME \
-  --do_train \
-  --do_eval \
-  --do_lower_case \
-  --data_dir "$GLUE_DIR/$TASK_NAME" \
-  --max_seq_length 128 \
-  --per_gpu_train_batch_size 32 \
-  --per_gpu_eval_batch_size 32 \
-  --learning_rate 2e-5 \
-  --save_steps 50 \
-  --logging_steps 50 \
-  --num_train_epochs 5 \
-  --output_dir /path/to/save/ \
-  --evaluate_during_training
-```
-
-## Inference
-
-You can inference with different patience settings by:
-```bash
-export GLUE_DIR=/path/to/glue_data
-export TASK_NAME=MRPC
-
-python ./run_glue_with_pabee.py \
-  --model_type albert \
-  --model_name_or_path /path/to/save/ \
-  --task_name $TASK_NAME \
-  --do_eval \
-  --do_lower_case \
-  --data_dir "$GLUE_DIR/$TASK_NAME" \
-  --max_seq_length 128 \
-  --per_gpu_eval_batch_size 1 \
-  --learning_rate 2e-5 \
-  --logging_steps 50 \
-  --num_train_epochs 15 \
-  --output_dir /path/to/save/ \
-  --eval_all_checkpoints \
-  --patience 3,4,5,6,7,8
-```
-where `patience` can be a list of patience settings, separated by a comma. It will help determine which patience works best.
-
-When evaluating on a regression task (STS-B), you may add `--regression_threshold 0.1` to define the regression threshold.
-
-## Results
-On the GLUE dev set:
-
-| Model        | \#Param | Speed  | CoLA  | MNLI  | MRPC  | QNLI  | QQP   | RTE   | SST\-2 | STS\-B |
-|--------------|---------|--------|-------|-------|-------|-------|-------|-------|--------|--------|
-| ALBERT\-base | 12M     |        | 58\.9 | 84\.6 | 89\.5 | 91\.7 | 89\.6 | 78\.6 | 92\.8  | 89\.5  |
-| \+PABEE      | 12M     | 1\.57x | 61\.2 | 85\.1 | 90\.0 | 91\.8 | 89\.6 | 80\.1 | 93\.0  | 90\.1  |
-
-| Model         | \#Param | Speed\-up | MNLI  | SST\-2 | STS\-B |
-|---------------|---------|-----------|-------|--------|--------|
-| BERT\-base    | 108M    |           | 84\.5 | 92\.1  | 88\.9  |
-| \+PABEE       | 108M    | 1\.62x    | 83\.6 | 92\.0  | 88\.7  |
-| ALBERT\-large | 18M     |           | 86\.4 | 94\.9  | 90\.4  |
-| \+PABEE       | 18M     | 2\.42x    | 86\.8 | 95\.2  | 90\.6  |
-
-
-## Citation
-If you find this resource useful, please consider citing the following paper:
-```bibtex
-@misc{zhou2020bert,
-    title={BERT Loses Patience: Fast and Robust Inference with Early Exit},
-    author={Wangchunshu Zhou and Canwen Xu and Tao Ge and Julian McAuley and Ke Xu and Furu Wei},
-    year={2020},
-    eprint={2006.04152},
-    archivePrefix={arXiv},
-    primaryClass={cs.CL}
-}
-```

examples/bert-loses-patience/pabee/modeling_pabee_albert.py

@@ -1,310 +0,0 @@
-# coding=utf-8
-# Copyright 2020 Google AI, Google Brain, the HuggingFace Inc. team and Microsoft Corporation.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch ALBERT model with Patience-based Early Exit. """
-
-import logging
-
-import torch
-import torch.nn as nn
-from torch.nn import CrossEntropyLoss, MSELoss
-
-from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_callable
-from transformers.modeling_albert import (
-    ALBERT_INPUTS_DOCSTRING,
-    ALBERT_START_DOCSTRING,
-    AlbertModel,
-    AlbertPreTrainedModel,
-    AlbertTransformer,
-)
-
-
-logger = logging.getLogger(__name__)
-
-
-class AlbertTransformerWithPabee(AlbertTransformer):
-    def adaptive_forward(self, hidden_states, current_layer, attention_mask=None, head_mask=None):
-        if current_layer == 0:
-            hidden_states = self.embedding_hidden_mapping_in(hidden_states)
-        else:
-            hidden_states = hidden_states[0]
-
-        layers_per_group = int(self.config.num_hidden_layers / self.config.num_hidden_groups)
-
-        # Index of the hidden group
-        group_idx = int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups))
-
-        layer_group_output = self.albert_layer_groups[group_idx](
-            hidden_states,
-            attention_mask,
-            head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group],
-        )
-        hidden_states = layer_group_output[0]
-
-        return (hidden_states,)
-
-
-@add_start_docstrings(
-    "The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.",
-    ALBERT_START_DOCSTRING,
-)
-class AlbertModelWithPabee(AlbertModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.encoder = AlbertTransformerWithPabee(config)
-
-        self.init_weights()
-        self.patience = 0
-        self.inference_instances_num = 0
-        self.inference_layers_num = 0
-
-        self.regression_threshold = 0
-
-    def set_regression_threshold(self, threshold):
-        self.regression_threshold = threshold
-
-    def set_patience(self, patience):
-        self.patience = patience
-
-    def reset_stats(self):
-        self.inference_instances_num = 0
-        self.inference_layers_num = 0
-
-    def log_stats(self):
-        avg_inf_layers = self.inference_layers_num / self.inference_instances_num
-        message = f"*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up = {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"
-        print(message)
-
-    @add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        output_dropout=None,
-        output_layers=None,
-        regression=False,
-    ):
-        r"""
-    Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs:
-        last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
-            Sequence of hidden-states at the output of the last layer of the model.
-        pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
-            Last layer hidden-state of the first token of the sequence (classification token)
-            further processed by a Linear layer and a Tanh activation function. The Linear
-            layer weights are trained from the next sentence prediction (classification)
-            objective during pre-training.
-
-            This output is usually *not* a good summary
-            of the semantic content of the input, you're often better with averaging or pooling
-            the sequence of hidden-states for the whole input sequence.
-        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
-            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
-            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-        """
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        if attention_mask is None:
-            attention_mask = torch.ones(input_shape, device=device)
-        if token_type_ids is None:
-            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
-
-        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
-        embedding_output = self.embeddings(
-            input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
-        )
-        encoder_outputs = embedding_output
-
-        if self.training:
-            res = []
-            for i in range(self.config.num_hidden_layers):
-                encoder_outputs = self.encoder.adaptive_forward(
-                    encoder_outputs, current_layer=i, attention_mask=extended_attention_mask, head_mask=head_mask,
-                )
-
-                pooled_output = self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
-                logits = output_layers[i](output_dropout(pooled_output))
-                res.append(logits)
-        elif self.patience == 0:  # Use all layers for inference
-            encoder_outputs = self.encoder(encoder_outputs, extended_attention_mask, head_mask=head_mask)
-            pooled_output = self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
-            res = [output_layers[self.config.num_hidden_layers - 1](pooled_output)]
-        else:
-            patient_counter = 0
-            patient_result = None
-            calculated_layer_num = 0
-            for i in range(self.config.num_hidden_layers):
-                calculated_layer_num += 1
-                encoder_outputs = self.encoder.adaptive_forward(
-                    encoder_outputs, current_layer=i, attention_mask=extended_attention_mask, head_mask=head_mask,
-                )
-
-                pooled_output = self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
-                logits = output_layers[i](pooled_output)
-                if regression:
-                    labels = logits.detach()
-                    if patient_result is not None:
-                        patient_labels = patient_result.detach()
-                    if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
-                        patient_counter += 1
-                    else:
-                        patient_counter = 0
-                else:
-                    labels = logits.detach().argmax(dim=1)
-                    if patient_result is not None:
-                        patient_labels = patient_result.detach().argmax(dim=1)
-                    if (patient_result is not None) and torch.all(labels.eq(patient_labels)):
-                        patient_counter += 1
-                    else:
-                        patient_counter = 0
-
-                patient_result = logits
-                if patient_counter == self.patience:
-                    break
-            res = [patient_result]
-            self.inference_layers_num += calculated_layer_num
-            self.inference_instances_num += 1
-
-        return res
-
-
-@add_start_docstrings(
-    """Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
-    the pooled output) e.g. for GLUE tasks. """,
-    ALBERT_START_DOCSTRING,
-)
-class AlbertForSequenceClassificationWithPabee(AlbertPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.albert = AlbertModelWithPabee(config)
-        self.dropout = nn.Dropout(config.classifier_dropout_prob)
-        self.classifiers = nn.ModuleList(
-            [nn.Linear(config.hidden_size, self.config.num_labels) for _ in range(config.num_hidden_layers)]
-        )
-
-        self.init_weights()
-
-    @add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        labels=None,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
-            Labels for computing the sequence classification/regression loss.
-            Indices should be in ``[0, ..., config.num_labels - 1]``.
-            If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
-            If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
-
-    Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs:
-        loss: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
-            Classification (or regression if config.num_labels==1) loss.
-        logits ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
-            Classification (or regression if config.num_labels==1) scores (before SoftMax).
-        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
-            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
-            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-
-        Examples::
-
-            from transformers import AlbertTokenizer
-            from pabee import AlbertForSequenceClassificationWithPabee
-            import torch
-
-            tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
-            model = AlbertForSequenceClassificationWithPabee.from_pretrained('albert-base-v2')
-            input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
-            labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
-            outputs = model(input_ids, labels=labels)
-            loss, logits = outputs[:2]
-
-        """
-
-        logits = self.albert(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_dropout=self.dropout,
-            output_layers=self.classifiers,
-            regression=self.num_labels == 1,
-        )
-
-        outputs = (logits[-1],)
-
-        if labels is not None:
-            total_loss = None
-            total_weights = 0
-            for ix, logits_item in enumerate(logits):
-                if self.num_labels == 1:
-                    #  We are doing regression
-                    loss_fct = MSELoss()
-                    loss = loss_fct(logits_item.view(-1), labels.view(-1))
-                else:
-                    loss_fct = CrossEntropyLoss()
-                    loss = loss_fct(logits_item.view(-1, self.num_labels), labels.view(-1))
-                if total_loss is None:
-                    total_loss = loss
-                else:
-                    total_loss += loss * (ix + 1)
-                total_weights += ix + 1
-            outputs = (total_loss / total_weights,) + outputs
-
-        return outputs

examples/bert-loses-patience/pabee/modeling_pabee_bert.py

@@ -1,342 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and Microsoft Corporation.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch BERT model with Patience-based Early Exit. """
-
-
-import logging
-
-import torch
-from torch import nn
-from torch.nn import CrossEntropyLoss, MSELoss
-
-from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_callable
-from transformers.modeling_bert import (
-    BERT_INPUTS_DOCSTRING,
-    BERT_START_DOCSTRING,
-    BertEncoder,
-    BertModel,
-    BertPreTrainedModel,
-)
-
-
-logger = logging.getLogger(__name__)
-
-
-class BertEncoderWithPabee(BertEncoder):
-    def adaptive_forward(self, hidden_states, current_layer, attention_mask=None, head_mask=None):
-        layer_outputs = self.layer[current_layer](hidden_states, attention_mask, head_mask[current_layer])
-
-        hidden_states = layer_outputs[0]
-
-        return hidden_states
-
-
-@add_start_docstrings(
-    "The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.",
-    BERT_START_DOCSTRING,
-)
-class BertModelWithPabee(BertModel):
-    """
-
-    The model can behave as an encoder (with only self-attention) as well
-    as a decoder, in which case a layer of cross-attention is added between
-    the self-attention layers, following the architecture described in `Attention is all you need`_ by Ashish Vaswani,
-    Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
-
-    To behave as an decoder the model needs to be initialized with the
-    :obj:`is_decoder` argument of the configuration set to :obj:`True`; an
-    :obj:`encoder_hidden_states` is expected as an input to the forward pass.
-
-    .. _`Attention is all you need`:
-        https://arxiv.org/abs/1706.03762
-
-    """
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.encoder = BertEncoderWithPabee(config)
-
-        self.init_weights()
-        self.patience = 0
-        self.inference_instances_num = 0
-        self.inference_layers_num = 0
-
-        self.regression_threshold = 0
-
-    def set_regression_threshold(self, threshold):
-        self.regression_threshold = threshold
-
-    def set_patience(self, patience):
-        self.patience = patience
-
-    def reset_stats(self):
-        self.inference_instances_num = 0
-        self.inference_layers_num = 0
-
-    def log_stats(self):
-        avg_inf_layers = self.inference_layers_num / self.inference_instances_num
-        message = f"*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up = {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"
-        print(message)
-
-    @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        output_dropout=None,
-        output_layers=None,
-        regression=False,
-    ):
-        r"""
-    Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
-        last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
-            Sequence of hidden-states at the output of the last layer of the model.
-        pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
-            Last layer hidden-state of the first token of the sequence (classification token)
-            further processed by a Linear layer and a Tanh activation function. The Linear
-            layer weights are trained from the next sentence prediction (classification)
-            objective during pre-training.
-
-            This output is usually *not* a good summary
-            of the semantic content of the input, you're often better with averaging or pooling
-            the sequence of hidden-states for the whole input sequence.
-        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
-            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
-            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-        """
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        if attention_mask is None:
-            attention_mask = torch.ones(input_shape, device=device)
-        if token_type_ids is None:
-            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
-
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
-
-        # If a 2D ou 3D attention mask is provided for the cross-attention
-        # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length]
-        if self.config.is_decoder and encoder_hidden_states is not None:
-            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-            if encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-        else:
-            encoder_extended_attention_mask = None
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
-        embedding_output = self.embeddings(
-            input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
-        )
-        encoder_outputs = embedding_output
-
-        if self.training:
-            res = []
-            for i in range(self.config.num_hidden_layers):
-                encoder_outputs = self.encoder.adaptive_forward(
-                    encoder_outputs, current_layer=i, attention_mask=extended_attention_mask, head_mask=head_mask
-                )
-
-                pooled_output = self.pooler(encoder_outputs)
-                logits = output_layers[i](output_dropout(pooled_output))
-                res.append(logits)
-        elif self.patience == 0:  # Use all layers for inference
-            encoder_outputs = self.encoder(
-                embedding_output,
-                attention_mask=extended_attention_mask,
-                head_mask=head_mask,
-                encoder_hidden_states=encoder_hidden_states,
-                encoder_attention_mask=encoder_extended_attention_mask,
-            )
-            pooled_output = self.pooler(encoder_outputs[0])
-            res = [output_layers[self.config.num_hidden_layers - 1](pooled_output)]
-        else:
-            patient_counter = 0
-            patient_result = None
-            calculated_layer_num = 0
-            for i in range(self.config.num_hidden_layers):
-                calculated_layer_num += 1
-                encoder_outputs = self.encoder.adaptive_forward(
-                    encoder_outputs, current_layer=i, attention_mask=extended_attention_mask, head_mask=head_mask
-                )
-
-                pooled_output = self.pooler(encoder_outputs)
-                logits = output_layers[i](pooled_output)
-                if regression:
-                    labels = logits.detach()
-                    if patient_result is not None:
-                        patient_labels = patient_result.detach()
-                    if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
-                        patient_counter += 1
-                    else:
-                        patient_counter = 0
-                else:
-                    labels = logits.detach().argmax(dim=1)
-                    if patient_result is not None:
-                        patient_labels = patient_result.detach().argmax(dim=1)
-                    if (patient_result is not None) and torch.all(labels.eq(patient_labels)):
-                        patient_counter += 1
-                    else:
-                        patient_counter = 0
-
-                patient_result = logits
-                if patient_counter == self.patience:
-                    break
-            res = [patient_result]
-            self.inference_layers_num += calculated_layer_num
-            self.inference_instances_num += 1
-
-        return res
-
-
-@add_start_docstrings(
-    """Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
-    the pooled output) e.g. for GLUE tasks. """,
-    BERT_START_DOCSTRING,
-)
-class BertForSequenceClassificationWithPabee(BertPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.bert = BertModelWithPabee(config)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifiers = nn.ModuleList(
-            [nn.Linear(config.hidden_size, self.config.num_labels) for _ in range(config.num_hidden_layers)]
-        )
-
-        self.init_weights()
-
-    @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        labels=None,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
-            Labels for computing the sequence classification/regression loss.
-            Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
-            If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
-            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-
-    Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
-        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
-            Classification (or regression if config.num_labels==1) loss.
-        logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
-            Classification (or regression if config.num_labels==1) scores (before SoftMax).
-        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
-            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
-            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-
-    Examples::
-
-        from transformers import BertTokenizer, BertForSequenceClassification
-        from pabee import BertForSequenceClassificationWithPabee
-        import torch
-
-        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        model = BertForSequenceClassificationWithPabee.from_pretrained('bert-base-uncased')
-
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
-        labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
-        outputs = model(input_ids, labels=labels)
-
-        loss, logits = outputs[:2]
-
-        """
-
-        logits = self.bert(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_dropout=self.dropout,
-            output_layers=self.classifiers,
-            regression=self.num_labels == 1,
-        )
-
-        outputs = (logits[-1],)
-
-        if labels is not None:
-            total_loss = None
-            total_weights = 0
-            for ix, logits_item in enumerate(logits):
-                if self.num_labels == 1:
-                    #  We are doing regression
-                    loss_fct = MSELoss()
-                    loss = loss_fct(logits_item.view(-1), labels.view(-1))
-                else:
-                    loss_fct = CrossEntropyLoss()
-                    loss = loss_fct(logits_item.view(-1, self.num_labels), labels.view(-1))
-                if total_loss is None:
-                    total_loss = loss
-                else:
-                    total_loss += loss * (ix + 1)
-                total_weights += ix + 1
-            outputs = (total_loss / total_weights,) + outputs
-
-        return outputs

examples/bert-loses-patience/test_run_glue_with_pabee.py

@@ -1,48 +0,0 @@
-import argparse
-import logging
-import sys
-import unittest
-from unittest.mock import patch
-
-import run_glue_with_pabee
-
-
-logging.basicConfig(level=logging.DEBUG)
-
-logger = logging.getLogger()
-
-
-def get_setup_file():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-f")
-    args = parser.parse_args()
-    return args.f
-
-
-class PabeeTests(unittest.TestCase):
-    def test_run_glue(self):
-        stream_handler = logging.StreamHandler(sys.stdout)
-        logger.addHandler(stream_handler)
-
-        testargs = """
-            run_glue_with_pabee.py
-            --model_type albert
-            --model_name_or_path albert-base-v2
-            --data_dir ./tests/fixtures/tests_samples/MRPC/
-            --task_name mrpc
-            --do_train
-            --do_eval
-            --output_dir ./tests/fixtures/tests_samples/temp_dir
-            --per_gpu_train_batch_size=2
-            --per_gpu_eval_batch_size=1
-            --learning_rate=2e-5
-            --max_steps=50
-            --warmup_steps=2
-            --overwrite_output_dir
-            --seed=42
-            --max_seq_length=128
-            """.split()
-        with patch.object(sys, "argv", testargs):
-            result = run_glue_with_pabee.main()
-            for value in result.values():
-                self.assertGreaterEqual(value, 0.75)

examples/lightning_base.py

@@ -2,13 +2,10 @@ import argparse
 import logging
 import os
 import random
-from pathlib import Path
-from typing import Any, Dict
 
 import numpy as np
 import pytorch_lightning as pl
 import torch
-from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
 
 from transformers import (
     AdamW,
@@ -16,13 +13,10 @@ from transformers import (
     AutoModel,
     AutoModelForPreTraining,
     AutoModelForQuestionAnswering,
-    AutoModelForSeq2SeqLM,
     AutoModelForSequenceClassification,
     AutoModelForTokenClassification,
     AutoModelWithLMHead,
     AutoTokenizer,
-    PretrainedConfig,
-    PreTrainedTokenizer,
     get_linear_schedule_with_warmup,
 )
 
@@ -37,8 +31,6 @@ MODEL_MODES = {
     "pretraining": AutoModelForPreTraining,
     "token-classification": AutoModelForTokenClassification,
     "language-modeling": AutoModelWithLMHead,
-    "summarization": AutoModelForSeq2SeqLM,
-    "translation": AutoModelForSeq2SeqLM,
 }
 
 
@@ -46,60 +38,40 @@ def set_seed(args: argparse.Namespace):
     random.seed(args.seed)
     np.random.seed(args.seed)
     torch.manual_seed(args.seed)
-    if args.gpus > 0:
+    if args.n_gpu > 0:
         torch.cuda.manual_seed_all(args.seed)
 
 
 class BaseTransformer(pl.LightningModule):
-    def __init__(
-        self,
-        hparams: argparse.Namespace,
-        num_labels=None,
-        mode="base",
-        config=None,
-        tokenizer=None,
-        model=None,
-        **config_kwargs
-    ):
-        """Initialize a model, tokenizer and config."""
-        super().__init__()
-        self.hparams = hparams  # TODO: move to self.save_hyperparameters()
-        self.step_count = 0
-        self.tfmr_ckpts = {}
-        self.output_dir = Path(self.hparams.output_dir)
-        cache_dir = self.hparams.cache_dir if self.hparams.cache_dir else None
-        if config is None:
-            self.config = AutoConfig.from_pretrained(
-                self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,
-                **({"num_labels": num_labels} if num_labels is not None else {}),
-                cache_dir=cache_dir,
-                **config_kwargs,
-            )
-        else:
-            self.config: PretrainedConfig = config
-        if tokenizer is None:
-            self.tokenizer = AutoTokenizer.from_pretrained(
-                self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,
-                cache_dir=cache_dir,
-            )
-        else:
-            self.tokenizer: PreTrainedTokenizer = tokenizer
-        self.model_type = MODEL_MODES[mode]
-        if model is None:
-            self.model = self.model_type.from_pretrained(
-                self.hparams.model_name_or_path,
-                from_tf=bool(".ckpt" in self.hparams.model_name_or_path),
-                config=self.config,
-                cache_dir=cache_dir,
-            )
-        else:
-            self.model = model
+    def __init__(self, hparams: argparse.Namespace, num_labels=None, mode="base", **config_kwargs):
+        "Initialize a model."
 
-    def load_hf_checkpoint(self, *args, **kwargs):
-        self.model = self.model_type.from_pretrained(*args, **kwargs)
+        super().__init__()
+        self.hparams = hparams
+        cache_dir = self.hparams.cache_dir if self.hparams.cache_dir else None
+        self.config = AutoConfig.from_pretrained(
+            self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,
+            **({"num_labels": num_labels} if num_labels is not None else {}),
+            cache_dir=cache_dir,
+            **config_kwargs,
+        )
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,
+            cache_dir=cache_dir,
+        )
+        self.model = MODEL_MODES[mode].from_pretrained(
+            self.hparams.model_name_or_path,
+            from_tf=bool(".ckpt" in self.hparams.model_name_or_path),
+            config=self.config,
+            cache_dir=cache_dir,
+        )
+
+    def is_logger(self):
+        return self.trainer.proc_rank <= 0
 
     def configure_optimizers(self):
         "Prepare optimizer and schedule (linear warmup and decay)"
+
         model = self.model
         no_decay = ["bias", "LayerNorm.weight"]
         optimizer_grouped_parameters = [
@@ -132,7 +104,7 @@ class BaseTransformer(pl.LightningModule):
     def test_step(self, batch, batch_nb):
         return self.validation_step(batch, batch_nb)
 
-    def test_epoch_end(self, outputs):
+    def test_end(self, outputs):
         return self.validation_end(outputs)
 
     def train_dataloader(self):
@@ -166,15 +138,6 @@ class BaseTransformer(pl.LightningModule):
             ),
         )
 
-    @pl.utilities.rank_zero_only
-    def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
-        save_path = self.output_dir.joinpath("best_tfmr")
-        save_path.mkdir(exist_ok=True)
-        self.model.config.save_step = self.step_count
-        self.model.save_pretrained(save_path)
-        self.tokenizer.save_pretrained(save_path)
-        self.tfmr_ckpts[self.step_count] = save_path
-
     @staticmethod
     def add_model_specific_args(parser, root_dir):
         parser.add_argument(
@@ -189,7 +152,7 @@ class BaseTransformer(pl.LightningModule):
         )
         parser.add_argument(
             "--tokenizer_name",
-            default=None,
+            default="",
             type=str,
             help="Pretrained tokenizer name or path if not the same as model_name",
         )
@@ -202,8 +165,7 @@ class BaseTransformer(pl.LightningModule):
         parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
         parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
         parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
-        parser.add_argument("--warmup_steps", default=500, type=int, help="Linear warmup over warmup_steps.")
-        parser.add_argument("--num_workers", default=4, type=int, help="kwarg passed to DataLoader")
+        parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
         parser.add_argument(
             "--num_train_epochs", default=3, type=int, help="Total number of training epochs to perform."
         )
@@ -213,30 +175,31 @@ class BaseTransformer(pl.LightningModule):
 
 
 class LoggingCallback(pl.Callback):
-    @rank_zero_only
     def on_validation_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
-        rank_zero_info("***** Validation results *****")
-        metrics = trainer.callback_metrics
-        # Log results
-        for key in sorted(metrics):
-            if key not in ["log", "progress_bar"]:
-                rank_zero_info("{} = {}\n".format(key, str(metrics[key])))
-
-    @rank_zero_only
-    def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
-        logger.info("***** Test results *****")
-        metrics = trainer.callback_metrics
-        # Log and save results to file
-        output_test_results_file = os.path.join(pl_module.hparams.output_dir, "test_results.txt")
-        with open(output_test_results_file, "w") as writer:
+        logger.info("***** Validation results *****")
+        if pl_module.is_logger():
+            metrics = trainer.callback_metrics
+            # Log results
             for key in sorted(metrics):
                 if key not in ["log", "progress_bar"]:
                     logger.info("{} = {}\n".format(key, str(metrics[key])))
-                    writer.write("{} = {}\n".format(key, str(metrics[key])))
+
+    def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
+        logger.info("***** Test results *****")
+
+        if pl_module.is_logger():
+            metrics = trainer.callback_metrics
+
+            # Log and save results to file
+            output_test_results_file = os.path.join(pl_module.hparams.output_dir, "test_results.txt")
+            with open(output_test_results_file, "w") as writer:
+                for key in sorted(metrics):
+                    if key not in ["log", "progress_bar"]:
+                        logger.info("{} = {}\n".format(key, str(metrics[key])))
+                        writer.write("{} = {}\n".format(key, str(metrics[key])))
 
 
-def add_generic_args(parser, root_dir) -> None:
-    #  TODO(SS): allow all pl args? parser = pl.Trainer.add_argparse_args(parser)
+def add_generic_args(parser, root_dir):
     parser.add_argument(
         "--output_dir",
         default=None,
@@ -258,8 +221,8 @@ def add_generic_args(parser, root_dir) -> None:
         help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
         "See details at https://nvidia.github.io/apex/amp.html",
     )
-    parser.add_argument("--fast_dev_run", action="store_true")
-    parser.add_argument("--gpus", type=int, default=1)
+
+    parser.add_argument("--n_gpu", type=int, default=1)
     parser.add_argument("--n_tpu_cores", type=int, default=0)
     parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
     parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
@@ -272,32 +235,28 @@ def add_generic_args(parser, root_dir) -> None:
     )
 
     parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
-    parser.add_argument("--resume_from_checkpoint", type=str, default=None)
-    parser.add_argument("--val_check_interval", default=1.0, type=float)
 
 
-def generic_train(
-    model: BaseTransformer,
-    args: argparse.Namespace,
-    early_stopping_callback=False,
-    logger=True,  # can pass WandbLogger() here
-    extra_callbacks=[],
-    checkpoint_callback=None,
-    logging_callback=None,
-    **extra_train_kwargs
-):
+def generic_train(model: BaseTransformer, args: argparse.Namespace):
     # init model
     set_seed(args)
-    odir = Path(model.hparams.output_dir)
-    odir.mkdir(exist_ok=True)
-    if checkpoint_callback is None:
-        checkpoint_callback = pl.callbacks.ModelCheckpoint(
-            filepath=args.output_dir, prefix="checkpoint", monitor="val_loss", mode="min", save_top_k=1
-        )
-    if logging_callback is None:
-        logging_callback = LoggingCallback()
 
-    train_params = {}
+    if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
+        raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
+
+    checkpoint_callback = pl.callbacks.ModelCheckpoint(
+        filepath=args.output_dir, prefix="checkpoint", monitor="val_loss", mode="min", save_top_k=5
+    )
+
+    train_params = dict(
+        accumulate_grad_batches=args.gradient_accumulation_steps,
+        gpus=args.n_gpu,
+        max_epochs=args.num_train_epochs,
+        early_stop_callback=False,
+        gradient_clip_val=args.max_grad_norm,
+        checkpoint_callback=checkpoint_callback,
+        callbacks=[LoggingCallback()],
+    )
 
     if args.fp16:
         train_params["use_amp"] = args.fp16
@@ -310,27 +269,12 @@ def generic_train(
         train_params["num_tpu_cores"] = args.n_tpu_cores
         train_params["gpus"] = 0
 
-    if args.gpus > 1:
+    if args.n_gpu > 1:
         train_params["distributed_backend"] = "ddp"
 
-    trainer = pl.Trainer(
-        logger=logger,
-        accumulate_grad_batches=args.gradient_accumulation_steps,
-        gpus=args.gpus,
-        max_epochs=args.num_train_epochs,
-        early_stop_callback=early_stopping_callback,
-        gradient_clip_val=args.max_grad_norm,
-        checkpoint_callback=checkpoint_callback,
-        callbacks=[logging_callback] + extra_callbacks,
-        fast_dev_run=args.fast_dev_run,
-        val_check_interval=args.val_check_interval,
-        weights_summary=None,
-        resume_from_checkpoint=args.resume_from_checkpoint,
-        **train_params,
-    )
+    trainer = pl.Trainer(**train_params)
 
     if args.do_train:
         trainer.fit(model)
-    trainer.logger.log_hyperparams(args)
-    trainer.logger.save()
+
     return trainer

examples/longform-qa/README.md

@@ -1,5 +0,0 @@
-# Long Form Question Answering
-
-This folder contains the code for the Long Form Question answering [demo](http://35.226.96.115:8080/) as well as methods to train and use a fully end-to-end Long Form Question Answering system using the [🤗transformers](https://github.com/huggingface/transformers) and [🤗nlp](https://github.com/huggingface/nlp) libraries.
-
-You can use these methods to train your own system by following along the associate [notebook](https://github.com/huggingface/notebooks/blob/master/longform-qa/Long_Form_Question_Answering_with_ELI5_and_Wikipedia.ipynb) or [blog post](https://yjernite.github.io/lfqa.html).

examples/longform-qa/eli5_app.py

@@ -1,332 +0,0 @@
-import faiss
-import nlp
-import numpy as np
-import torch
-from elasticsearch import Elasticsearch
-
-import streamlit as st
-import transformers
-from eli5_utils import (
-    embed_questions_for_retrieval,
-    make_qa_s2s_model,
-    qa_s2s_generate,
-    query_es_index,
-    query_qa_dense_index,
-)
-from transformers import AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer
-
-
-MODEL_TYPE = "bart"
-LOAD_DENSE_INDEX = True
-
-
-@st.cache(allow_output_mutation=True)
-def load_models():
-    if LOAD_DENSE_INDEX:
-        qar_tokenizer = AutoTokenizer.from_pretrained("yjernite/retribert-base-uncased")
-        qar_model = AutoModel.from_pretrained("yjernite/retribert-base-uncased").to("cuda:0")
-        _ = qar_model.eval()
-    else:
-        qar_tokenizer, qar_model = (None, None)
-    if MODEL_TYPE == "bart":
-        s2s_tokenizer = AutoTokenizer.from_pretrained("yjernite/bart_eli5")
-        s2s_model = AutoModelForSeq2SeqLM.from_pretrained("yjernite/bart_eli5").to("cuda:0")
-        save_dict = torch.load("seq2seq_models/eli5_bart_model_blm_2.pth")
-        s2s_model.load_state_dict(save_dict["model"])
-        _ = s2s_model.eval()
-    else:
-        s2s_tokenizer, s2s_model = make_qa_s2s_model(
-            model_name="t5-small", from_file="seq2seq_models/eli5_t5_model_1024_4.pth", device="cuda:0"
-        )
-    return (qar_tokenizer, qar_model, s2s_tokenizer, s2s_model)
-
-
-@st.cache(allow_output_mutation=True)
-def load_indexes():
-    if LOAD_DENSE_INDEX:
-        faiss_res = faiss.StandardGpuResources()
-        wiki40b_passages = nlp.load_dataset(path="wiki_snippets", name="wiki40b_en_100_0")["train"]
-        wiki40b_passage_reps = np.memmap(
-            "wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat",
-            dtype="float32",
-            mode="r",
-            shape=(wiki40b_passages.num_rows, 128),
-        )
-        wiki40b_index_flat = faiss.IndexFlatIP(128)
-        wiki40b_gpu_index_flat = faiss.index_cpu_to_gpu(faiss_res, 1, wiki40b_index_flat)
-        wiki40b_gpu_index_flat.add(wiki40b_passage_reps)  # TODO fix for larger GPU
-    else:
-        wiki40b_passages, wiki40b_gpu_index_flat = (None, None)
-    es_client = Elasticsearch([{"host": "localhost", "port": "9200"}])
-    return (wiki40b_passages, wiki40b_gpu_index_flat, es_client)
-
-
-@st.cache(allow_output_mutation=True)
-def load_train_data():
-    eli5 = nlp.load_dataset("eli5", name="LFQA_reddit")
-    eli5_train = eli5["train_eli5"]
-    eli5_train_q_reps = np.memmap(
-        "eli5_questions_reps.dat", dtype="float32", mode="r", shape=(eli5_train.num_rows, 128)
-    )
-    eli5_train_q_index = faiss.IndexFlatIP(128)
-    eli5_train_q_index.add(eli5_train_q_reps)
-    return (eli5_train, eli5_train_q_index)
-
-
-passages, gpu_dense_index, es_client = load_indexes()
-qar_tokenizer, qar_model, s2s_tokenizer, s2s_model = load_models()
-eli5_train, eli5_train_q_index = load_train_data()
-
-
-def find_nearest_training(question, n_results=10):
-    q_rep = embed_questions_for_retrieval([question], qar_tokenizer, qar_model)
-    D, I = eli5_train_q_index.search(q_rep, n_results)
-    nn_examples = [eli5_train[int(i)] for i in I[0]]
-    return nn_examples
-
-
-def make_support(question, source="wiki40b", method="dense", n_results=10):
-    if source == "none":
-        support_doc, hit_lst = (" <P> ".join(["" for _ in range(11)]).strip(), [])
-    else:
-        if method == "dense":
-            support_doc, hit_lst = query_qa_dense_index(
-                question, qar_model, qar_tokenizer, passages, gpu_dense_index, n_results
-            )
-        else:
-            support_doc, hit_lst = query_es_index(
-                question, es_client, index_name="english_wiki40b_snippets_100w", n_results=n_results,
-            )
-    support_list = [
-        (res["article_title"], res["section_title"].strip(), res["score"], res["passage_text"]) for res in hit_lst
-    ]
-    question_doc = "question: {} context: {}".format(question, support_doc)
-    return question_doc, support_list
-
-
-@st.cache(hash_funcs={torch.Tensor: (lambda _: None), transformers.tokenization_bart.BartTokenizer: (lambda _: None)})
-def answer_question(
-    question_doc, s2s_model, s2s_tokenizer, min_len=64, max_len=256, sampling=False, n_beams=2, top_p=0.95, temp=0.8
-):
-    with torch.no_grad():
-        answer = qa_s2s_generate(
-            question_doc,
-            s2s_model,
-            s2s_tokenizer,
-            num_answers=1,
-            num_beams=n_beams,
-            min_len=min_len,
-            max_len=max_len,
-            do_sample=sampling,
-            temp=temp,
-            top_p=top_p,
-            top_k=None,
-            max_input_length=1024,
-            device="cuda:0",
-        )[0]
-    return (answer, support_list)
-
-
-st.title("Long Form Question Answering with ELI5")
-
-# Start sidebar
-header_html = "<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"
-header_full = """
-<html>
-  <head>
-    <style>
-      .img-container {
-        padding-left: 90px;
-        padding-right: 90px;
-        padding-top: 50px;
-        padding-bottom: 50px;
-        background-color: #f0f3f9;
-      }
-    </style>
-  </head>
-  <body>
-    <span class="img-container"> <!-- Inline parent element -->
-      %s
-    </span>
-  </body>
-</html>
-""" % (
-    header_html,
-)
-st.sidebar.markdown(
-    header_full, unsafe_allow_html=True,
-)
-
-# Long Form QA with ELI5 and Wikipedia
-description = """
-This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
-First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
-a pre-processed fixed snapshot of Wikipedia.
-"""
-st.sidebar.markdown(description, unsafe_allow_html=True)
-
-action_list = [
-    "Answer the question",
-    "View the retrieved document only",
-    "View the most similar ELI5 question and answer",
-    "Show me everything, please!",
-]
-demo_options = st.sidebar.checkbox("Demo options")
-if demo_options:
-    action_st = st.sidebar.selectbox("", action_list, index=3,)
-    action = action_list.index(action_st)
-    show_type = st.sidebar.selectbox("", ["Show full text of passages", "Show passage section titles"], index=0,)
-    show_passages = show_type == "Show full text of passages"
-else:
-    action = 3
-    show_passages = True
-
-retrieval_options = st.sidebar.checkbox("Retrieval options")
-if retrieval_options:
-    retriever_info = """
-    ### Information retriever options
-
-    The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
-    trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
-    The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
-    """
-    st.sidebar.markdown(retriever_info)
-    wiki_source = st.sidebar.selectbox("Which Wikipedia format should the model use?", ["wiki40b", "none"])
-    index_type = st.sidebar.selectbox("Which Wikipedia indexer should the model use?", ["dense", "sparse", "mixed"])
-else:
-    wiki_source = "wiki40b"
-    index_type = "dense"
-
-sampled = "beam"
-n_beams = 2
-min_len = 64
-max_len = 256
-top_p = None
-temp = None
-generate_options = st.sidebar.checkbox("Generation options")
-if generate_options:
-    generate_info = """
-    ### Answer generation options
-
-    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
-    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
-    **beam** search, or **sample** from the decoder's output probabilities.
-    """
-    st.sidebar.markdown(generate_info)
-    sampled = st.sidebar.selectbox("Would you like to use beam search or sample an answer?", ["beam", "sampled"])
-    min_len = st.sidebar.slider(
-        "Minimum generation length", min_value=8, max_value=256, value=64, step=8, format=None, key=None
-    )
-    max_len = st.sidebar.slider(
-        "Maximum generation length", min_value=64, max_value=512, value=256, step=16, format=None, key=None
-    )
-    if sampled == "beam":
-        n_beams = st.sidebar.slider("Beam size", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
-    else:
-        top_p = st.sidebar.slider(
-            "Nucleus sampling p", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
-        )
-        temp = st.sidebar.slider(
-            "Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
-        )
-        n_beams = None
-
-# start main text
-questions_list = [
-    "<MY QUESTION>",
-    "How do people make chocolate?",
-    "Why do we get a fever when we are sick?",
-    "How can different animals perceive different colors?",
-    "What is natural language processing?",
-    "What's the best way to treat a sunburn?",
-    "What exactly are vitamins ?",
-    "How does nuclear energy provide electricity?",
-    "What's the difference between viruses and bacteria?",
-    "Why are flutes classified as woodwinds when most of them are made out of metal ?",
-    "Why do people like drinking coffee even though it tastes so bad?",
-    "What happens when wine ages? How does it make the wine taste better?",
-    "If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?",
-    "How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?",
-    "How does New Zealand have so many large bird predators?",
-]
-question_s = st.selectbox(
-    "What would you like to ask? ---- select <MY QUESTION> to enter a new query", questions_list, index=1,
-)
-if question_s == "<MY QUESTION>":
-    question = st.text_input("Enter your question here:", "")
-else:
-    question = question_s
-
-if st.button("Show me!"):
-    if action in [0, 1, 3]:
-        if index_type == "mixed":
-            _, support_list_dense = make_support(question, source=wiki_source, method="dense", n_results=10)
-            _, support_list_sparse = make_support(question, source=wiki_source, method="sparse", n_results=10)
-            support_list = []
-            for res_d, res_s in zip(support_list_dense, support_list_sparse):
-                if tuple(res_d) not in support_list:
-                    support_list += [tuple(res_d)]
-                if tuple(res_s) not in support_list:
-                    support_list += [tuple(res_s)]
-            support_list = support_list[:10]
-            question_doc = "<P> " + " <P> ".join([res[-1] for res in support_list])
-        else:
-            question_doc, support_list = make_support(question, source=wiki_source, method=index_type, n_results=10)
-    if action in [0, 3]:
-        answer, support_list = answer_question(
-            question_doc,
-            s2s_model,
-            s2s_tokenizer,
-            min_len=min_len,
-            max_len=int(max_len),
-            sampling=(sampled == "sampled"),
-            n_beams=n_beams,
-            top_p=top_p,
-            temp=temp,
-        )
-        st.markdown("### The model generated answer is:")
-        st.write(answer)
-    if action in [0, 1, 3] and wiki_source != "none":
-        st.markdown("--- \n ### The model is drawing information from the following Wikipedia passages:")
-        for i, res in enumerate(support_list):
-            wiki_url = "https://en.wikipedia.org/wiki/{}".format(res[0].replace(" ", "_"))
-            sec_titles = res[1].strip()
-            if sec_titles == "":
-                sections = "[{}]({})".format(res[0], wiki_url)
-            else:
-                sec_list = sec_titles.split(" & ")
-                sections = " & ".join(
-                    ["[{}]({}#{})".format(sec.strip(), wiki_url, sec.strip().replace(" ", "_")) for sec in sec_list]
-                )
-            st.markdown(
-                "{0:02d} - **Article**: {1:<18} <br>  _Section_: {2}".format(i + 1, res[0], sections),
-                unsafe_allow_html=True,
-            )
-            if show_passages:
-                st.write(
-                    '> <span style="font-family:arial; font-size:10pt;">' + res[-1] + "</span>", unsafe_allow_html=True
-                )
-    if action in [2, 3]:
-        nn_train_list = find_nearest_training(question)
-        train_exple = nn_train_list[0]
-        st.markdown(
-            "--- \n ### The most similar question in the ELI5 training set was: \n\n {}".format(train_exple["title"])
-        )
-        answers_st = [
-            "{}. {}".format(i + 1, "  \n".join([line.strip() for line in ans.split("\n") if line.strip() != ""]))
-            for i, (ans, sc) in enumerate(zip(train_exple["answers"]["text"], train_exple["answers"]["score"]))
-            if i == 0 or sc > 2
-        ]
-        st.markdown("##### Its answers were: \n\n {}".format("\n".join(answers_st)))
-
-
-disclaimer = """
----
-
-**Disclaimer**
-
-*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
-Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
-Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
-"""
-st.sidebar.markdown(disclaimer, unsafe_allow_html=True)

examples/longform-qa/eli5_utils.py

@@ -1,653 +0,0 @@
-import functools
-import math
-import os  # noqa: F401
-from random import choice, randint
-from time import time
-
-import faiss  # noqa: F401
-import nlp  # noqa: F401
-import numpy as np
-import pandas as pd
-import torch
-import torch.utils.checkpoint as checkpoint
-from elasticsearch import Elasticsearch  # noqa: F401
-from elasticsearch.helpers import bulk, streaming_bulk  # noqa: F401
-from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
-from tqdm import tqdm
-
-from transformers import AdamW, AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer, get_linear_schedule_with_warmup
-
-
-pd.set_option("display.max_colwidth", None)
-
-
-###############
-# Sparse index
-###############
-def make_es_index_snippets(es_client, passages_dset, index_name="english_wiki_kilt_snippets_100w"):
-    index_config = {
-        "settings": {
-            "number_of_shards": 1,
-            "analysis": {"analyzer": {"stop_standard": {"type": "standard", " stopwords": "_english_"}}},
-        },
-        "mappings": {
-            "properties": {
-                "article_title": {"type": "text", "analyzer": "standard", "similarity": "BM25"},
-                "section_title": {"type": "text", "analyzer": "standard", "similarity": "BM25"},
-                "passage_text": {"type": "text", "analyzer": "standard", "similarity": "BM25"},
-            }
-        },
-    }
-    es_client.indices.create(index=index_name, body=index_config)
-    number_of_docs = passages_dset.num_rows
-    progress = tqdm(unit="docs", total=number_of_docs)
-    successes = 0
-
-    def passage_generator():
-        for passage in passages_dset:
-            yield passage
-
-    # create the ES index
-    for ok, action in streaming_bulk(client=es_client, index=index_name, actions=passage_generator(),):
-        progress.update(1)
-        successes += ok
-    print("Indexed %d documents" % (successes,))
-
-
-def query_es_index(question, es_client, index_name="english_wiki_kilt_snippets_100w", n_results=10, min_length=20):
-    q = question.lower()
-    banned = ["how", "why", "what", "where", "which", "do", "does", "is", "?", "eli5", "eli5:"]
-    q = " ".join([w for w in q.split() if w not in banned])
-    response = es_client.search(
-        index=index_name,
-        body={
-            "query": {
-                "multi_match": {
-                    "query": q,
-                    "fields": ["article_title", "section_title", "passage_text^2"],
-                    "type": "cross_fields",
-                }
-            },
-            "size": 2 * n_results,
-        },
-    )
-    hits = response["hits"]["hits"]
-    support_doc = "<P> " + " <P> ".join([hit["_source"]["passage_text"] for hit in hits])
-    res_list = [dict([(k, hit["_source"][k]) for k in hit["_source"] if k != "passage_text"]) for hit in hits]
-    for r, hit in zip(res_list, hits):
-        r["passage_id"] = hit["_id"]
-        r["score"] = hit["_score"]
-        r["passage_text"] = hit["_source"]["passage_text"]
-    res_list = [res for res in res_list if len(res["passage_text"].split()) > min_length][:n_results]
-    return support_doc, res_list
-
-
-###############
-# ELI5 retriever training
-###############
-class ELI5DatasetQARetriver(Dataset):
-    def __init__(self, examples_array, extra_answer_threshold=3, min_answer_length=64, training=True, n_samples=None):
-        self.data = examples_array
-        self.answer_thres = extra_answer_threshold
-        self.min_length = min_answer_length
-        self.training = training
-        self.n_samples = self.data.num_rows if n_samples is None else n_samples
-
-    def __len__(self):
-        return self.n_samples
-
-    def make_example(self, idx):
-        example = self.data[idx]
-        question = example["title"]
-        if self.training:
-            answers = [a for i, (a, sc) in enumerate(zip(example["answers"]["text"], example["answers"]["score"]))]
-            answer_tab = choice(answers).split(" ")
-            start_idx = randint(0, max(0, len(answer_tab) - self.min_length))
-            answer_span = " ".join(answer_tab[start_idx:])
-        else:
-            answer_span = example["answers"]["text"][0]
-        return (question, answer_span)
-
-    def __getitem__(self, idx):
-        return self.make_example(idx % self.data.num_rows)
-
-
-class RetrievalQAEmbedder(torch.nn.Module):
-    def __init__(self, sent_encoder, dim):
-        super(RetrievalQAEmbedder, self).__init__()
-        self.sent_encoder = sent_encoder
-        self.output_dim = 128
-        self.project_q = torch.nn.Linear(dim, self.output_dim, bias=False)
-        self.project_a = torch.nn.Linear(dim, self.output_dim, bias=False)
-        self.ce_loss = torch.nn.CrossEntropyLoss(reduction="mean")
-
-    def embed_sentences_checkpointed(self, input_ids, attention_mask, checkpoint_batch_size=-1):
-        # reproduces BERT forward pass with checkpointing
-        if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
-            return self.sent_encoder(input_ids, attention_mask=attention_mask)[1]
-        else:
-            # prepare implicit variables
-            device = input_ids.device
-            input_shape = input_ids.size()
-            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
-            head_mask = [None] * self.sent_encoder.config.num_hidden_layers
-            extended_attention_mask: torch.Tensor = self.sent_encoder.get_extended_attention_mask(
-                attention_mask, input_shape, device
-            )
-
-            # define function for checkpointing
-            def partial_encode(*inputs):
-                encoder_outputs = self.sent_encoder.encoder(inputs[0], attention_mask=inputs[1], head_mask=head_mask,)
-                sequence_output = encoder_outputs[0]
-                pooled_output = self.sent_encoder.pooler(sequence_output)
-                return pooled_output
-
-            # run embedding layer on everything at once
-            embedding_output = self.sent_encoder.embeddings(
-                input_ids=input_ids, position_ids=None, token_type_ids=token_type_ids, inputs_embeds=None
-            )
-            # run encoding and pooling on one mini-batch at a time
-            pooled_output_list = []
-            for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)):
-                b_embedding_output = embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
-                b_attention_mask = extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
-                pooled_output = checkpoint.checkpoint(partial_encode, b_embedding_output, b_attention_mask)
-                pooled_output_list.append(pooled_output)
-            return torch.cat(pooled_output_list, dim=0)
-
-    def embed_questions(self, q_ids, q_mask, checkpoint_batch_size=-1):
-        q_reps = self.embed_sentences_checkpointed(q_ids, q_mask, checkpoint_batch_size)
-        return self.project_q(q_reps)
-
-    def embed_answers(self, a_ids, a_mask, checkpoint_batch_size=-1):
-        a_reps = self.embed_sentences_checkpointed(a_ids, a_mask, checkpoint_batch_size)
-        return self.project_a(a_reps)
-
-    def forward(self, q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=-1):
-        device = q_ids.device
-        q_reps = self.embed_questions(q_ids, q_mask, checkpoint_batch_size)
-        a_reps = self.embed_answers(a_ids, a_mask, checkpoint_batch_size)
-        compare_scores = torch.mm(q_reps, a_reps.t())
-        loss_qa = self.ce_loss(compare_scores, torch.arange(compare_scores.shape[1]).to(device))
-        loss_aq = self.ce_loss(compare_scores.t(), torch.arange(compare_scores.shape[0]).to(device))
-        loss = (loss_qa + loss_aq) / 2
-        return loss
-
-
-def make_qa_retriever_model(model_name="google/bert_uncased_L-8_H-512_A-8", from_file=None, device="cuda:0"):
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    bert_model = AutoModel.from_pretrained(model_name).to(device)
-    # run bert_model on a dummy batch to get output dimension
-    d_ids = torch.LongTensor(
-        [[bert_model.config.bos_token_id if bert_model.config.bos_token_id is not None else 1]]
-    ).to(device)
-    d_mask = torch.LongTensor([[1]]).to(device)
-    sent_dim = bert_model(d_ids, attention_mask=d_mask)[1].shape[-1]
-    qa_embedder = RetrievalQAEmbedder(bert_model, sent_dim).to(device)
-    if from_file is not None:
-        param_dict = torch.load(from_file)  # has model weights, optimizer, and scheduler states
-        qa_embedder.load_state_dict(param_dict["model"])
-    return tokenizer, qa_embedder
-
-
-def make_qa_retriever_batch(qa_list, tokenizer, max_len=64, device="cuda:0"):
-    q_ls = [q for q, a in qa_list]
-    a_ls = [a for q, a in qa_list]
-    q_toks = tokenizer.batch_encode_plus(q_ls, max_length=max_len, pad_to_max_length=True)
-    q_ids, q_mask = (
-        torch.LongTensor(q_toks["input_ids"]).to(device),
-        torch.LongTensor(q_toks["attention_mask"]).to(device),
-    )
-    a_toks = tokenizer.batch_encode_plus(a_ls, max_length=max_len, pad_to_max_length=True)
-    a_ids, a_mask = (
-        torch.LongTensor(a_toks["input_ids"]).to(device),
-        torch.LongTensor(a_toks["attention_mask"]).to(device),
-    )
-    return (q_ids, q_mask, a_ids, a_mask)
-
-
-def train_qa_retriever_epoch(model, dataset, tokenizer, optimizer, scheduler, args, e=0):
-    model.train()
-    # make iterator
-    train_sampler = RandomSampler(dataset)
-    model_collate_fn = functools.partial(
-        make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
-    )
-    data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn)
-    epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True)
-    # accumulate loss since last print
-    loc_steps = 0
-    loc_loss = 0.0
-    st_time = time()
-    for step, batch in enumerate(epoch_iterator):
-        q_ids, q_mask, a_ids, a_mask = batch
-        pre_loss = model(q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=args.checkpoint_batch_size)
-        loss = pre_loss.sum()
-        # optimizer
-        loss.backward()
-        optimizer.step()
-        scheduler.step()
-        model.zero_grad()
-        # some printing within the epoch
-        loc_loss += loss.item()
-        loc_steps += 1
-        if step % args.print_freq == 0 or step == 1:
-            print(
-                "{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format(
-                    e, step, len(dataset) // args.batch_size, loc_loss / loc_steps, time() - st_time,
-                )
-            )
-            loc_loss = 0
-            loc_steps = 0
-
-
-def train_qa_retriever_joint_epoch(model, dataset_list, tokenizer, optimizer, scheduler, args, e=0):
-    model.train()
-    model_collate_fn = functools.partial(
-        make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
-    )
-    # make iterator
-    train_samplers = [RandomSampler(dataset) for dataset in dataset_list]
-    data_loaders = [
-        DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn)
-        for dataset, train_sampler in zip(dataset_list, train_samplers)
-    ]
-    iterators = [iter(dloader) for dloader in data_loaders]
-    joint_iter = zip(*iterators)
-    # accumulate loss since last print
-    loc_steps = 0
-    loc_loss = 0.0
-    st_time = time()
-    for step, (batches,) in enumerate(zip(joint_iter)):
-        for batch in batches:
-            q_ids, q_mask, a_ids, a_mask = batch
-            loss = model(q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=args.checkpoint_batch_size)
-            # optimizer
-            loss.backward()
-            optimizer.step()
-            scheduler.step()
-            model.zero_grad()
-            # some printing within the epoch
-            loc_loss += loss.item()
-            loc_steps += 1
-        if step % args.print_freq == 0:
-            print(
-                "{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format(
-                    e, step, len(dataset_list[0]) // args.batch_size, loc_loss / loc_steps, time() - st_time,
-                )
-            )
-            loc_loss = 0
-            loc_steps = 0
-
-
-def evaluate_qa_retriever(model, dataset, tokenizer, args):
-    model.eval()
-    # make iterator
-    eval_sampler = SequentialSampler(dataset)
-    model_collate_fn = functools.partial(
-        make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
-    )
-    data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=eval_sampler, collate_fn=model_collate_fn)
-    epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True)
-    tot_loss = 0.0
-    with torch.no_grad():
-        for step, batch in enumerate(epoch_iterator):
-            q_ids, q_mask, a_ids, a_mask = batch
-            loss = model(q_ids, q_mask, a_ids, a_mask)
-            tot_loss += loss.item()
-        return tot_loss / (step + 1)
-
-
-def train_qa_retriever(qar_model, qar_tokenizer, qar_train_dset, qar_valid_dset, qar_args):
-    qar_optimizer = AdamW(qar_model.parameters(), lr=qar_args.learning_rate, eps=1e-8)
-    qar_scheduler = get_linear_schedule_with_warmup(
-        qar_optimizer,
-        num_warmup_steps=100,
-        num_training_steps=(qar_args.num_epochs + 1) * math.ceil(len(qar_train_dset) / qar_args.batch_size),
-    )
-    for e in range(qar_args.num_epochs):
-        train_qa_retriever_epoch(qar_model, qar_train_dset, qar_tokenizer, qar_optimizer, qar_scheduler, qar_args, e)
-        m_save_dict = {
-            "model": qar_model.state_dict(),
-            "optimizer": qar_optimizer.state_dict(),
-            "scheduler": qar_scheduler.state_dict(),
-        }
-        print("Saving model {}".format(qar_args.model_save_name))
-        torch.save(m_save_dict, "{}_{}.pth".format(qar_args.model_save_name, e))
-        eval_loss = evaluate_qa_retriever(qar_model, qar_valid_dset, qar_tokenizer, qar_args)
-        print("Evaluation loss epoch {:4d}: {:.3f}".format(e, eval_loss))
-
-
-###############
-# ELI5 seq2seq model training
-###############
-class ELI5DatasetS2S(Dataset):
-    def __init__(
-        self, examples_array, make_doc_fun=None, extra_answer_threshold=3, document_cache=None, training=True
-    ):
-        self.training = training
-        self.data = examples_array
-        self.make_doc_function = make_doc_fun
-        self.document_cache = {} if document_cache is None else document_cache
-        assert not (make_doc_fun is None and document_cache is None)
-        # make index of specific question-answer pairs from multi-answers
-        if self.training:
-            self.qa_id_list = [
-                (i, j)
-                for i, qa in enumerate(self.data)
-                for j, (a, sc) in enumerate(zip(qa["answers"]["text"], qa["answers"]["score"]))
-                if j == 0 or sc >= extra_answer_threshold
-            ]
-        else:
-            self.qa_id_list = [(i, 0) for i in range(self.data.num_rows)]
-
-    def __len__(self):
-        return len(self.qa_id_list)
-
-    def make_example(self, idx):
-        i, j = self.qa_id_list[idx]
-        example = self.data[i]
-        question = example["title"] + " " + example["selftext"]
-        answer = example["answers"]["text"][j]
-        q_id = example["q_id"]
-        if self.make_doc_function is not None:
-            self.document_cache[q_id] = self.document_cache.get(q_id, self.make_doc_function(example["title"]))
-        document = self.document_cache[q_id]
-        in_st = "question: {} context: {}".format(
-            question.lower().replace(" --t--", "").strip(), document.lower().strip(),
-        )
-        out_st = answer
-        return (in_st, out_st)
-
-    def __getitem__(self, idx):
-        return self.make_example(idx)
-
-
-def make_qa_s2s_model(model_name="facebook/bart-large", from_file=None, device="cuda:0"):
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
-    if from_file is not None:
-        param_dict = torch.load(from_file)  # has model weights, optimizer, and scheduler states
-        model.load_state_dict(param_dict["model"])
-    return tokenizer, model
-
-
-def make_qa_s2s_batch(qa_list, tokenizer, max_len=64, max_a_len=360, device="cuda:0"):
-    q_ls = [q for q, a in qa_list]
-    a_ls = [a for q, a in qa_list]
-    q_toks = tokenizer.batch_encode_plus(q_ls, max_length=max_len, pad_to_max_length=True)
-    q_ids, q_mask = (
-        torch.LongTensor(q_toks["input_ids"]).to(device),
-        torch.LongTensor(q_toks["attention_mask"]).to(device),
-    )
-    a_toks = tokenizer.batch_encode_plus(a_ls, max_length=min(max_len, max_a_len), pad_to_max_length=True)
-    a_ids, a_mask = (
-        torch.LongTensor(a_toks["input_ids"]).to(device),
-        torch.LongTensor(a_toks["attention_mask"]).to(device),
-    )
-    lm_labels = a_ids[:, 1:].contiguous().clone()
-    lm_labels[a_mask[:, 1:].contiguous() == 0] = -100
-    model_inputs = {
-        "input_ids": q_ids,
-        "attention_mask": q_mask,
-        "decoder_input_ids": a_ids[:, :-1].contiguous(),
-        "lm_labels": lm_labels,
-    }
-    return model_inputs
-
-
-def train_qa_s2s_epoch(model, dataset, tokenizer, optimizer, scheduler, args, e=0, curriculum=False):
-    model.train()
-    # make iterator
-    if curriculum:
-        train_sampler = SequentialSampler(dataset)
-    else:
-        train_sampler = RandomSampler(dataset)
-    model_collate_fn = functools.partial(
-        make_qa_s2s_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
-    )
-    data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn)
-    epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True)
-    # accumulate loss since last print
-    loc_steps = 0
-    loc_loss = 0.0
-    st_time = time()
-    for step, batch_inputs in enumerate(epoch_iterator):
-        pre_loss = model(**batch_inputs)[0]
-        loss = pre_loss.sum() / pre_loss.shape[0]
-        loss.backward()
-        # optimizer
-        if step % args.backward_freq == 0:
-            optimizer.step()
-            scheduler.step()
-            model.zero_grad()
-        # some printing within the epoch
-        loc_loss += loss.item()
-        loc_steps += 1
-        if step % args.print_freq == 0 or step == 1:
-            print(
-                "{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format(
-                    e, step, len(dataset) // args.batch_size, loc_loss / loc_steps, time() - st_time,
-                )
-            )
-            loc_loss = 0
-            loc_steps = 0
-
-
-def eval_qa_s2s_epoch(model, dataset, tokenizer, args):
-    model.eval()
-    # make iterator
-    train_sampler = SequentialSampler(dataset)
-    model_collate_fn = functools.partial(
-        make_qa_s2s_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
-    )
-    data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn)
-    epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True)
-    # accumulate loss since last print
-    loc_steps = 0
-    loc_loss = 0.0
-    st_time = time()
-    with torch.no_grad():
-        for step, batch_inputs in enumerate(epoch_iterator):
-            pre_loss = model(**batch_inputs)[0]
-            loss = pre_loss.sum() / pre_loss.shape[0]
-            loc_loss += loss.item()
-            loc_steps += 1
-            if step % args.print_freq == 0:
-                print(
-                    "{:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format(
-                        step, len(dataset) // args.batch_size, loc_loss / loc_steps, time() - st_time,
-                    )
-                )
-    print("Total \t L: {:.3f} \t -- {:.3f}".format(loc_loss / loc_steps, time() - st_time,))
-
-
-def train_qa_s2s(qa_s2s_model, qa_s2s_tokenizer, s2s_train_dset, s2s_valid_dset, s2s_args):
-    s2s_optimizer = AdamW(qa_s2s_model.parameters(), lr=s2s_args.learning_rate, eps=1e-8)
-    s2s_scheduler = get_linear_schedule_with_warmup(
-        s2s_optimizer,
-        num_warmup_steps=400,
-        num_training_steps=(s2s_args.num_epochs + 1) * math.ceil(len(s2s_train_dset) / s2s_args.batch_size),
-    )
-    for e in range(s2s_args.num_epochs):
-        train_qa_s2s_epoch(
-            qa_s2s_model,
-            s2s_train_dset,
-            qa_s2s_tokenizer,
-            s2s_optimizer,
-            s2s_scheduler,
-            s2s_args,
-            e,
-            curriculum=(e == 0),
-        )
-        m_save_dict = {
-            "model": qa_s2s_model.state_dict(),
-            "optimizer": s2s_optimizer.state_dict(),
-            "scheduler": s2s_scheduler.state_dict(),
-        }
-        print("Saving model {}".format(s2s_args.model_save_name))
-        eval_qa_s2s_epoch(qa_s2s_model, s2s_valid_dset, qa_s2s_tokenizer, s2s_args)
-        torch.save(m_save_dict, "{}_{}.pth".format(s2s_args.model_save_name, e))
-
-
-# generate answer from input "question: ... context: <p> ..."
-def qa_s2s_generate(
-    question_doc,
-    qa_s2s_model,
-    qa_s2s_tokenizer,
-    num_answers=1,
-    num_beams=None,
-    min_len=64,
-    max_len=256,
-    do_sample=False,
-    temp=1.0,
-    top_p=None,
-    top_k=None,
-    max_input_length=512,
-    device="cuda:0",
-):
-    model_inputs = make_qa_s2s_batch([(question_doc, "A")], qa_s2s_tokenizer, max_input_length, device=device,)
-    n_beams = num_answers if num_beams is None else max(num_beams, num_answers)
-    generated_ids = qa_s2s_model.generate(
-        input_ids=model_inputs["input_ids"],
-        attention_mask=model_inputs["attention_mask"],
-        min_length=min_len,
-        max_length=max_len,
-        do_sample=do_sample,
-        early_stopping=True,
-        num_beams=1 if do_sample else n_beams,
-        temperature=temp,
-        top_k=top_k,
-        top_p=top_p,
-        eos_token_id=qa_s2s_tokenizer.eos_token_id,
-        no_repeat_ngram_size=3,
-        num_return_sequences=num_answers,
-        decoder_start_token_id=qa_s2s_tokenizer.bos_token_id,
-    )
-    return [qa_s2s_tokenizer.decode(ans_ids, skip_special_tokens=True).strip() for ans_ids in generated_ids]
-
-
-###############
-# ELI5-trained retrieval model usage
-###############
-def embed_passages_for_retrieval(passages, tokenizer, qa_embedder, max_length=128, device="cuda:0"):
-    a_toks = tokenizer.batch_encode_plus(passages, max_length=max_length, pad_to_max_length=True)
-    a_ids, a_mask = (
-        torch.LongTensor(a_toks["input_ids"]).to(device),
-        torch.LongTensor(a_toks["attention_mask"]).to(device),
-    )
-    with torch.no_grad():
-        a_reps = qa_embedder.embed_answers(a_ids, a_mask).cpu().type(torch.float)
-    return a_reps.numpy()
-
-
-def embed_questions_for_retrieval(q_ls, tokenizer, qa_embedder, device="cuda:0"):
-    q_toks = tokenizer.batch_encode_plus(q_ls, max_length=128, pad_to_max_length=True)
-    q_ids, q_mask = (
-        torch.LongTensor(q_toks["input_ids"]).to(device),
-        torch.LongTensor(q_toks["attention_mask"]).to(device),
-    )
-    with torch.no_grad():
-        q_reps = qa_embedder.embed_questions(q_ids, q_mask).cpu().type(torch.float)
-    return q_reps.numpy()
-
-
-def make_qa_dense_index(
-    qa_embedder,
-    tokenizer,
-    passages_dset,
-    batch_size=512,
-    max_length=128,
-    index_name="kilt_passages_reps.dat",
-    dtype="float32",
-    device="cuda:0",
-):
-    st_time = time()
-    fp = np.memmap(index_name, dtype=dtype, mode="w+", shape=(passages_dset.num_rows, 128))
-    n_batches = math.ceil(passages_dset.num_rows / batch_size)
-    for i in range(n_batches):
-        passages = [p for p in passages_dset[i * batch_size : (i + 1) * batch_size]["passage_text"]]
-        reps = embed_passages_for_retrieval(passages, tokenizer, qa_embedder, max_length, device)
-        fp[i * batch_size : (i + 1) * batch_size] = reps
-        if i % 50 == 0:
-            print(i, time() - st_time)
-
-
-def evaluate_retriever(qa_list, retriever_func, scoring_func, n_ret=10, verbose=False):
-    total_retriever_time = 0.0
-    total_retriever_score = 0.0
-    st_time = time()
-    for i, (question, answer) in enumerate(qa_list):
-        r_time = time()
-        retrieved_passages = retriever_func(question, n_ret)
-        total_retriever_time += time() - r_time
-        total_retriever_score += scoring_func(retrieved_passages, answer)
-        if verbose and ((i + 1) % 500 == 0 or i <= 1):
-            print(
-                "{:03d}: S-{:.4f} T-{:.4f} | {:.2f}".format(
-                    i + 1, total_retriever_score / (i + 1), total_retriever_time / (i + 1), time() - st_time
-                )
-            )
-    return {"idf_recall": total_retriever_score / (i + 1), "retrieval_time": total_retriever_time / (i + 1)}
-
-
-# build a support document for the question out of Wikipedia snippets
-def query_qa_dense_index(
-    question, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10, min_length=20, device="cuda:0"
-):
-    q_rep = embed_questions_for_retrieval([question], tokenizer, qa_embedder, device=device)
-    D, I = wiki_index.search(q_rep, 2 * n_results)
-    res_passages = [wiki_passages[int(i)] for i in I[0]]
-    support_doc = "<P> " + " <P> ".join([p["passage_text"] for p in res_passages])
-    res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages]
-    res_list = [res for res in res_list if len(res["passage_text"].split()) > min_length][:n_results]
-    for r, sc in zip(res_list, D[0]):
-        r["score"] = float(sc)
-    return support_doc, res_list
-
-
-def batch_query_qa_dense_index(questions, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10):
-    q_rep = embed_questions_for_retrieval(questions, tokenizer, qa_embedder)
-    D, I = wiki_index.search(q_rep, n_results)
-    res_passages_lst = [[wiki_passages[int(i)] for i in i_lst] for i_lst in I]
-    support_doc_lst = [
-        "<P> " + " <P> ".join([p["passage_text"] for p in res_passages]) for res_passages in res_passages_lst
-    ]
-    all_res_lists = []
-    for (res_passages, dl) in zip(res_passages_lst, D):
-        res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages]
-        for r, sc in zip(res_list, dl):
-            r["score"] = float(sc)
-        all_res_lists += [res_list[:]]
-    return support_doc_lst, all_res_lists
-
-
-# find nearest neighbors of an answer or declarative text in Wikipedia snippets
-def query_qa_dense_index_nn(passage, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10, min_length=20):
-    a_rep = embed_passages_for_retrieval([passage], tokenizer, qa_embedder)
-    D, I = wiki_index.search(a_rep, 2 * n_results)
-    res_passages = [wiki_passages[int(i)] for i in I[0]]
-    support_doc = "<P> " + " <P> ".join([p["passage_text"] for p in res_passages])
-    res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages]
-    res_list = [res for res in res_list if len(res["passage_text"].split()) > min_length][:n_results]
-    for r, sc, i in zip(res_list, D[0], I[0]):
-        r["passage_id"] = int(i)
-        r["score"] = float(sc)
-    return support_doc, res_list
-
-
-def batch_query_qa_dense_index_nn(passages, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10):
-    a_reps = embed_passages_for_retrieval(passages, tokenizer, qa_embedder)
-    D, I = wiki_index.search(a_reps, n_results)
-    res_passages_lst = [[wiki_passages[int(i)] for i in i_lst] for i_lst in I]
-    support_doc_lst = [
-        "<P> " + " <P> ".join([p["passage_text"] for p in res_passages]) for res_passages in res_passages_lst
-    ]
-    all_res_lists = []
-    for (res_passages, dl, il) in zip(res_passages_lst, D, I):
-        res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages]
-        for r, sc, i in zip(res_list, dl, il):
-            r["passage_id"] = int(i)
-            r["score"] = float(sc)
-        all_res_lists += [res_list[:]]
-    return support_doc_lst, all_res_lists

examples/movement-pruning/README.md

@@ -140,7 +140,7 @@ Regularization based pruning methods (soft movement pruning and L0 regularizatio
 To obtain the effective sparsity level in the encoder, we simply count the number of activated (non-null) weights:
 
 ```bash
-python examples/movement-pruning/counts_parameters.py \
+python examples/movement-pruning/count_parameters.py \
     --pruning_method sigmoied_threshold \
     --threshold 0.1 \
     --serialization_dir $SERIALIZATION_DIR

examples/question-answering/run_squad.py

@@ -319,6 +319,7 @@ def evaluate(args, model, tokenizer, prefix=""):
             outputs = model(**inputs)
 
         for i, feature_index in enumerate(feature_indices):
+            # TODO: i and feature_index are the same number! Simplify by removing enumerate?
             eval_feature = features[feature_index.item()]
             unique_id = int(eval_feature.unique_id)
 

examples/requirements.txt

@@ -5,11 +5,5 @@ psutil
 sacrebleu
 rouge-score
 tensorflow_datasets
-pytorch-lightning==0.8.1
+pytorch-lightning==0.7.3  # April 10, 2020 release
 matplotlib
-git-python==1.0.3
-faiss
-streamlit
-elasticsearch
-pandas
-nlp

examples/summarization/README.md

@@ -1,70 +1,47 @@
-### Data
+### Get CNN Data
+To be able to reproduce the authors' results on the CNN/Daily Mail dataset you first need to download both CNN and Daily Mail datasets [from Kyunghyun Cho's website](https://cs.nyu.edu/~kcho/DMQA/) (the links next to "Stories") in the same folder. Then uncompress the archives by running:
 
-CNN/DailyMail data
 ```bash
-cd examples/summarization
 wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/cnn_dm.tgz
 tar -xzvf cnn_dm.tgz
-export CNN_DIR=${PWD}/cnn_dm
 ```
 
 this should make a directory called cnn_dm/ with files like `test.source`.
 To use your own data, copy that files format. Each article to be summarized is on its own line.
 
-XSUM Data:
-```bash
-cd examples/summarization
-wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz
-tar -xzvf xsum.tar.gz
-export XSUM_DIR=${PWD}/xsum
-```
-
-
 ### Evaluation
 
 To create summaries for each article in dataset, run:
 ```bash
-python run_eval.py <path_to_test.source> test_generations.txt <model-name>  --score_path rouge_scores.txt
+python evaluate_cnn.py <path_to_test.source> test_generations.txt <model-name>  --score_path rouge_scores.txt
 ```
-The default batch size, 4, fits in 16GB GPU memory, but may need to be adjusted to fit your system.
-
+The default batch size, 8, fits in 16GB GPU memory, but may need to be adjusted to fit your system.
 
 ### Training
-Run/modify `finetune.sh`
+Run/modify `finetune_bart.sh` or `finetune_t5.sh`
 
-The following command should work on a 16GB GPU:
-```bash
-export me=`git config user.name`
-./finetune.sh \
-    --data_dir $XSUM_DIR \
-    --train_batch_size=1 \
-    --eval_batch_size=1 \
-    --output_dir="$me"_xsum_results \
-    --num_train_epochs 1
+### Stanford CoreNLP Setup
 ```
+ptb_tokenize () {
+    cat $1 | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > $2
+}
 
-Tips:
-- 1 epoch at batch size 1 for bart-large takes 24 hours, requires 13GB GPU RAM with fp16 on an NVIDIA-V100. 
-- try `bart-base`, `--freeze_encoder` or `--freeze_embeds` for faster training/larger batch size.  (3hr/epoch with bs=8, see below)
-- `fp16_opt_level=O1` (the default works best).
-- If you are finetuning on your own dataset, start from `bart-large-cnn` if you want long summaries and `bart-large-xsum` if you want short summaries.
-(It rarely makes sense to start from `bart-large` unless you are a researching finetuning methods).
-- In addition to the pytorch-lightning .ckpt checkpoint, a transformers checkpoint will be saved.
-Load it with `BartForConditionalGeneration.from_pretrained(f'{output_dir}/best_tfmr)`.
-- At the moment, `--do_predict` does not work in a multi-gpu setting. You need to use `evaluate_checkpoint` or the `run_eval.py` code.
-- If you want to run experiments on improving the summarization finetuning process, try the XSUM Shared Task (below). It's faster to train than CNNDM because the summaries are shorter.    
-
-### XSUM Shared Task
-Compare XSUM results with others by using `--logger wandb_shared`. This requires `wandb` registration.
-Here is an example command
-```bash
-export me=`git config user.name`
-./finetune.sh \
-    --data_dir $XSUM_DIR \
-    --output_dir "$me"_xsum_frozen_embs \
-    --logger wandb_shared \
-    --train_batch_size 16 --eval_batch_size 16 --freeze_embeds --freeze_encoder \
-    --num_train_epochs 6
+sudo apt install openjdk-8-jre-headless
+sudo apt-get install ant
+wget http://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip
+unzip stanford-corenlp-full-2018-10-05.zip
+cd stanford-corenlp-full-2018-10-05
+export CLASSPATH=stanford-corenlp-3.9.2.jar:stanford-corenlp-3.9.2-models.jar
 ```
+Then run `ptb_tokenize` on `test.target` and your generated hypotheses.
+### Rouge Setup
+Install `files2rouge` following the instructions at [here](https://github.com/pltrdy/files2rouge).
+I also needed to run `sudo apt-get install libxml-parser-perl`
 
-Results can be viewed [here](https://app.wandb.ai/sshleifer/hf_summarization/table?workspace=user-)
+```python
+from files2rouge import files2rouge
+from files2rouge import settings
+files2rouge.run(<path_to_tokenized_hypo>,
+                <path_to_tokenized_target>,
+               saveto='rouge_output.txt')
+```

examples/summarization/callbacks.py

@@ -1,84 +0,0 @@
-import logging
-import os
-from pathlib import Path
-
-import numpy as np
-import pytorch_lightning as pl
-import torch
-from pytorch_lightning.callbacks import ModelCheckpoint
-from pytorch_lightning.utilities import rank_zero_only
-
-
-def count_trainable_parameters(model):
-    model_parameters = filter(lambda p: p.requires_grad, model.parameters())
-    params = sum([np.prod(p.size()) for p in model_parameters])
-    return params
-
-
-logger = logging.getLogger(__name__)
-
-
-class Seq2SeqLoggingCallback(pl.Callback):
-    @rank_zero_only
-    def _write_logs(
-        self, trainer: pl.Trainer, pl_module: pl.LightningModule, type_path: str, save_generations=True
-    ) -> None:
-        logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****")
-        metrics = trainer.callback_metrics
-        trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]})
-        # Log results
-        od = Path(pl_module.hparams.output_dir)
-        if type_path == "test":
-            results_file = od / "test_results.txt"
-            generations_file = od / "test_generations.txt"
-        else:
-            results_file = od / f"{type_path}_results_{trainer.global_step:05d}.txt"
-            generations_file = od / f"{type_path}_generations_{trainer.global_step:05d}.txt"
-
-        with open(results_file, "a+") as writer:
-            for key in sorted(metrics):
-                if key in ["log", "progress_bar", "preds"]:
-                    continue
-                val = metrics[key]
-                if isinstance(val, torch.Tensor):
-                    val = val.item()
-                msg = f"{key}: {val:.6f}\n"
-                writer.write(msg)
-
-        if not save_generations:
-            return
-
-        if "preds" in metrics:
-            content = "\n".join(metrics["preds"])
-            generations_file.open("w+").write(content)
-
-    @rank_zero_only
-    def on_train_start(self, trainer, pl_module):
-        try:
-            npars = pl_module.model.model.num_parameters()
-        except AttributeError:
-            npars = pl_module.model.num_parameters()
-
-        n_trainable_pars = count_trainable_parameters(pl_module)
-        # mp stands for million parameters
-        trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6})
-
-    @rank_zero_only
-    def on_validation_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
-        return self._write_logs(trainer, pl_module, "val")
-
-    @rank_zero_only
-    def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
-        return self._write_logs(trainer, pl_module, "test")
-
-
-def get_rouge2_checkpoint_callback(output_dir):
-    """Saves the best model by validation ROUGE2 score."""
-    checkpoint_callback = ModelCheckpoint(
-        filepath=os.path.join(output_dir, "{val_avg_rouge2:.4f}-{step_count}"),
-        monitor="val_rouge",
-        mode="max",
-        save_top_k=1,
-        period=0,  # maybe save a checkpoint every time val is run, not just end of epoch.
-    )
-    return checkpoint_callback

examples/summarization/distillation.py

@@ -1,449 +0,0 @@
-import argparse
-import gc
-import os
-from pathlib import Path
-from typing import List
-
-import pytorch_lightning as pl
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from lightning_base import generic_train
-from transformers import AdamW, BartConfig, BartForConditionalGeneration, T5Config, T5ForConditionalGeneration
-
-
-try:
-    from .finetune import SummarizationModule
-    from .initialization_utils import init_student, copy_layers
-    from .utils import (
-        use_task_specific_params,
-        SummarizationDataset,
-        pickle_load,
-        freeze_params,
-        assert_all_frozen,
-        any_requires_grad,
-    )
-    from .finetune import main as ft_main
-except ImportError:
-    from finetune import SummarizationModule
-    from finetune import main as ft_main
-    from initialization_utils import init_student, copy_layers
-    from utils import (
-        use_task_specific_params,
-        SummarizationDataset,
-        pickle_load,
-        freeze_params,
-        assert_all_frozen,
-        any_requires_grad,
-    )
-
-
-class SummarizationDistiller(SummarizationModule):
-    loss_names = ["loss", "ce_loss", "mlm_loss", "enc_mse_loss", "hid_loss_enc", "hid_loss_dec"]
-
-    def __init__(self, hparams):
-        assert Path(hparams.data_dir).exists()
-
-        d_layers_to_copy, student, student_cfg, teacher = self.pre_init(hparams)
-
-        super().__init__(hparams, model=student, config=student_cfg)
-        self.teacher = teacher
-        use_task_specific_params(self.teacher, "summarization")
-        freeze_params(self.teacher)
-        self.sanity_check_gradients()
-        self.ce_loss_fct = nn.KLDivLoss(reduction="batchmean")
-        self.temperature = 2.0
-        self.alpha_mlm = hparams.alpha_mlm
-        self.alpha_ce = hparams.alpha_ce
-        self.alpha_hid = hparams.alpha_hid
-        # self.alpha_cos = hparams.alpha_cos
-        self.alpha_encoder_loss = self.hparams.alpha_encoder_loss
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def sanity_check_gradients(self):
-        assert_all_frozen(self.teacher)
-        assert_all_frozen(self.model.model.decoder.embed_tokens)
-        assert_all_frozen(self.model.model.encoder.embed_tokens)
-        if self.different_encoder:
-            assert any_requires_grad(self.model.model.encoder)
-        else:
-            freeze_params(self.model.model.encoder)
-            del self.teacher.model.encoder
-
-    def pre_init(self, hparams):
-        # Dump empty student model at a path, then call from_pretrained on it
-        teacher = BartForConditionalGeneration.from_pretrained(hparams.teacher).eval()
-        student_updates = {
-            "decoder_layers": hparams.student_decoder_layers,
-            "encoder_layers": hparams.student_encoder_layers,
-        }
-        d_layers_to_copy = get_layers_to_copy(student_updates["decoder_layers"], teacher.config.decoder_layers)
-        e_layers_to_copy: List = get_layers_to_copy(student_updates["encoder_layers"], teacher.config.encoder_layers)
-        hparams.d_layer_to_copy = d_layers_to_copy
-        hparams.e_layer_to_copy = e_layers_to_copy
-        kw = teacher.config.to_diff_dict()
-        kw.update(student_updates)
-        # Copy weights
-        student_cfg = BartConfig(**kw)
-        student = BartForConditionalGeneration(student_cfg)
-        student, _ = init_student(student, teacher)
-        self.copy_to_student(d_layers_to_copy, e_layers_to_copy, hparams, student, teacher)
-        Path(hparams.output_dir).mkdir(exist_ok=True)
-        return d_layers_to_copy, student, student_cfg, teacher
-
-    def copy_to_student(self, d_layers_to_copy, e_layers_to_copy, hparams, student, teacher):
-        if teacher.config.model_type == "t5":
-            return self.copy_t5_to_student(d_layers_to_copy, e_layers_to_copy, hparams, student, teacher)
-        self.different_encoder: bool = hparams.student_encoder_layers != teacher.config.encoder_layers
-        self.different_decoder = hparams.student_decoder_layers != teacher.config.decoder_layers
-        if self.different_decoder:
-            copy_layers(teacher.model.decoder.layers, student.model.decoder.layers, d_layers_to_copy)
-        if self.different_encoder:
-            copy_layers(teacher.model.encoder.layers, student.model.encoder.layers, e_layers_to_copy)
-
-    def copy_t5_to_student(self, d_layers_to_copy, e_layers_to_copy, hparams, student, teacher):
-        self.different_encoder: bool = hparams.student_encoder_layers != teacher.config.num_layers
-        self.different_decoder = hparams.student_decoder_layers != teacher.config.num_layers
-        if self.different_decoder:
-            copy_layers(teacher.decoder.block, student.decoder.block, d_layers_to_copy)
-        if self.different_encoder:
-            copy_layers(teacher.encoder.block, student.encoder.block, e_layers_to_copy)
-
-    def get_dataset(self, type_path) -> SummarizationDataset:
-        n_obs = self.n_obs[type_path]
-        dataset = SummarizationDataset(self.tokenizer, type_path=type_path, n_obs=n_obs, **self.dataset_kwargs)
-        return dataset
-
-    def calc_mse_loss(self, teacher_outputs: torch.Tensor, student_outputs: torch.Tensor, mask) -> torch.FloatTensor:
-        if mask is not None:
-            # mask has False at padding_idx
-            sel_mask = mask[:, :, None].expand_as(student_outputs).bool()
-            s_logits_slct = torch.masked_select(student_outputs, sel_mask)
-            t_logits_slct = torch.masked_select(teacher_outputs, sel_mask)
-        else:
-            t_logits_slct = teacher_outputs
-            s_logits_slct = student_outputs
-        return F.mse_loss(s_logits_slct, t_logits_slct)
-
-    def calc_ce_loss(self, mask, s_logits, t_logits):
-        if mask is not None:
-            # mask has False at padding_idx
-            sel_mask = mask[:, :, None].expand_as(s_logits)
-            s_logits_slct = torch.masked_select(
-                s_logits, sel_mask
-            )  # (bs * seq_length * voc_size) modulo the 1s in mask
-            t_logits_slct = torch.masked_select(
-                t_logits, sel_mask
-            )  # (bs * seq_length * voc_size) modulo the 1s in mask
-        else:
-            t_logits_slct = t_logits
-            s_logits_slct = s_logits  # (bs * seq_length * voc_size) modulo the 1s in mask
-        s_logits_slct = s_logits_slct.view(-1, s_logits.size(-1))  # (bs * seq_length, voc_size) modulo the 1s in mask
-        t_logits_slct = t_logits_slct.view(-1, s_logits.size(-1))  # (bs * seq_length, voc_size) modulo the 1s in mask
-        assert t_logits_slct.size() == s_logits_slct.size()
-        loss_ce = (
-            self.ce_loss_fct(
-                F.log_softmax(s_logits_slct / self.temperature, dim=-1),
-                F.softmax(t_logits_slct / self.temperature, dim=-1),
-            )
-            * (self.temperature) ** 2
-        )
-        return loss_ce, s_logits_slct, t_logits_slct
-
-    def configure_optimizers(self):
-        "Prepare optimizer and schedule (linear warmup and decay)"
-
-        model = self.model
-        no_decay = ["bias", "LayerNorm.weight"]
-        optimizer_grouped_parameters = [
-            {
-                "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
-                "weight_decay": self.hparams.weight_decay,
-            },
-            {
-                "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
-                "weight_decay": 0.0,
-            },
-        ]
-        optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)
-        self.opt = optimizer
-        return [optimizer]
-
-    @staticmethod
-    def add_model_specific_args(parser, root_dir):
-        SummarizationModule.add_model_specific_args(parser, root_dir)
-        parser.add_argument("--teacher", default="facebook/bart-large-cnn", type=str)
-        parser.add_argument("--alpha_ce", default=0.8, type=float)
-        parser.add_argument("--alpha_mlm", default=0.2, type=float)
-        # parser.add_argument("--alpha_cos", default=0.0, type=float)
-        parser.add_argument("--alpha_encoder_loss", default=0.0, type=float)
-        parser.add_argument("--alpha_hid", default=0.0, type=float, required=False)
-        parser.add_argument(
-            "--student_decoder_layers", default=12, type=int, required=False,
-        )
-        parser.add_argument(
-            "--student_encoder_layers", default=12, type=int, required=False,
-        )
-        parser.add_argument(
-            "--no_teacher", action="store_true", default=False,
-        )
-        parser.add_argument(  # TODO: remove
-            "--enc_only", action="store_true", default=False,
-        )
-        return parser
-
-    def _step(self, batch):
-        # assert is_frozen(self.teacher)
-        pad_token_id = self.tokenizer.pad_token_id
-        input_ids, src_mask, y = batch["input_ids"], batch["attention_mask"], batch["decoder_input_ids"]
-        decoder_input_ids = y[:, :-1].contiguous()
-        labels = y[:, 1:].clone()
-        labels[y[:, 1:] == pad_token_id] = -100
-        # noinspection PyCallingNonCallable
-        sloss, slogits, dec_hidden, enc_outputs, enc_hidden_state = self(
-            input_ids,
-            attention_mask=src_mask,
-            decoder_input_ids=decoder_input_ids,
-            labels=labels,
-            output_hidden_states=True,
-            output_attentions=False,
-        )
-
-        def zero_tensor():
-            return torch.tensor(0.0).type_as(sloss)
-
-        loss_encoder, hid_loss_enc, hid_loss_dec = zero_tensor(), zero_tensor(), zero_tensor()
-        if self.different_encoder:
-            with torch.no_grad():
-                teacher_enc_outputs, teacher_enc_hid, _ = self.teacher.model.encoder(
-                    input_ids, attention_mask=src_mask, output_hidden_states=True
-                )
-            if self.hparams.alpha_encoder_loss > 0:
-                loss_encoder = self.calc_mse_loss(enc_outputs, teacher_enc_outputs, src_mask)
-
-            hid_loss_enc = self.calc_hidden_loss(
-                src_mask, enc_hidden_state, teacher_enc_hid, self.hparams.e_layer_to_copy
-            )
-
-        teacher_enc_outputs = (enc_outputs,)
-        assert isinstance(teacher_enc_outputs, tuple), type(teacher_enc_outputs)
-
-        with torch.no_grad():
-            tloss, tlogits, tdec_hidden, _ = self.teacher(
-                input_ids,
-                attention_mask=src_mask,
-                encoder_outputs=teacher_enc_outputs,
-                decoder_input_ids=decoder_input_ids,
-                lm_labels=labels,
-                output_hidden_states=True,
-            )
-        dec_mask = decoder_input_ids.ne(pad_token_id)
-        loss_ce, s_logits_slct, t_logits_slct = self.calc_ce_loss(dec_mask, slogits, tlogits)
-        if self.alpha_hid > 0:
-            hid_loss_dec = self.calc_hidden_loss(dec_mask, dec_hidden, tdec_hidden, self.hparams.d_layer_to_copy)
-
-        blended_loss = (
-            self.alpha_ce * loss_ce
-            + self.alpha_mlm * sloss
-            + self.hparams.alpha_encoder_loss * loss_encoder
-            + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
-        )
-        return blended_loss, loss_ce, sloss, loss_encoder, hid_loss_enc, hid_loss_dec
-
-    def calc_hidden_loss(self, attention_mask, hidden_states, hidden_states_T, matches):
-        assert not isinstance(
-            hidden_states, torch.Tensor
-        ), f"expected list or tuple for hidden_states, got tensor of shape {hidden_states.shape}"
-        assert not isinstance(
-            hidden_states_T, torch.Tensor
-        ), f"expected list or tuple for hidden_states_T, got tensor of shape {hidden_states_T.shape}"
-        mask = attention_mask.to(hidden_states[0])
-        valid_count = mask.sum() * hidden_states[0].size(-1)
-        hidden_losses = [
-            (F.mse_loss(hidden_states[i], hidden_states_T[j], reduction="none") * mask.unsqueeze(-1)).sum()
-            / valid_count
-            for i, j in enumerate(matches)
-        ]
-        return sum(hidden_losses)
-
-
-class T5SummarizationDistiller(SummarizationDistiller):
-    def pre_init(self, hparams):
-        raise NotImplementedError("T5 Distillation does not work yet")
-        teacher = T5ForConditionalGeneration.from_pretrained(hparams.teacher)
-        n_layer = hparams.student_decoder_layers
-        assert n_layer == hparams.student_encoder_layers  # TODO(SS): relax this
-        d_layers_to_copy = get_layers_to_copy(n_layer, len(teacher.decoder.block))
-        e_layers_to_copy: List = get_layers_to_copy(n_layer, len(teacher.encoder.block))
-        student_updates = {"num_layers": n_layer}
-        hparams.d_layer_to_copy = d_layers_to_copy
-        hparams.e_layer_to_copy = e_layers_to_copy
-        kw = teacher.config.to_diff_dict()
-
-        kw.update(student_updates)
-        # Copy weights
-        student_cfg = T5Config(**kw)
-        student = T5ForConditionalGeneration(student_cfg)
-        student, _ = init_student(student, teacher)
-        self.copy_to_student(d_layers_to_copy, e_layers_to_copy, hparams, student, teacher)
-        Path(hparams.output_dir).mkdir(exist_ok=True)
-        task_specific_params = student.config.task_specific_params
-        if task_specific_params is not None:
-            student.config.update(task_specific_params.get("summarization", {}))
-        return d_layers_to_copy, student, student_cfg, teacher
-
-    def freeze_embeds(self):
-        freeze_params(self.model.shared)
-        for d in [self.model.encoder, self.model.decoder]:
-            freeze_params(d.embed_tokens)
-
-    def sanity_check_gradients(self):
-        """T5"""
-        assert_all_frozen(self.teacher)
-        assert_all_frozen(self.model.decoder.embed_tokens)
-        assert_all_frozen(self.model.encoder.embed_tokens)
-        if self.different_encoder:
-            assert any_requires_grad(self.model.encoder)
-        else:
-            freeze_params(self.model.encoder)
-            del self.teacher.model.encoder
-        if self.different_decoder:
-            assert any_requires_grad(self.model.decoder)
-        else:
-            freeze_params(self.model.decoder)  # TODO(SS): very suspicious
-
-    def _step(self, batch):
-        pad_token_id = self.tokenizer.pad_token_id
-        source_ids, source_mask, y = batch["input_ids"], batch["attention_mask"], batch["decoder_input_ids"]
-        decoder_input_ids = y[:, :-1].contiguous()
-        labels = y[:, 1:].clone()
-        labels[y[:, 1:] == pad_token_id] = -100
-        # noinspection PyCallingNonCallable
-        dec_mask = decoder_input_ids.ne(pad_token_id)
-
-        sloss, slogits, dec_hidden, enc_outputs, enc_hidden_state = self(
-            source_ids,
-            attention_mask=source_mask,
-            decoder_input_ids=decoder_input_ids,
-            labels=labels,
-            output_hidden_states=True,
-            output_attentions=False,
-            use_cache=False,
-        )
-
-        def zero_tensor():
-            return torch.tensor(0.0).type_as(sloss)
-
-        loss_encoder, hid_loss_enc, hid_loss_dec = zero_tensor(), zero_tensor(), zero_tensor()
-        if self.different_encoder:
-            with torch.no_grad():
-                teacher_enc_outputs, teacher_enc_hid = self.teacher.encoder(
-                    source_ids, attention_mask=source_mask, output_hidden_states=True, use_cache=False,
-                )
-            if self.hparams.alpha_encoder_loss > 0:
-                loss_encoder = self.calc_mse_loss(enc_outputs, teacher_enc_outputs, source_mask)
-
-            hid_loss_enc = self.calc_hidden_loss(
-                source_mask, enc_hidden_state, teacher_enc_hid, self.hparams.e_layer_to_copy
-            )
-
-        teacher_enc_outputs = (enc_outputs,)
-        assert isinstance(teacher_enc_outputs, tuple), type(teacher_enc_outputs)
-
-        with torch.no_grad():
-            tloss, tlogits, tdec_hidden, _ = self.teacher(
-                source_ids,
-                attention_mask=source_mask,
-                encoder_outputs=teacher_enc_outputs,
-                decoder_input_ids=decoder_input_ids,
-                lm_labels=labels,
-                output_hidden_states=True,
-                use_cache=False,
-            )
-
-        loss_ce, s_logits_slct, t_logits_slct = self.calc_ce_loss(dec_mask, slogits, tlogits)
-        if self.alpha_hid > 0:
-            hid_loss_dec = self.calc_hidden_loss(dec_mask, dec_hidden, tdec_hidden, self.hparams.d_layer_to_copy)
-
-        blended_loss = (
-            self.alpha_ce * loss_ce
-            + self.alpha_mlm * sloss
-            + self.hparams.alpha_encoder_loss * loss_encoder
-            + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
-        )
-        return blended_loss, loss_ce, sloss, loss_encoder, hid_loss_enc, hid_loss_dec
-
-
-def create_module(args):
-    t5 = "t5" in args.model_name_or_path
-    if args.no_teacher:
-        assert not args.enc_only
-        module_cls = SummarizationModule
-    elif t5:
-        module_cls = T5SummarizationDistiller
-    elif args.enc_only:
-        raise ValueError("Deleted that")
-    else:
-        module_cls = SummarizationDistiller
-    args.setup_cls: str = module_cls.__name__
-    model = module_cls(args)
-    return model
-
-
-def evaluate_checkpoint(ckpt_path: Path, dest_dir=None):
-    exp_dir = ckpt_path.parent
-    if dest_dir is None:
-        dest_dir = exp_dir
-    clash = list(dest_dir.glob("test_generations*"))
-    if clash:
-        print(f"SKIPPING to avoid overwriting {clash}")
-    ckpt = torch.load(ckpt_path, map_location="cpu")
-    if "hparams" in ckpt:
-        args = argparse.Namespace(**ckpt["hparams"])
-    else:
-        args = argparse.Namespace(**pickle_load(exp_dir / "hparams.pkl"))
-    args.resume_from_checkpoint = str(ckpt_path)
-    args.do_train = False
-    args.output_dir = str(dest_dir)
-    args.n_gpu = 1
-    args.eval_batch_size = 16
-    Path(args.output_dir).mkdir(exist_ok=True)
-    model = create_module(args)
-    trainer: pl.Trainer = generic_train(model, args, early_stopping_callback=False)
-    trainer.test(model)
-
-
-def get_layers_to_copy(n_to_get, tot):
-    all_layers = list(range(tot))
-    if tot == 12:  # Alternating for special cases
-        layers_to_copy = {  # maps # layers in student -> which teacher layers to copy
-            6: [0, 2, 4, 7, 9, 11],
-            1: [11],
-            3: [0, 6, 11],
-            2: [0, 11],
-            4: [0, 4, 8, 11],
-            9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
-            12: all_layers,
-        }
-        return layers_to_copy[n_to_get]
-    else:
-        return all_layers[:n_to_get]
-
-
-def distill_main(args):
-    Path(args.output_dir).mkdir(exist_ok=True)
-    if len(os.listdir(args.output_dir)) > 3 and args.do_train:
-        raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
-
-    model = create_module(args)
-    return ft_main(args, model=model)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser = SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
-    args = parser.parse_args()
-
-    distill_main(args)

examples/summarization/evaluate_cnn.py

@@ -0,0 +1,100 @@
+import argparse
+from pathlib import Path
+
+import torch
+from rouge_score import rouge_scorer, scoring
+from tqdm import tqdm
+
+from transformers import AutoModelWithLMHead, AutoTokenizer
+
+
+DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def chunks(lst, n):
+    """Yield successive n-sized chunks from lst."""
+    for i in range(0, len(lst), n):
+        yield lst[i : i + n]
+
+
+def generate_summaries(
+    examples: list, out_file: str, model_name: str, batch_size: int = 8, device: str = DEFAULT_DEVICE
+):
+    fout = Path(out_file).open("w", encoding="utf-8")
+    model = AutoModelWithLMHead.from_pretrained(model_name).to(device)
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+    # update config with summarization specific params
+    task_specific_params = model.config.task_specific_params
+    if task_specific_params is not None:
+        model.config.update(task_specific_params.get("summarization", {}))
+
+    for batch in tqdm(list(chunks(examples, batch_size))):
+        if "t5" in model_name:
+            batch = [model.config.prefix + text for text in batch]
+        dct = tokenizer.batch_encode_plus(batch, max_length=1024, return_tensors="pt", pad_to_max_length=True).to(
+            device
+        )
+        summaries = model.generate(**dct)
+
+        dec = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+        for hypothesis in dec:
+            fout.write(hypothesis + "\n")
+            fout.flush()
+
+
+def calculate_rouge(output_lns, reference_lns, score_path):
+    score_file = Path(score_path).open("w")
+    scorer = rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=True)
+    aggregator = scoring.BootstrapAggregator()
+
+    for reference_ln, output_ln in zip(reference_lns, output_lns):
+        scores = scorer.score(reference_ln, output_ln)
+        aggregator.add_scores(scores)
+
+    result = aggregator.aggregate()
+    score_file.write(
+        "ROUGE_1: \n{} \n\n ROUGE_2: \n{} \n\n ROUGE_L: \n{} \n\n".format(
+            result["rouge1"], result["rouge2"], result["rougeL"]
+        )
+    )
+
+
+def run_generate():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "input_path", type=str, help="like cnn_dm/test.source or cnn_dm/test_articles_input.txt",
+    )
+    parser.add_argument(
+        "output_path", type=str, help="where to save summaries",
+    )
+    parser.add_argument(
+        "model_name",
+        type=str,
+        default="facebook/bart-large-cnn",
+        help="like bart-large-cnn,'t5-small', 't5-base', 't5-large', 't5-3b', 't5-11b",
+    )
+    parser.add_argument("--reference_path", type=str, required=False, help="like cnn_dm/test_reference_summaries.txt")
+    parser.add_argument(
+        "--score_path", type=str, required=False, help="where to save the rouge score",
+    )
+    parser.add_argument(
+        "--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.",
+    )
+    parser.add_argument(
+        "--bs", type=int, default=8, required=False, help="batch size: how many to summarize at a time",
+    )
+    args = parser.parse_args()
+    examples = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path).readlines()]
+
+    generate_summaries(examples, args.output_path, args.model_name, batch_size=args.bs, device=args.device)
+    if args.score_path is not None:
+        output_lns = [x.rstrip() for x in open(args.output_path).readlines()]
+        reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()]
+
+        calculate_rouge(output_lns, reference_lns, args.score_path)
+
+
+if __name__ == "__main__":
+    run_generate()

examples/summarization/finetune.py

@@ -3,202 +3,114 @@ import glob
 import logging
 import os
 import time
-from pathlib import Path
-from typing import Dict, List, Tuple
 
-import numpy as np
-import pytorch_lightning as pl
 import torch
 from torch.utils.data import DataLoader
 
-from lightning_base import BaseTransformer, add_generic_args, generic_train
-from transformers import get_linear_schedule_with_warmup
+from lightning_base import BaseTransformer, add_generic_args, generic_train, get_linear_schedule_with_warmup
 
 
 try:
-    from .utils import (
-        use_task_specific_params,
-        SummarizationDataset,
-        lmap,
-        flatten_list,
-        pickle_save,
-        save_git_info,
-        freeze_params,
-        calculate_rouge,
-        get_git_info,
-        ROUGE_KEYS,
-    )
-    from .callbacks import Seq2SeqLoggingCallback, get_rouge2_checkpoint_callback
+    from .utils import SummarizationDataset
 except ImportError:
-    from utils import (
-        use_task_specific_params,
-        SummarizationDataset,
-        lmap,
-        flatten_list,
-        pickle_save,
-        save_git_info,
-        freeze_params,
-        calculate_rouge,
-        get_git_info,
-        ROUGE_KEYS,
-    )
-    from callbacks import Seq2SeqLoggingCallback, get_rouge2_checkpoint_callback
+    from utils import SummarizationDataset
+
 
 logger = logging.getLogger(__name__)
 
 
-class SummarizationModule(BaseTransformer):
-    mode = "summarization"
-    loss_names = ["loss"]
+class SummarizationTrainer(BaseTransformer):
 
-    def __init__(self, hparams, **kwargs):
-        super().__init__(hparams, num_labels=None, mode=self.mode, **kwargs)
-        use_task_specific_params(self.model, "summarization")
-        save_git_info(self.hparams.output_dir)
-        self.metrics_save_path = Path(self.output_dir) / "metrics.pkl"
-        self.hparams_save_path = Path(self.output_dir) / "hparams.pkl"
-        self.step_count = 0
-        self.metrics = {"train": [], "val": [], "test": []}
+    mode = "language-modeling"
 
+    def __init__(self, hparams):
+        super().__init__(hparams, num_labels=None, mode=self.mode)
         self.dataset_kwargs: dict = dict(
             data_dir=self.hparams.data_dir,
             max_source_length=self.hparams.max_source_length,
-            prefix=self.model.config.prefix or "",
+            max_target_length=self.hparams.max_target_length,
         )
-        n_observations_per_split = {
-            "train": self.hparams.n_train,
-            "val": self.hparams.n_val,
-            "test": self.hparams.n_test,
-        }
-        self.n_obs = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
 
-        self.target_lens = {
-            "train": self.hparams.max_target_length,
-            "val": self.hparams.val_max_target_length,
-            "test": self.hparams.test_max_target_length,
-        }
-        assert self.target_lens["train"] <= self.target_lens["val"], f"target_lens: {self.target_lens}"
-        assert self.target_lens["train"] <= self.target_lens["test"], f"target_lens: {self.target_lens}"
-
-        if self.hparams.freeze_embeds:
-            self.freeze_embeds()
-        if self.hparams.freeze_encoder:
-            freeze_params(self.model.model.encoder)  # TODO: this will break for t5
-        self.hparams.git_sha = get_git_info()["repo_sha"]
-        self.num_workers = hparams.num_workers
-
-    def freeze_embeds(self):
-        """Freeze token embeddings and positional embeddings for bart, just token embeddings for t5."""
-        if self.model.config.model_type == "bart":
-            freeze_params(self.model.model.shared)
-            for d in [self.model.model.encoder, self.model.model.decoder]:
-                freeze_params(d.embed_positions)
-                freeze_params(d.embed_tokens)
-        else:
-            freeze_params(self.model.shared)
-            for d in [self.model.encoder, self.model.decoder]:
-                freeze_params(d.embed_tokens)
-
-    def forward(self, input_ids, **kwargs):
-        return self.model(input_ids, **kwargs)
-
-    def ids_to_clean_text(self, generated_ids: List[int]):
-        gen_text = self.tokenizer.batch_decode(
-            generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
+    def forward(self, input_ids, attention_mask=None, decoder_input_ids=None, lm_labels=None):
+        return self.model(
+            input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, lm_labels=lm_labels,
         )
-        return lmap(str.strip, gen_text)
 
-    def _step(self, batch: dict) -> Tuple:
+    def _step(self, batch):
         pad_token_id = self.tokenizer.pad_token_id
-        source_ids, source_mask, y = batch["input_ids"], batch["attention_mask"], batch["decoder_input_ids"]
+        source_ids, source_mask, y = batch["source_ids"], batch["source_mask"], batch["target_ids"]
         y_ids = y[:, :-1].contiguous()
         lm_labels = y[:, 1:].clone()
         lm_labels[y[:, 1:] == pad_token_id] = -100
-        outputs = self(source_ids, attention_mask=source_mask, decoder_input_ids=y_ids, labels=lm_labels,)
+        outputs = self(source_ids, attention_mask=source_mask, decoder_input_ids=y_ids, lm_labels=lm_labels,)
+
         loss = outputs[0]
-        return (loss,)
 
-    def training_step(self, batch, batch_idx) -> Dict:
-        loss_tensors = self._step(batch)
-        logs = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
-        return {"loss": loss_tensors[0], "log": logs}
+        return loss
 
-    def validation_step(self, batch, batch_idx) -> Dict:
-        return self._generative_step(batch)
+    def training_step(self, batch, batch_idx):
+        loss = self._step(batch)
 
-    def validation_epoch_end(self, outputs, prefix="val") -> Dict:
-        self.step_count += 1
-        losses = {k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
-        loss = losses["loss"]
-        rouges = {k: np.array([x[k] for x in outputs]).mean() for k in ROUGE_KEYS + ["gen_time", "summ_len"]}
-        rouge_tensor: torch.FloatTensor = torch.tensor(rouges["rouge2"]).type_as(loss)
-        rouges.update({k: v.item() for k, v in losses.items()})
-        losses.update(rouges)
-        metrics = {f"{prefix}_avg_{k}": x for k, x in losses.items()}
-        metrics["step_count"] = self.step_count
-        self.save_metrics(metrics, prefix)  # writes to self.metrics_save_path
-        preds = flatten_list([x["preds"] for x in outputs])
-        return {"log": metrics, "preds": preds, f"{prefix}_loss": loss, f"{prefix}_rouge": rouge_tensor}
+        tensorboard_logs = {"train_loss": loss}
+        return {"loss": loss, "log": tensorboard_logs}
 
-    def save_metrics(self, metrics, prefix) -> None:
-        self.metrics[prefix].append(metrics)
-        pickle_save(self.metrics, self.metrics_save_path)
+    def validation_step(self, batch, batch_idx):
+        loss = self._step(batch)
+        return {"val_loss": loss}
 
-    def _generative_step(self, batch: dict) -> dict:
-        pad_token_id = self.tokenizer.pad_token_id
-        source_ids, source_mask, y = SummarizationDataset.trim_seq2seq_batch(batch, pad_token_id)
-        t0 = time.time()
-        generated_ids = self.model.generate(input_ids=source_ids, attention_mask=source_mask, use_cache=True,)
-        gen_time = (time.time() - t0) / source_ids.shape[0]
-        preds = self.ids_to_clean_text(generated_ids)
-        target = self.ids_to_clean_text(y)
-        loss_tensors = self._step(batch)
-        base_metrics = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
-        rouge: Dict = calculate_rouge(preds, target)
-        summ_len = np.mean(lmap(len, generated_ids))
-        base_metrics.update(gen_time=gen_time, summ_len=summ_len, preds=preds, target=target, **rouge)
-        return base_metrics
+    def validation_end(self, outputs):
+        avg_loss = torch.stack([x["val_loss"] for x in outputs]).mean()
+        tensorboard_logs = {"val_loss": avg_loss}
+        return {"avg_val_loss": avg_loss, "log": tensorboard_logs}
 
     def test_step(self, batch, batch_idx):
-        return self._generative_step(batch)
+        pad_token_id = self.tokenizer.pad_token_id
+        source_ids, source_mask, y = SummarizationDataset.trim_seq2seq_batch(batch, pad_token_id)
+        # NOTE: the following kwargs get more speed and lower quality summaries than those in evaluate_cnn.py
+        generated_ids = self.model.generate(
+            input_ids=source_ids,
+            attention_mask=source_mask,
+            num_beams=1,
+            max_length=80,
+            repetition_penalty=2.5,
+            length_penalty=1.0,
+            early_stopping=True,
+            use_cache=True,
+        )
+        preds = [
+            self.tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+            for g in generated_ids
+        ]
+        target = [self.tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in y]
+        loss = self._step(batch)
+
+        return {"val_loss": loss, "preds": preds, "target": target}
+
+    def test_end(self, outputs):
+        return self.validation_end(outputs)
 
     def test_epoch_end(self, outputs):
-        return self.validation_epoch_end(outputs, prefix="test")
+        output_test_predictions_file = os.path.join(self.hparams.output_dir, "test_predictions.txt")
+        output_test_targets_file = os.path.join(self.hparams.output_dir, "test_targets.txt")
+        # write predictions and targets for later rouge evaluation.
+        with open(output_test_predictions_file, "w+") as p_writer, open(output_test_targets_file, "w+") as t_writer:
+            for output_batch in outputs:
+                p_writer.writelines(s + "\n" for s in output_batch["preds"])
+                t_writer.writelines(s + "\n" for s in output_batch["target"])
+            p_writer.close()
+            t_writer.close()
 
-    def get_dataset(self, type_path) -> SummarizationDataset:
-        n_obs = self.n_obs[type_path]
-        max_target_length = self.target_lens[type_path]
-        dataset = SummarizationDataset(
-            self.tokenizer,
-            type_path=type_path,
-            n_obs=n_obs,
-            max_target_length=max_target_length,
-            **self.dataset_kwargs,
-        )
-        return dataset
+        return self.test_end(outputs)
 
     def get_dataloader(self, type_path: str, batch_size: int, shuffle: bool = False) -> DataLoader:
-        dataset = self.get_dataset(type_path)
-        sampler = None
-        if self.hparams.sortish_sampler and type_path == "train":
-            assert self.hparams.gpus <= 1  # TODO: assert earlier
-            sampler = dataset.make_sortish_sampler(batch_size)
-            shuffle = False
-
-        dataloader = DataLoader(
-            dataset,
-            batch_size=batch_size,
-            collate_fn=dataset.collate_fn,
-            shuffle=shuffle,
-            num_workers=self.num_workers,
-            sampler=sampler,
-        )
+        dataset = SummarizationDataset(self.tokenizer, type_path=type_path, **self.dataset_kwargs)
+        dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=dataset.collate_fn, shuffle=shuffle)
         return dataloader
 
     def train_dataloader(self) -> DataLoader:
         dataloader = self.get_dataloader("train", batch_size=self.hparams.train_batch_size, shuffle=True)
         t_total = (
-            (len(dataloader.dataset) // (self.hparams.train_batch_size * max(1, self.hparams.gpus)))
+            (len(dataloader.dataset) // (self.hparams.train_batch_size * max(1, self.hparams.n_gpu)))
             // self.hparams.gradient_accumulation_steps
             * float(self.hparams.num_train_epochs)
         )
@@ -217,7 +129,7 @@ class SummarizationModule(BaseTransformer):
     @staticmethod
     def add_model_specific_args(parser, root_dir):
         BaseTransformer.add_model_specific_args(parser, root_dir)
-        add_generic_args(parser, root_dir)
+        # Add BART specific options
         parser.add_argument(
             "--max_source_length",
             default=1024,
@@ -232,83 +144,41 @@ class SummarizationModule(BaseTransformer):
             help="The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded.",
         )
-        parser.add_argument(
-            "--val_max_target_length",
-            default=142,  # these defaults are optimized for CNNDM. For xsum, see README.md.
-            type=int,
-            help="The maximum total input sequence length after tokenization. Sequences longer "
-            "than this will be truncated, sequences shorter will be padded.",
-        )
-        parser.add_argument(
-            "--test_max_target_length",
-            default=142,
-            type=int,
-            help="The maximum total input sequence length after tokenization. Sequences longer "
-            "than this will be truncated, sequences shorter will be padded.",
-        )
+
         parser.add_argument(
             "--data_dir",
+            default=None,
             type=str,
             required=True,
-            help="The input data dir. Should contain train.source, train.target, val.source, val.target, test.source, test.target",
+            help="The input data dir. Should contain the dataset files for the CNN/DM summarization task.",
         )
-        parser.add_argument("--freeze_encoder", action="store_true")
-        parser.add_argument("--freeze_embeds", action="store_true")
-        parser.add_argument("--sortish_sampler", action="store_true", default=False)
-        parser.add_argument("--logger", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
-        parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.")
-        parser.add_argument("--n_val", type=int, default=500, required=False, help="# examples. -1 means use all.")
-        parser.add_argument("--n_test", type=int, default=-1, required=False, help="# examples. -1 means use all.")
         return parser
 
 
-def main(args, model=None) -> SummarizationModule:
-    Path(args.output_dir).mkdir(exist_ok=True)
-    if len(os.listdir(args.output_dir)) > 3 and args.do_train:
-        raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
-    if model is None:
-        model: BaseTransformer = SummarizationModule(args)
-    if (
-        args.logger == "default"
-        or args.fast_dev_run
-        or str(args.output_dir).startswith("/tmp")
-        or str(args.output_dir).startswith("/var")
-    ):
-        logger = True  # don't pollute wandb logs unnecessarily
-    elif args.logger == "wandb":
-        from pytorch_lightning.loggers import WandbLogger
+def main(args):
 
-        logger = WandbLogger(name=model.output_dir.name)
-    elif args.logger == "wandb_shared":
-        from pytorch_lightning.loggers import WandbLogger
+    # If output_dir not provided, a folder will be generated in pwd
+    if not args.output_dir:
+        args.output_dir = os.path.join("./results", f"{args.task}_{time.strftime('%Y%m%d_%H%M%S')}",)
+        os.makedirs(args.output_dir)
+    model = SummarizationTrainer(args)
+    trainer = generic_train(model, args)
 
-        # TODO: separate LB for CNN, we should use Path(args.data_dir).name to determine the correct LB.
-        logger = WandbLogger(name=model.output_dir.name, project="hf_summarization")
-    trainer: pl.Trainer = generic_train(
-        model,
-        args,
-        logging_callback=Seq2SeqLoggingCallback(),
-        checkpoint_callback=get_rouge2_checkpoint_callback(args.output_dir),
-        logger=logger,
-        # TODO: early stopping callback seems messed up
-    )
-    pickle_save(model.hparams, model.output_dir / "hparams.pkl")
-    if not args.do_predict:
-        return model
-
-    model.hparams.test_checkpoint = ""
-    checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "*.ckpt"), recursive=True)))
-    if checkpoints:
-        model.hparams.test_checkpoint = checkpoints[-1]
-        trainer.resume_from_checkpoint = checkpoints[-1]
-    trainer.logger.log_hyperparams(model.hparams)
-    trainer.test(model)  # this breaks in DDP, known lightning issue. See evaluate_checkpoint to recover metrics.
-    return model
+    # Optionally, predict on dev set and write to output_dir
+    if args.do_predict:
+        # See https://github.com/huggingface/transformers/issues/3159
+        # pl use this format to create a checkpoint:
+        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
+        # /pytorch_lightning/callbacks/model_checkpoint.py#L169
+        checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "checkpointepoch=*.ckpt"), recursive=True)))
+        model = model.load_from_checkpoint(checkpoints[-1])
+        trainer.test(model)
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser = SummarizationModule.add_model_specific_args(parser, os.getcwd())
+    add_generic_args(parser, os.getcwd())
+    parser = SummarizationTrainer.add_model_specific_args(parser, os.getcwd())
     args = parser.parse_args()
 
     main(args)

examples/summarization/finetune.sh

@@ -1,20 +0,0 @@
-
-# Add parent directory to python path to access lightning_base.py
-export PYTHONPATH="../":"${PYTHONPATH}"
-
-
-# --model_name_or_path=t5-base for t5
-
-# the proper usage is documented in the README
-python finetune.py \
-    --model_name_or_path=facebook/bart-large \
-    --learning_rate=3e-5 \
-    --fp16 \
-    --gpus 1 \
-    --do_train \
-    --do_predict \
-    --n_val 1000 \
-    --val_check_interval 0.1 \
-    --sortish_sampler \
-    --max_target_length=56 \
-    $@

examples/summarization/finetune_bart.sh

@@ -0,0 +1,18 @@
+export OUTPUT_DIR_NAME=bart_sum
+export CURRENT_DIR=${PWD}
+export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
+
+# Make output directory if it doesn't exist
+mkdir -p $OUTPUT_DIR
+
+# Add parent directory to python path to access lightning_base.py
+export PYTHONPATH="../":"${PYTHONPATH}"
+
+python finetune.py \
+--data_dir=./cnn-dailymail/cnn_dm \
+--model_name_or_path=bart-large \
+--learning_rate=3e-5 \
+--train_batch_size=4 \
+--eval_batch_size=4 \
+--output_dir=$OUTPUT_DIR \
+--do_train  $@

examples/summarization/finetune_bart_tiny.sh

@@ -16,13 +16,14 @@ mkdir -p $OUTPUT_DIR
 export PYTHONPATH="../":"${PYTHONPATH}"
 python finetune.py \
 --data_dir=cnn_tiny/ \
+--model_type=bart \
 --model_name_or_path=sshleifer/bart-tiny-random \
 --learning_rate=3e-5 \
 --train_batch_size=2 \
 --eval_batch_size=2 \
 --output_dir=$OUTPUT_DIR \
 --num_train_epochs=1  \
---gpus=0 \
+--n_gpu=0 \
 --do_train $@
 
 rm -rf cnn_tiny

examples/summarization/finetune_t5.sh

@@ -11,6 +11,7 @@ export PYTHONPATH="../":"${PYTHONPATH}"
 python finetune.py \
 --data_dir=./cnn-dailymail/cnn_dm \
 --model_name_or_path=t5-large \
+--model_type=t5
 --learning_rate=3e-5 \
 --train_batch_size=4 \
 --eval_batch_size=4 \

examples/summarization/initialization_utils.py

@@ -1,20 +0,0 @@
-from typing import List
-
-from torch import nn
-
-
-def init_student(student, teacher):
-    teacher_state_dict = teacher.state_dict()
-    info = student.load_state_dict(teacher_state_dict, strict=False)
-    assert info.missing_keys == [], info.missing_keys
-    return student, info
-
-
-def copy_decoder_layers(teacher, student, l2copy=[0, 2, 4, 7, 9, 11]):
-    copy_layers(teacher.model.decoder.layers, student.model.decoder.layers, l2copy)
-
-
-def copy_layers(teacher_layers: nn.ModuleList, student_layers: nn.ModuleList, layers_to_copy: List) -> None:
-    layers_to_copy = nn.ModuleList([l for i, l in enumerate(teacher_layers) if i in layers_to_copy])
-    assert len(student_layers) == len(layers_to_copy), f"{len(student_layers)} != {len(layers_to_copy)}"
-    student_layers.load_state_dict(layers_to_copy.state_dict())

examples/summarization/run_distiller.sh

@@ -1,12 +0,0 @@
-#CNN_DIR = /home/shleifer/transformers_fork/examples/summarization/bart/cnn_dm
-
-# Add parent directory to python path to access lightning_base.py
-export PYTHONPATH="../":"${PYTHONPATH}"
-
-python distillation.py \
---learning_rate=3e-4 \
---do_train \
---do_predict \
---fp16 \
---val_check_interval 0.1 \
-$@

examples/summarization/run_eval.py

@@ -1,79 +0,0 @@
-import argparse
-import json
-from pathlib import Path
-
-import torch
-from tqdm import tqdm
-
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-
-
-try:
-    from .finetune import calculate_rouge, use_task_specific_params
-except ImportError:
-    from finetune import calculate_rouge, use_task_specific_params
-
-DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-def chunks(lst, n):
-    """Yield successive n-sized chunks from lst."""
-    for i in range(0, len(lst), n):
-        yield lst[i : i + n]
-
-
-def generate_summaries(
-    examples: list, out_file: str, model_name: str, batch_size: int = 8, device: str = DEFAULT_DEVICE, fp16=False,
-) -> None:
-    fout = Path(out_file).open("w", encoding="utf-8")
-    model_name = str(model_name)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
-    if fp16:
-        model = model.half()
-
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-    # update config with summarization specific params
-    use_task_specific_params(model, "summarization")
-
-    for batch in tqdm(list(chunks(examples, batch_size))):
-        if "t5" in model_name:
-            batch = [model.config.prefix + text for text in batch]
-        dct = tokenizer.batch_encode_plus(batch, max_length=1024, return_tensors="pt", pad_to_max_length=True).to(
-            device
-        )
-        summaries = model.generate(**dct)
-
-        dec = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False)
-        for hypothesis in dec:
-            fout.write(hypothesis + "\n")
-            fout.flush()
-
-
-def run_generate():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("input_path", type=str, help="like cnn_dm/test.source")
-    parser.add_argument("output_path", type=str, help="where to save summaries")
-    parser.add_argument("model_name", type=str, help="like facebook/bart-large-cnn,t5-base, etc.")
-    parser.add_argument("--reference_path", type=str, required=False, help="like cnn_dm/test_reference_summaries.txt")
-    parser.add_argument("--score_path", type=str, required=False, help="where to save the rouge score in json format")
-    parser.add_argument("--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.")
-    parser.add_argument("--bs", type=int, default=8, required=False, help="batch size")
-    parser.add_argument("--fp16", action="store_true")
-    args = parser.parse_args()
-    examples = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path).readlines()]
-
-    generate_summaries(
-        examples, args.output_path, args.model_name, batch_size=args.bs, device=args.device, fp16=args.fp16
-    )
-    if args.score_path is not None:
-        output_lns = [x.rstrip() for x in open(args.output_path).readlines()]
-        reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()]
-
-        rouge: dict = calculate_rouge(output_lns, reference_lns)
-
-        json.dump(rouge, open("score_path", "w+"))
-
-
-if __name__ == "__main__":
-    run_generate()

examples/summarization/test_summarization_examples.py

@@ -7,41 +7,28 @@ import unittest
 from pathlib import Path
 from unittest.mock import patch
 
-import torch
 from torch.utils.data import DataLoader
 
 from transformers import BartTokenizer
 
-from .distillation import distill_main, evaluate_checkpoint
+from .evaluate_cnn import run_generate
 from .finetune import main
-from .run_eval import generate_summaries, run_generate
-from .utils import SummarizationDataset, lmap, pickle_load
+from .utils import SummarizationDataset
 
 
 logging.basicConfig(level=logging.DEBUG)
 
 logger = logging.getLogger()
-FP16_EVER = False
-CHEAP_ARGS = {
-    "logger": "default",
-    "num_workers": 2,
-    "alpha_hid": 0,
-    "freeze_embeds": True,
-    "enc_only": False,
-    "tgt_suffix": "",
-    "resume_from_checkpoint": None,
-    "sortish_sampler": True,
-    "student_decoder_layers": 1,
-    "val_check_interval": 1.0,
+
+DEFAULT_ARGS = {
     "output_dir": "",
     "fp16": False,
-    "no_teacher": False,
     "fp16_opt_level": "O1",
-    "gpus": 1 if torch.cuda.is_available() else 0,
+    "n_gpu": 1,
     "n_tpu_cores": 0,
     "max_grad_norm": 1.0,
     "do_train": True,
-    "do_predict": True,
+    "do_predict": False,
     "gradient_accumulation_steps": 1,
     "server_ip": "",
     "server_port": "",
@@ -49,7 +36,7 @@ CHEAP_ARGS = {
     "model_type": "bart",
     "model_name_or_path": "sshleifer/bart-tiny-random",
     "config_name": "",
-    "tokenizer_name": "facebook/bart-large",
+    "tokenizer_name": "",
     "cache_dir": "",
     "do_lower_case": False,
     "learning_rate": 3e-05,
@@ -61,17 +48,6 @@ CHEAP_ARGS = {
     "eval_batch_size": 2,
     "max_source_length": 12,
     "max_target_length": 12,
-    "val_max_target_length": 12,
-    "test_max_target_length": 12,
-    "fast_dev_run": False,
-    "no_cache": False,
-    "n_train": -1,
-    "n_val": -1,
-    "n_test": -1,
-    "student_encoder_layers": 1,
-    "alpha_loss_encoder": 0.0,
-    "freeze_encoder": False,
-    "auto_scale_batch_size": False,
 }
 
 
@@ -80,10 +56,6 @@ def _dump_articles(path: Path, articles: list):
         f.write("\n".join(articles))
 
 
-MSG = "T5 is broken at the moment"
-T5_TINY = "patrickvonplaten/t5-tiny-random"
-
-
 def make_test_data_dir():
     tmp_dir = Path(tempfile.gettempdir())
     articles = [" Sam ate lunch today", "Sams lunch ingredients"]
@@ -94,111 +66,6 @@ def make_test_data_dir():
     return tmp_dir
 
 
-class TestSummarizationDistiller(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
-        return cls
-
-    @unittest.skipUnless(torch.cuda.device_count() > 1, "skipping multiGPU test")
-    def test_bdc_multigpu(self):
-        updates = dict(
-            student_encoder_layers=2,
-            student_decoder_layers=1,
-            no_teacher=True,
-            freeze_encoder=True,
-            gpus=2,
-            sortish_sampler=False,
-            fp16_opt_level="O1",
-            fp16=FP16_EVER,
-        )
-        self._bart_distiller_cli(updates)
-
-    def test_bdc_t5_train(self):
-        updates = dict(
-            fp16=FP16_EVER,
-            gpus=1 if torch.cuda.is_available() else 0,
-            model_type="t5",
-            model_name_or_path=T5_TINY,
-            do_train=True,
-            do_predict=True,
-            tokenizer_name=T5_TINY,
-            no_teacher=True,
-            alpha_hid=2.0,
-        )
-        self._bart_distiller_cli(updates)
-
-    def test_bdc_no_teacher(self):
-        updates = dict(student_encoder_layers=2, student_decoder_layers=1, no_teacher=True,)
-        self._bart_distiller_cli(updates)
-
-    def test_bdc_yes_teacher(self):
-        updates = dict(student_encoder_layers=2, student_decoder_layers=1,)
-        self._bart_distiller_cli(updates)
-
-    def test_bdc_checkpointing(self):
-        updates = dict(
-            student_encoder_layers=2,
-            student_decoder_layers=1,
-            num_train_epochs=4,
-            val_check_interval=0.25,
-            alpha_hid=2.0,
-        )
-        model = self._bart_distiller_cli(updates, check_contents=False)
-
-        ckpts = list(Path(model.output_dir).glob("*.ckpt"))
-        self.assertEqual(1, len(ckpts))
-        transformer_ckpts = list(Path(model.output_dir).glob("**/*.bin"))
-        self.assertEqual(len(transformer_ckpts), len(ckpts))
-        new_transformer_ckpts = list(Path(model.output_dir).glob("**/*.bin"))
-        self.assertEqual(len(new_transformer_ckpts), 1)
-        examples = lmap(str.strip, model.hparams.data_dir.joinpath("test.source").open().readlines())
-        out_path = tempfile.mktemp()
-        generate_summaries(examples, out_path, new_transformer_ckpts[0].parent)
-        self.assertTrue(Path(out_path).exists())
-
-        evaluate_checkpoint(ckpts[0], dest_dir=Path(tempfile.mkdtemp()))
-
-    def _bart_distiller_cli(self, updates, check_contents=True):
-        default_updates = dict(
-            train_batch_size=1,
-            eval_batch_size=2,
-            num_train_epochs=2,
-            alpha_mlm=0.2,
-            alpha_ce=0.8,
-            do_predict=True,
-            gpus=1 if torch.cuda.is_available() else 0,
-            model_name_or_path="sshleifer/tinier_bart",
-            teacher=CHEAP_ARGS["model_name_or_path"],
-            val_check_interval=0.5,
-            alpha_encoder_loss=0.4,
-        )
-        default_updates.update(updates)
-        args_d: dict = CHEAP_ARGS.copy()
-        tmp_dir = make_test_data_dir()
-        output_dir = tempfile.mkdtemp(prefix="output_")
-
-        args_d.update(data_dir=tmp_dir, output_dir=output_dir, **default_updates)
-        model = distill_main(argparse.Namespace(**args_d))
-        if not check_contents:
-            return model
-        contents = os.listdir(output_dir)
-        ckpt_name = "val_avg_rouge2=0.0000-step_count=2.ckpt"  # "val_avg_rouge2=0.0000-epoch=1.ckpt"  # "epoch=1-val_avg_rouge2=0.0000.ckpt"
-        contents = {os.path.basename(p) for p in contents}
-        self.assertIn(ckpt_name, contents)
-        self.assertIn("metrics.pkl", contents)
-        self.assertIn("test_generations.txt", contents)
-        self.assertIn("val_generations_00001.txt", contents)
-        self.assertIn("val_results_00001.txt", contents)
-        self.assertIn("test_results.txt", contents)
-
-        metrics = pickle_load(Path(output_dir) / "metrics.pkl")
-        desired_n_evals = int(args_d["num_train_epochs"] * (1 / args_d["val_check_interval"]) + 1)
-        self.assertEqual(len(metrics["val"]), desired_n_evals)
-        self.assertEqual(len(metrics["train"]), 0)  # doesn't get logged here
-        return model
-
-
 class TestBartExamples(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
@@ -212,30 +79,49 @@ class TestBartExamples(unittest.TestCase):
         output_file_name = Path(tempfile.gettempdir()) / "utest_output_bart_sum.hypo"
         articles = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
         _dump_articles(tmp, articles)
-        testargs = ["run_eval.py", str(tmp), str(output_file_name), "sshleifer/bart-tiny-random"]
+        testargs = ["evaluate_cnn.py", str(tmp), str(output_file_name), "sshleifer/bart-tiny-random"]
         with patch.object(sys, "argv", testargs):
             run_generate()
             self.assertTrue(Path(output_file_name).exists())
             os.remove(Path(output_file_name))
 
-    def test_t5_run_sum_cli(self):
-        args_d: dict = CHEAP_ARGS.copy()
+    def test_bart_run_sum_cli(self):
+        args_d: dict = DEFAULT_ARGS.copy()
+        tmp_dir = make_test_data_dir()
+        output_dir = tempfile.mkdtemp(prefix="output_")
+        args_d.update(
+            data_dir=tmp_dir, model_type="bart", train_batch_size=2, eval_batch_size=2, n_gpu=0, output_dir=output_dir,
+        )
+        main(argparse.Namespace(**args_d))
+        args_d.update({"do_train": False, "do_predict": True})
 
+        main(argparse.Namespace(**args_d))
+        contents = os.listdir(output_dir)
+        expected_contents = {
+            "checkpointepoch=0.ckpt",
+            "test_results.txt",
+        }
+        created_files = {os.path.basename(p) for p in contents}
+        self.assertSetEqual(expected_contents, created_files)
+
+    def test_t5_run_sum_cli(self):
+        args_d: dict = DEFAULT_ARGS.copy()
         tmp_dir = make_test_data_dir()
         output_dir = tempfile.mkdtemp(prefix="output_")
         args_d.update(
             data_dir=tmp_dir,
-            model_name_or_path=T5_TINY,
-            tokenizer_name=None,  # T5_TINY,
+            model_type="t5",
+            model_name_or_path="patrickvonplaten/t5-tiny-random",
             train_batch_size=2,
             eval_batch_size=2,
-            gpus=0,
+            n_gpu=0,
             output_dir=output_dir,
             do_predict=True,
         )
-        assert "n_train" in args_d
-        args = argparse.Namespace(**args_d)
-        main(args)
+        main(argparse.Namespace(**args_d))
+
+        # args_d.update({"do_train": False, "do_predict": True})
+        # main(argparse.Namespace(**args_d))
 
     def test_bart_summarization_dataset(self):
         tmp_dir = Path(tempfile.gettempdir())
@@ -252,16 +138,42 @@ class TestBartExamples(unittest.TestCase):
         )
         dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
         for batch in dataloader:
-            self.assertEqual(batch["attention_mask"].shape, batch["input_ids"].shape)
+            self.assertEqual(batch["source_mask"].shape, batch["source_ids"].shape)
             # show that articles were trimmed.
-            self.assertEqual(batch["input_ids"].shape[1], max_len_source)
-            self.assertGreater(20, batch["input_ids"].shape[1])  # trimmed significantly
+            self.assertEqual(batch["source_ids"].shape[1], max_len_source)
+            self.assertGreater(20, batch["source_ids"].shape[1])  # trimmed significantly
 
             # show that targets were truncated
-            self.assertEqual(batch["decoder_input_ids"].shape[1], trunc_target)  # Truncated
+            self.assertEqual(batch["target_ids"].shape[1], trunc_target)  # Truncated
             self.assertGreater(max_len_target, trunc_target)  # Truncated
 
 
-def list_to_text_file(lst, path):
-    dest = Path(path)
-    dest.open("w+").writelines(lst)
+class TestT5Examples(unittest.TestCase):
+    def test_t5_cli(self):
+        output_file_name = "output_t5_sum.txt"
+        score_file_name = "score_t5_sum.txt"
+        articles = ["New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+        tmp = Path(tempfile.gettempdir()) / "utest_generations_t5_sum.hypo"
+        with tmp.open("w", encoding="utf-8") as f:
+            f.write("\n".join(articles))
+
+        output_file_name = Path(tempfile.gettempdir()) / "utest_output_t5_sum.hypo"
+        score_file_name = Path(tempfile.gettempdir()) / "utest_score_t5_sum.hypo"
+
+        testargs = [
+            "evaluate_cnn.py",
+            str(tmp),
+            str(output_file_name),
+            "patrickvonplaten/t5-tiny-random",
+            "--reference_path",
+            str(tmp),
+            "--score_path",
+            str(score_file_name),
+        ]
+
+        with patch.object(sys, "argv", testargs):
+            run_generate()
+            self.assertTrue(Path(output_file_name).exists())
+            self.assertTrue(Path(score_file_name).exists())

examples/summarization/utils.py

@@ -1,66 +1,20 @@
-import itertools
-import json
 import os
-import pickle
-from pathlib import Path
-from typing import Dict, Iterable, List
 
-import git
-import numpy as np
 import torch
-from rouge_score import rouge_scorer, scoring
-from torch import nn
-from torch.utils.data import Dataset, Sampler
-from tqdm import tqdm
+from torch.utils.data import Dataset
 
 
-def encode_file(
-    tokenizer,
-    data_path,
-    max_length,
-    pad_to_max_length=True,
-    return_tensors="pt",
-    overwrite_cache=False,
-    prefix="",
-    tok_name="",
-):
-    cache_path = Path(f"{data_path}_{tok_name}{max_length}.pt")
-    if not overwrite_cache and cache_path.exists():
-        try:
-            examples = torch.load(cache_path)
-            assert isinstance(examples, list)
-            return examples
-
-        except Exception:
-            print(f"failed to load from {cache_path}, retokenizing {data_path}")
-    data_path = Path(data_path)
-
-    lns = lmap(str.strip, data_path.open().readlines())
-    lns = [prefix + text for text in lns]
-    assert lns, f"found empty file at {data_path}"
+def encode_file(tokenizer, data_path, max_length, pad_to_max_length=True, return_tensors="pt"):
     examples = []
-    for text in tqdm(lns, desc=f"Tokenizing {data_path.name}"):
-        tokenized = tokenizer.batch_encode_plus(
-            [text],  # DONT ADD SPACES
-            max_length=max_length,
-            pad_to_max_length=pad_to_max_length,
-            add_prefix_space=True,
-            truncation=True,
-            return_tensors=return_tensors,
-        )
-        assert tokenized.input_ids.shape[1] == max_length
-        examples.append(tokenized)
-    torch.save(lmap(dict, examples), cache_path.open("wb"))
+    with open(data_path, "r") as f:
+        for text in f.readlines():
+            tokenized = tokenizer.batch_encode_plus(
+                [text], max_length=max_length, pad_to_max_length=pad_to_max_length, return_tensors=return_tensors,
+            )
+            examples.append(tokenized)
     return examples
 
 
-def lmap(f, x):
-    return list(map(f, x))
-
-
-T5_PREFIX = "summarize: "  # HACK, fixme
-
-
 def trim_batch(
     input_ids, pad_token_id, attention_mask=None,
 ):
@@ -76,32 +30,15 @@ class SummarizationDataset(Dataset):
     def __init__(
         self,
         tokenizer,
-        data_dir,
+        data_dir="./cnn-dailymail/cnn_dm/",
         type_path="train",
         max_source_length=1024,
         max_target_length=56,
-        n_obs=None,
-        overwrite_cache=False,
-        prefix="",
     ):
         super().__init__()
-        tok_name = tokenizer.__class__.__name__.lower().rstrip("tokenizer")
-        self.source = encode_file(
-            tokenizer,
-            os.path.join(data_dir, type_path + ".source"),
-            max_source_length,
-            overwrite_cache=overwrite_cache,
-            prefix=prefix,
-            tok_name=tok_name,
-        )
-        tgt_path = os.path.join(data_dir, type_path + ".target")
-        self.target = encode_file(
-            tokenizer, tgt_path, max_target_length, overwrite_cache=overwrite_cache, tok_name=tok_name
-        )
-        if n_obs is not None:
-            self.source = self.source[:n_obs]
-            self.target = self.target[:n_obs]
-        self.pad_token_id = tokenizer.pad_token_id
+        self.tokenizer = tokenizer
+        self.source = encode_file(tokenizer, os.path.join(data_dir, type_path + ".source"), max_source_length)
+        self.target = encode_file(tokenizer, os.path.join(data_dir, type_path + ".target"), max_target_length)
 
     def __len__(self):
         return len(self.source)
@@ -110,141 +47,19 @@ class SummarizationDataset(Dataset):
         source_ids = self.source[index]["input_ids"].squeeze()
         target_ids = self.target[index]["input_ids"].squeeze()
         src_mask = self.source[index]["attention_mask"].squeeze()
-        return {"input_ids": source_ids, "attention_mask": src_mask, "decoder_input_ids": target_ids}
+        return {"source_ids": source_ids, "source_mask": src_mask, "target_ids": target_ids}
 
     @staticmethod
     def trim_seq2seq_batch(batch, pad_token_id):
-        y = trim_batch(batch["decoder_input_ids"], pad_token_id)
-        source_ids, source_mask = trim_batch(batch["input_ids"], pad_token_id, attention_mask=batch["attention_mask"])
+        y = trim_batch(batch["target_ids"], pad_token_id)
+        source_ids, source_mask = trim_batch(batch["source_ids"], pad_token_id, attention_mask=batch["source_mask"])
         return source_ids, source_mask, y
 
-    def collate_fn(self, batch) -> dict:
-        input_ids = torch.stack([x["input_ids"] for x in batch])
-        masks = torch.stack([x["attention_mask"] for x in batch])
-        target_ids = torch.stack([x["decoder_input_ids"] for x in batch])
-        pad_token_id = self.pad_token_id
+    def collate_fn(self, batch):
+        input_ids = torch.stack([x["source_ids"] for x in batch])
+        masks = torch.stack([x["source_mask"] for x in batch])
+        target_ids = torch.stack([x["target_ids"] for x in batch])
+        pad_token_id = self.tokenizer.pad_token_id
         y = trim_batch(target_ids, pad_token_id)
         source_ids, source_mask = trim_batch(input_ids, pad_token_id, attention_mask=masks)
-        batch = {"input_ids": source_ids, "attention_mask": source_mask, "decoder_input_ids": y}
-        return batch
-
-    @property
-    def src_lens(self):  # Can delete?
-        return lmap(len, self.source)
-
-    @property
-    def tgt_lens(self):
-        return lmap(len, self.target)
-
-    def make_sortish_sampler(self, batch_size):
-        return SortishSampler(self.source, batch_size)
-
-
-class SortishSampler(Sampler):
-    "Go through the text data by order of src length with a bit of randomness. From fastai repo."
-
-    def __init__(self, data, batch_size):
-        self.data, self.bs = data, batch_size
-
-    def key(self, i):
-        return len(self.data[i])
-
-    def __len__(self) -> int:
-        return len(self.data)
-
-    def __iter__(self):
-        idxs = np.random.permutation(len(self.data))
-        sz = self.bs * 50
-        ck_idx = [idxs[i : i + sz] for i in range(0, len(idxs), sz)]
-        sort_idx = np.concatenate([sorted(s, key=self.key, reverse=True) for s in ck_idx])
-        sz = self.bs
-        ck_idx = [sort_idx[i : i + sz] for i in range(0, len(sort_idx), sz)]
-        max_ck = np.argmax([self.key(ck[0]) for ck in ck_idx])  # find the chunk with the largest key,
-        ck_idx[0], ck_idx[max_ck] = ck_idx[max_ck], ck_idx[0]  # then make sure it goes first.
-        sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=np.int)
-        sort_idx = np.concatenate((ck_idx[0], sort_idx))
-        return iter(sort_idx)
-
-
-def use_task_specific_params(model, task):
-    # update config with summarization specific params
-    task_specific_params = model.config.task_specific_params
-    if task_specific_params is not None:
-        model.config.update(task_specific_params.get(task, {}))
-
-
-def pickle_load(path):
-    """pickle.load(path)"""
-    with open(path, "rb") as f:
-        return pickle.load(f)
-
-
-def pickle_save(obj, path):
-    """pickle.dump(obj, path)"""
-    with open(path, "wb") as f:
-        return pickle.dump(obj, f)
-
-
-def flatten_list(summary_ids: List[List]):
-    return [x for x in itertools.chain.from_iterable(summary_ids)]
-
-
-def save_git_info(folder_path: str):
-    """
-    Log commit info.
-    """
-    repo_infos = get_git_info()
-
-    with open(os.path.join(folder_path, "git_log.json"), "w") as f:
-        json.dump(repo_infos, f, indent=4)
-
-
-def get_git_info():
-    repo = git.Repo(search_parent_directories=True)
-    repo_infos = {
-        "repo_id": str(repo),
-        "repo_sha": str(repo.head.object.hexsha),
-        "repo_branch": str(repo.active_branch),
-    }
-    return repo_infos
-
-
-ROUGE_KEYS = ["rouge1", "rouge2", "rougeL"]
-
-
-def calculate_rouge(output_lns: List[str], reference_lns: List[str]) -> Dict:
-    scorer = rouge_scorer.RougeScorer(ROUGE_KEYS, use_stemmer=True)
-    aggregator = scoring.BootstrapAggregator()
-
-    for reference_ln, output_ln in zip(reference_lns, output_lns):
-        scores = scorer.score(reference_ln, output_ln)
-        aggregator.add_scores(scores)
-
-    result = aggregator.aggregate()
-    return {k: v.mid.fmeasure for k, v in result.items()}
-
-
-def freeze_params(model: nn.Module):
-    for par in model.parameters():
-        par.requires_grad = False
-
-
-def grad_status(model: nn.Module) -> Iterable:
-    return (par.requires_grad for par in model.parameters())
-
-
-def any_requires_grad(model: nn.Module) -> bool:
-    return any(grad_status(model))
-
-
-def assert_all_frozen(model):
-    model_grads: List[bool] = list(grad_status(model))
-    n_require_grad = sum(lmap(int, model_grads))
-    npars = len(model_grads)
-    assert not any(model_grads), f"{n_require_grad/npars:.1%} of {npars} weights require grad"
-
-
-def assert_not_all_frozen(model):
-    model_grads: List[bool] = list(grad_status(model))
-    npars = len(model_grads)
-    assert any(model_grads), f"none of {npars} weights require grad"
+        return {"source_ids": source_ids, "source_mask": source_mask, "target_ids": y}

examples/test_examples.py

@@ -55,7 +55,7 @@ class ExamplesTests(unittest.TestCase):
 
         testargs = """
             run_glue.py
-            --model_name_or_path distilbert-base-uncased
+            --model_name_or_path bert-base-uncased
             --data_dir ./tests/fixtures/tests_samples/MRPC/
             --task_name mrpc
             --do_train
@@ -79,7 +79,6 @@ class ExamplesTests(unittest.TestCase):
     def test_run_language_modeling(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
-        # TODO: switch to smaller model like sshleifer/tiny-distilroberta-base
 
         testargs = """
             run_language_modeling.py
@@ -106,9 +105,10 @@ class ExamplesTests(unittest.TestCase):
 
         testargs = """
             run_squad.py
-            --model_type=distilbert
-            --model_name_or_path=sshleifer/tiny-distilbert-base-cased-distilled-squad
+            --model_type=bert
+            --model_name_or_path=bert-base-uncased
             --data_dir=./tests/fixtures/tests_samples/SQUAD
+            --model_name=bert-base-uncased
             --output_dir=./tests/fixtures/tests_samples/temp_dir
             --max_steps=10
             --warmup_steps=2
@@ -123,15 +123,15 @@ class ExamplesTests(unittest.TestCase):
         """.split()
         with patch.object(sys, "argv", testargs):
             result = run_squad.main()
-            self.assertGreaterEqual(result["f1"], 25)
-            self.assertGreaterEqual(result["exact"], 21)
+            self.assertGreaterEqual(result["f1"], 30)
+            self.assertGreaterEqual(result["exact"], 30)
 
     def test_generation(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
 
         testargs = ["run_generation.py", "--prompt=Hello", "--length=10", "--seed=42"]
-        model_type, model_name = ("--model_type=gpt2", "--model_name_or_path=sshleifer/tiny-gpt2")
+        model_type, model_name = ("--model_type=openai-gpt", "--model_name_or_path=openai-gpt")
         with patch.object(sys, "argv", testargs + [model_type, model_name]):
             result = run_generation.main()
             self.assertGreaterEqual(len(result[0]), 10)

examples/text-classification/README.md

@@ -52,12 +52,9 @@ Some of these results are significantly different from the ones reported on the
 of GLUE benchmark on the website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the webite.
 
 Before running any one of these GLUE tasks you should download the
-[GLUE data](https://gluebenchmark.com/tasks) by running the following lines at the root of the repo
-```
-python utils/download_glue_data.py --data_dir /path/to/glue --tasks all
-```
-
-after replacing *path/to/glue* with a value that you like. Then you can run
+[GLUE data](https://gluebenchmark.com/tasks) by running
+[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
+and unpack it to some directory `$GLUE_DIR`.
 
 ```bash
 export GLUE_DIR=/path/to/glue

examples/text-classification/run_nlp_glue.py

@@ -16,20 +16,21 @@
 """ Finetuning the library models for sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa, Albert, XLM-RoBERTa)."""
 
 
+import dataclasses
 import logging
 import os
 import sys
 from dataclasses import dataclass, field
 from typing import Callable, Dict, Optional
 
-import nlp
 import numpy as np
 
+import torch
+
+import nlp
+
+from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, EvalPrediction
 from transformers import (
-    AutoConfig,
-    AutoModelForSequenceClassification,
-    AutoTokenizer,
-    EvalPrediction,
     HfArgumentParser,
     Trainer,
     TrainingArguments,
@@ -51,12 +52,8 @@ class GlueDataTrainingArguments:
     the command line.
     """
 
-    task_name: str = field(
-        metadata={
-            "help": "The name of the task to train and/or evaluate on: "
-            "['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax']"
-        }
-    )
+    task_name: str = field(metadata={"help": "The name of the task to train and/or evaluate on: "
+        "['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax']"})
 
     max_seq_length: int = field(
         default=128,
@@ -67,7 +64,7 @@ class GlueDataTrainingArguments:
     )
 
     def __post_init__(self):
-        self.task_name = self.task_name.lower().replace("-", "")  # We used to allow 'sts-b' for 'stsb'
+        self.task_name = self.task_name.lower().replace('-', '')  # We used to allow 'sts-b' for 'stsb'
 
 
 @dataclass
@@ -141,64 +138,45 @@ def main():
     )
 
     # Download, tokenize and prepare datasets for training a PyTorch model
-    task_name = data_args.task_name.replace("-", "")  # We used to allow 'sts-b' for 'stsb'
-    glue = nlp.load_dataset("glue", name=task_name)
-
-    def tokenize(batch):
-        """ Tokenize the dataset and:
-            - ``return_lengths=True`` => add a column with the length of the encoded sequences
-            - ``truncate=True`` => Truncate to the max input length of the model
-        """
-        return tokenizer(batch["sentence1"], batch["sentence2"], return_lengths=True, truncate=True)
-
-    def batch_dataset(batch):
-        """ Pad a batch to the length of the longest sequence in the batch (``padding=True``)
-            And then group the batch in a single example so the sequences will stay together
-        """
-        padded_batch = tokenizer.pad(batch, padding=True)
-        padded_batch = {key: [array] for key, array in padded_batch.items()}
-        return padded_batch
+    task_name = data_args.task_name.replace('-', '')  # We used to allow 'sts-b' for 'stsb'
+    glue = nlp.load_dataset('glue', name=task_name)
 
+    def tokenize(example):
+        return tokenizer.batch_encode_plus(list(zip(example['sentence1'], example['sentence2'])),
+                                           max_length=data_args.max_seq_length,
+                                           pad_to_max_length=True)
     for split_name, split in glue.items():
-        split = split.map(
-            tokenize, batched=True
-        )  # Tokenize the dataset (adds columns to the dataset w. tokenized inputs)
-        split = split.sort("length")  # Sort the dataset by length
-
-        # Build batches by gathering sequences of similar lengths
-        batch_size = training_args.train_batch_size if split_name == "train" else training_args.eval_batch_size
-        split = split.map(batch_dataset, batched=True, batch_size=batch_size)
-
-        # Set the format of the dataset to output only the columns our model can digest
-        glue[split_name].set_format(columns=["input_ids", "attention_mask", "token_type_ids", "label"])
+        # Tokenize the dataset (this adds columns to the dataset)
+        glue[split_name] = split.map(tokenize, batched=True)
+        # Set the format of the dataset to output only the column our model can digest
+        glue[split_name].set_format(columns=['input_ids', 'attention_mask', 'token_type_ids', 'label'])
 
     # Get the splits
     train_dataset, eval_dataset, test_dataset = None, None, None
     if training_args.do_train:
-        train_dataset = glue["train"]
+        train_dataset = glue['train']
     if training_args.do_eval:
-        eval_dataset = glue["validation" + ("_matched" if task_name == "mnli" else "")]
+        eval_dataset = glue['validation' + ('_matched' if task_name == 'mnli' else '')]
     if training_args.do_predict:
-        test_dataset = glue["test" + ("_matched" if task_name == "mnli" else "")]
+        test_dataset = glue['test' + ('_matched' if task_name == 'mnli' else '')]
 
     # Define output mode (regression or classification) and num labels (to define the size of the last layer of the model)
-    output_mode = "regression" if "float" in glue["train"].features["label"].dtype else "classification"
+    output_mode = "regression" if 'float' in glue['train'].features['label'].dtype else "classification"
     if output_mode == "regression":
         num_labels = 1
     else:
-        num_labels = len(train_dataset.unique("label"))
+        num_labels = len(train_dataset.unique('label'))
+
 
     if task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
         "RobertaTokenizer",
         "RobertaTokenizerFast",
         "XLMRobertaTokenizer",
     ):
-        raise NotImplementedError(
-            """
+        raise NotImplementedError("""
         # HACK(label indices are swapped in RoBERTa pretrained model)
         label_list[1], label_list[2] = label_list[2], label_list[1]
-        """
-        )
+        """)
 
     # Load pretrained model and tokenizer
     #

examples/text-classification/run_pl_glue.py

@@ -108,7 +108,7 @@ class GLUETransformer(BaseTransformer):
 
         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
 
-    def _eval_end(self, outputs) -> tuple:
+    def _eval_end(self, outputs):
         val_loss_mean = torch.stack([x["val_loss"] for x in outputs]).mean().detach().cpu().item()
         preds = np.concatenate([x["pred"] for x in outputs], axis=0)
 
@@ -132,14 +132,20 @@ class GLUETransformer(BaseTransformer):
         logs = ret["log"]
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
 
-    def test_epoch_end(self, outputs) -> dict:
+    def test_epoch_end(self, outputs):
+        # updating to test_epoch_end instead of deprecated test_end
         ret, predictions, targets = self._eval_end(outputs)
+
+        # Converting to the dic required by pl
+        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
+        # pytorch_lightning/trainer/logging.py#L139
         logs = ret["log"]
         # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
         return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
 
     @staticmethod
     def add_model_specific_args(parser, root_dir):
+        # Add NER specific options
         BaseTransformer.add_model_specific_args(parser, root_dir)
         parser.add_argument(
             "--max_seq_length",

model_cards/a-ware/bart-squadv2/README.md

@@ -1,76 +0,0 @@
----
-datasets:
-- squad_v2
----
-
-# BART-LARGE finetuned on SQuADv2
-
-This is bart-large model finetuned on SQuADv2 dataset for question answering task
-
-## Model details
-BART was propsed in the [paper](https://arxiv.org/abs/1910.13461) **BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension**.
-BART is a seq2seq model intended for both NLG and NLU tasks. 
-
-To use BART for question answering tasks, we feed the complete document into the encoder and decoder, and use the top
-hidden state of the decoder as a representation for each
-word. This representation is used to classify the token. As given in the paper bart-large achives comparable to ROBERTa on SQuAD.
-Another notable thing about BART is that it can handle sequences with upto 1024 tokens.
-
-| Param               | #Value |
-|---------------------|--------|
-| encoder layers      | 12     |
-| decoder layers      | 12     |
-| hidden size         | 4096   |
-| num attetion heads  | 16     |
-| on disk size        | 1.63GB |
-
-
-## Model training
-This model was trained with following parameters using simpletransformers wrapper:
-```
-train_args = {
-    'learning_rate': 1e-5,
-    'max_seq_length': 512,
-    'doc_stride': 512,
-    'overwrite_output_dir': True,
-    'reprocess_input_data': False,
-    'train_batch_size': 8,
-    'num_train_epochs': 2,
-    'gradient_accumulation_steps': 2,
-    'no_cache': True,
-    'use_cached_eval_features': False,
-    'save_model_every_epoch': False,
-    'output_dir': "bart-squadv2",
-    'eval_batch_size': 32,
-    'fp16_opt_level': 'O2',
-    }
-```
-
-[You can even train your own model using this colab notebook](https://colab.research.google.com/drive/1I5cK1M_0dLaf5xoewh6swcm5nAInfwHy?usp=sharing)
-
-## Results
-```{"correct": 6832, "similar": 4409, "incorrect": 632, "eval_loss": -14.950117511952177}```
-
-## Model in Action  🚀
-```python3
-from transformers import BartTokenizer, BartForQuestionAnswering
-import torch
-
-tokenizer = BartTokenizer.from_pretrained('a-ware/bart-squadv2')
-model = BartForQuestionAnswering.from_pretrained('a-ware/bart-squadv2')
-
-question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
-encoding = tokenizer.encode_plus(question, text, return_tensors='pt')
-input_ids = encoding['input_ids']
-attention_mask = encoding['attention_mask']
-
-start_scores, end_scores = model(input_ids, attention_mask=attention_mask, output_attentions=False)[:2]
-
-all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
-answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])
-answer = tokenizer.convert_tokens_to_ids(answer.split())
-answer = tokenizer.decode(answer)
-#answer => 'a nice puppet' 
-```
-
-> Created with ❤️ by A-ware UG [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/aware-ai)

model_cards/a-ware/roberta-large-squad-classification/README.md

@@ -1,48 +0,0 @@
----
-datasets:
-- squad_v2
----
-
-# Roberta-LARGE finetuned on SQuADv2
-
-This is roberta-large model finetuned on SQuADv2 dataset for question answering answerability classification
-
-## Model details
-This model is simply an Sequenceclassification model with two inputs (context and question) in a list.
-The result is either [1] for answerable or [0] if it is not answerable.
-It was trained over 4 epochs on squadv2 dataset and can be used to filter out which context is good to give into the QA model to avoid bad answers.
-
-## Model training
-This model was trained with following parameters using simpletransformers wrapper:
-```
-train_args = {
-    'learning_rate': 1e-5,
-    'max_seq_length': 512,
-    'overwrite_output_dir': True,
-    'reprocess_input_data': False,
-    'train_batch_size': 4,
-    'num_train_epochs': 4,
-    'gradient_accumulation_steps': 2,
-    'no_cache': True,
-    'use_cached_eval_features': False,
-    'save_model_every_epoch': False,
-    'output_dir': "bart-squadv2",
-    'eval_batch_size': 8,
-    'fp16_opt_level': 'O2',
-    }
-```
-
-## Results
-```{"accuracy": 90.48%}```
-## Model in Action  🚀
-```python3
-from simpletransformers.classification import ClassificationModel
-
-model = ClassificationModel('roberta', 'a-ware/roberta-large-squadv2', num_labels=2, args=train_args)
-
-predictions, raw_outputs = model.predict([["my dog is an year old. he loves to go into the rain", "how old is my dog ?"]])
-print(predictions)
-==> [1]
-```
-
-> Created with ❤️ by A-ware UG [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/aware-ai)

model_cards/a-ware/xlmroberta-squadv2/README.md

@@ -1,59 +0,0 @@
----
-datasets:
-- squad_v2
----
-
-# XLM-ROBERTA-LARGE finetuned on SQuADv2
-
-This is xlm-roberta-large model finetuned on SQuADv2 dataset for question answering task
-
-## Model details
-XLM-Roberta was propsed in the [paper](https://arxiv.org/pdf/1911.02116.pdf) **XLM-R: State-of-the-art cross-lingual understanding through self-supervision
-
-## Model training
-This model was trained with following parameters using simpletransformers wrapper:
-```
-train_args = {
-    'learning_rate': 1e-5,
-    'max_seq_length': 512,
-    'doc_stride': 512,
-    'overwrite_output_dir': True,
-    'reprocess_input_data': False,
-    'train_batch_size': 8,
-    'num_train_epochs': 2,
-    'gradient_accumulation_steps': 2,
-    'no_cache': True,
-    'use_cached_eval_features': False,
-    'save_model_every_epoch': False,
-    'output_dir': "bart-squadv2",
-    'eval_batch_size': 32,
-    'fp16_opt_level': 'O2',
-    }
-```
-
-## Results
-```{"correct": 6961, "similar": 4359, "incorrect": 553, "eval_loss": -12.177856394381962}```
-
-## Model in Action  🚀
-```python3
-from transformers import XLMRobertaTokenizer, XLMRobertaForQuestionAnswering
-import torch
-
-tokenizer = XLMRobertaTokenizer.from_pretrained('a-ware/xlmroberta-squadv2')
-model = XLMRobertaForQuestionAnswering.from_pretrained('a-ware/xlmroberta-squadv2')
-
-question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
-encoding = tokenizer.encode_plus(question, text, return_tensors='pt')
-input_ids = encoding['input_ids']
-attention_mask = encoding['attention_mask']
-
-start_scores, end_scores = model(input_ids, attention_mask=attention_mask, output_attentions=False)[:2]
-
-all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
-answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])
-answer = tokenizer.convert_tokens_to_ids(answer.split())
-answer = tokenizer.decode(answer)
-#answer => 'a nice puppet' 
-```
-
-> Created with ❤️ by A-ware UG [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/aware-ai)

model_cards/ahotrod/electra_large_discriminator_squad2_512/README.md

@@ -1,58 +0,0 @@
-## ELECTRA_large_discriminator language model fine-tuned on SQuAD2.0
-
-### with the following results:
-
-```
-  "exact": 87.09677419354838,
-  "f1": 89.98343832723452,
-  "total": 11873,
-  "HasAns_exact": 84.66599190283401,
-  "HasAns_f1": 90.44759839056285,
-  "HasAns_total": 5928,
-  "NoAns_exact": 89.52060555088309,
-  "NoAns_f1": 89.52060555088309,
-  "NoAns_total": 5945,
-  "best_exact": 87.09677419354838,
-  "best_exact_thresh": 0.0,
-  "best_f1": 89.98343832723432,
-  "best_f1_thresh": 0.0
-```
-### from script:
-```
-python ${EXAMPLES}/run_squad.py \
-  --model_type electra \
-  --model_name_or_path google/electra-large-discriminator \
-  --do_train \
-  --do_eval \
-  --train_file ${SQUAD}/train-v2.0.json \
-  --predict_file ${SQUAD}/dev-v2.0.json \
-  --version_2_with_negative \
-  --do_lower_case \
-  --num_train_epochs 3 \
-  --warmup_steps 306 \
-  --weight_decay 0.01 \
-  --learning_rate 3e-5 \
-  --max_grad_norm 0.5 \
-  --adam_epsilon 1e-6 \
-  --max_seq_length 512 \
-  --doc_stride 128 \
-  --per_gpu_train_batch_size 8 \
-  --gradient_accumulation_steps 16 \
-  --per_gpu_eval_batch_size 128 \
-  --fp16 \
-  --fp16_opt_level O1 \
-  --threads 12 \
-  --logging_steps 50 \
-  --save_steps 1000 \
-  --overwrite_output_dir \
-  --output_dir ${MODEL_PATH}
-```
-### using the following system & software:
-```
-Transformers: 2.11.0
-PyTorch: 1.5.0
-TensorFlow: 2.2.0
-Python: 3.8.1
-OS/Platform: Linux-5.3.0-59-generic-x86_64-with-glibc2.10
-CPU/GPU: Intel i9-9900K / NVIDIA Titan RTX 24GB
-```

model_cards/aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616/README.md

@@ -1,20 +0,0 @@
-# BERT L-10 H-512 fine-tuned on MLM (CORD-19 2020/06/16)
-
-BERT model with [10 Transformer layers and hidden embedding of size 512](https://huggingface.co/google/bert_uncased_L-10_H-512_A-8), referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962), fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16).
-
-## Training the model
-
-```bash
-python run_language_modeling.py
-    --model_type bert
-    --model_name_or_path google/bert_uncased_L-10_H-512_A-8
-    --do_train
-    --train_data_file {cord19-200616-dataset}
-    --mlm
-    --mlm_probability 0.2
-    --line_by_line
-    --block_size 512
-    --per_device_train_batch_size 10
-    --learning_rate 3e-5
-    --num_train_epochs 2
-    --output_dir bert_uncased_L-10_H-512_A-8_cord19-200616

model_cards/aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2/README.md

@@ -1,26 +0,0 @@
----
-datasets:
-- squad_v2
----
-
-# BERT L-10 H-512 CORD-19 (2020/06/16) fine-tuned on SQuAD v2.0
-
-BERT model with [10 Transformer layers and hidden embedding of size 512](https://huggingface.co/google/bert_uncased_L-10_H-512_A-8), referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962), [fine-tuned for MLM](https://huggingface.co/aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616) on CORD-19 dataset (as released on 2020/06/16) and fine-tuned for QA on SQuAD v2.0.
-
-## Training the model
-
-```bash
-python run_squad.py
-    --model_type bert
-    --model_name_or_path aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616
-    --train_file 'train-v2.0.json'
-    --predict_file 'dev-v2.0.json'
-    --do_train
-    --do_eval
-    --do_lower_case
-    --version_2_with_negative
-    --max_seq_length 384
-    --per_gpu_train_batch_size 10
-    --learning_rate 3e-5
-    --num_train_epochs 2
-    --output_dir bert_uncased_L-10_H-512_A-8_cord19-200616_squad2

model_cards/aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616/README.md

@@ -1,20 +0,0 @@
-# BERT L-2 H-512 fine-tuned on MLM (CORD-19 2020/06/16)
-
-BERT model with [2 Transformer layers and hidden embedding of size 512](https://huggingface.co/google/bert_uncased_L-2_H-512_A-8), referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962), fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16).
-
-## Training the model
-
-```bash
-python run_language_modeling.py
-    --model_type bert
-    --model_name_or_path google/bert_uncased_L-2_H-512_A-8
-    --do_train
-    --train_data_file {cord19-200616-dataset}
-    --mlm
-    --mlm_probability 0.2
-    --line_by_line
-    --block_size 512
-    --per_device_train_batch_size 20
-    --learning_rate 3e-5
-    --num_train_epochs 2
-    --output_dir bert_uncased_L-2_H-512_A-8_cord19-200616

model_cards/aodiniz/bert_uncased_L-4_H-256_A-4_cord19-200616/README.md

@@ -1,20 +0,0 @@
-# BERT L-4 H-256 fine-tuned on MLM (CORD-19 2020/06/16)
-
-BERT model with [4 Transformer layers and hidden embedding of size 256](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4), referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962), fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16).
-
-## Training the model
-
-```bash
-python run_language_modeling.py
-    --model_type bert
-    --model_name_or_path google/bert_uncased_L-4_H-256_A-4
-    --do_train
-    --train_data_file {cord19-200616-dataset}
-    --mlm
-    --mlm_probability 0.2
-    --line_by_line
-    --block_size 256
-    --per_device_train_batch_size 20
-    --learning_rate 3e-5
-    --num_train_epochs 2
-    --output_dir bert_uncased_L-4_H-256_A-4_cord19-200616

model_cards/canwenxu/BERT-of-Theseus-MNLI/README.md

@@ -20,5 +20,3 @@ We provide a 6-layer pretrained model on MNLI as a general-purpose model, which
 | BERT-base       | 83.5 | 89.5 | 91.2 | 89.8 | 71.1 | 91.5  | 88.9  |
 | DistillBERT     | 79.0 | 87.5 | 85.3 | 84.9 | 59.9 | 90.7  | 81.2  |
 | BERT-of-Theseus | 82.1 | 87.5 | 88.8 | 88.8 | 70.1 | 91.8  | 87.8  |
-
-Please Note: this checkpoint is for [Intermediate-Task Transfer Learning](https://arxiv.org/abs/2005.00628) so it does not include the classification head for MNLI! Please fine-tune it before use (like DistilBERT).

model_cards/deepset/roberta-base-squad2-covid/README.md

@@ -4,7 +4,7 @@
 **Language model:** deepset/roberta-base-squad2  
 **Language:** English  
 **Downstream-task:** Extractive QA  
-**Training data:** [SQuAD-style CORD-19 annotations from 23rd April](https://github.com/deepset-ai/COVID-QA/blob/master/data/question-answering/200423_covidQA.json)  
+**Training data:** [SQuAD-style CORD-19 annotations](https://github.com/deepset-ai/COVID-QA/tree/master/data/question-answering)  
 **Code:**  See [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering_crossvalidation.py) in [FARM](https://github.com/deepset-ai/FARM)  
 **Infrastructure**: Tesla v100
 

model_cards/distilbert-base-uncased-distilled-squad-README.md

@@ -1,4 +0,0 @@
----
-datasets:
-- squad
----

model_cards/fran-martinez/scibert_scivocab_cased_ner_jnlpba/README.md

@@ -1,161 +0,0 @@
----
-language: scientific english
----
-
-# SciBERT finetuned on JNLPA for NER downstream task
-## Language Model
- [SciBERT](https://arxiv.org/pdf/1903.10676.pdf) is a pretrained language model based on BERT and trained by the 
- [Allen Institute for AI](https://allenai.org/) on papers from the corpus of 
- [Semantic Scholar](https://www.semanticscholar.org/). 
- Corpus size is 1.14M papers, 3.1B tokens. SciBERT has its own vocabulary (scivocab) that's built to best match 
- the training corpus.
- 
-## Downstream task
-[`allenai/scibert_scivocab_cased`](https://huggingface.co/allenai/scibert_scivocab_cased#) has been finetuned for Named Entity 
-Recognition (NER) dowstream task. The code to train the NER can be found [here](https://github.com/fran-martinez/bio_ner_bert).
-
-### Data
-The corpus used to fine-tune the NER is [BioNLP / JNLPBA shared task](http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004).
-
-- Training data consist of 2,000 PubMed abstracts with term/word annotation. This corresponds to 18,546 samples (senteces).
-- Evaluation data consist of 404 PubMed abstracts with term/word annotation. This corresponds to 3,856 samples (sentences).
-
-The classes (at word level) and its distribution (number of examples for each class) for training and evaluation datasets are shown below:
- 
-| Class Label         | # training examples| # evaluation examples|
-|:--------------|--------------:|----------------:|
-|O              |   382,963     |     81,647      |
-|B-protein      |    30,269     |      5,067      |
-|I-protein      |    24,848     |      4,774      |
-|B-cell_type    |     6,718     |      1,921      |
-|I-cell_type    |     8,748     |      2,991      |
-|B-DNA          |     9,533     |      1,056      |
-|I-DNA          |    15,774     |      1,789      |
-|B-cell_line    |     3,830     |        500      |
-|I-cell_line    |     7,387     |       9,89      |
-|B-RNA          |       951     |        118      |
-|I-RNA          |     1,530     |        187      |
-
-### Model
-An exhaustive hyperparameter search was done.
-The hyperparameters that provided the best results are:
-
-- Max length sequence: 128
-- Number of epochs: 6
-- Batch size: 32
-- Dropout: 0.3
-- Optimizer: Adam
-
-The used learning rate was 5e-5 with a decreasing linear schedule. A warmup was used at the beggining of the training
-with a ratio of steps equal to 0.1 from the total training steps.
-
-The model from the epoch with the best F1-score was selected, in this case, the model from epoch 5.
-
-
-### Evaluation
-The following table shows the evaluation metrics calculated at span/entity level:
-
-|          |   precision|    recall|  f1-score|   
-|:---------|-----------:|---------:|---------:|
-cell_line   |  0.5205   | 0.7100   | 0.6007   | 
-cell_type   |  0.7736   | 0.7422   | 0.7576   |
-protein     |  0.6953   | 0.8459   | 0.7633   |
-DNA         |  0.6997   | 0.7894   | 0.7419   | 
-RNA         |  0.6985   | 0.8051   | 0.7480   | 
-|           |          |          |
-**micro avg**   |  0.6984   | 0.8076  |  0.7490|
-**macro avg**   | 0.7032   | 0.8076   | 0.7498 |
-
-The macro F1-score is equal to 0.7498, compared to the value provided by the Allen Institute for AI in their
-[paper](https://arxiv.org/pdf/1903.10676.pdf), which is equal to 0.7728. This drop in performance could be due to 
-several reasons, but one hypothesis could be the fact that the authors used an additional conditional random field, 
-while this model uses a regular classification layer with softmax activation on top of SciBERT model.
-
-At word level, this model achieves a precision of 0.7742, a recall of 0.8536 and a F1-score of 0.8093.
-
-### Model usage in inference
-Use the pipeline:
-````python
-from transformers import pipeline
-
-text = "Mouse thymus was used as a source of glucocorticoid receptor from normal CS lymphocytes."
-
-nlp_ner = pipeline("ner",
-                   model='fran-martinez/scibert_scivocab_cased_ner_jnlpba',
-                   tokenizer='fran-martinez/scibert_scivocab_cased_ner_jnlpba')
-
-nlp_ner(text)
-
-"""
-Output:
----------------------------
-[
-{'word': 'glucocorticoid', 
-'score': 0.9894881248474121, 
-'entity': 'B-protein'}, 
- 
-{'word': 'receptor', 
-'score': 0.989505410194397, 
-'entity': 'I-protein'}, 
-
-{'word': 'normal', 
-'score': 0.7680378556251526, 
-'entity': 'B-cell_type'}, 
-
-{'word': 'cs', 
-'score': 0.5176806449890137, 
-'entity': 'I-cell_type'}, 
-
-{'word': 'lymphocytes', 
-'score': 0.9898491501808167, 
-'entity': 'I-cell_type'}
-]
-"""
-````
-Or load model and tokenizer as follows:
-````python
-import torch
-from transformers import AutoTokenizer, AutoModelForTokenClassification
-
-# Example
-text = "Mouse thymus was used as a source of glucocorticoid receptor from normal CS lymphocytes."
-
-# Load model
-tokenizer = AutoTokenizer.from_pretrained("fran-martinez/scibert_scivocab_cased_ner_jnlpba")
-model = AutoModelForTokenClassification.from_pretrained("fran-martinez/scibert_scivocab_cased_ner_jnlpba")
-
-# Get input for BERT
-input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)
-
-# Predict
-with torch.no_grad():
-  outputs = model(input_ids)
-
-# From the output let's take the first element of the tuple.
-# Then, let's get rid of [CLS] and [SEP] tokens (first and last)
-predictions = outputs[0].argmax(axis=-1)[0][1:-1]
-
-# Map label class indexes to string labels.
-for token, pred in zip(tokenizer.tokenize(text), predictions):
-  print(token, '->', model.config.id2label[pred.numpy().item()])
-
-"""
-Output:
----------------------------
-mouse -> O
-thymus -> O
-was -> O
-used -> O
-as -> O
-a -> O
-source -> O
-of -> O
-glucocorticoid -> B-protein
-receptor -> I-protein
-from -> O
-normal -> B-cell_type
-cs -> I-cell_type
-lymphocytes -> I-cell_type
-. -> O
-"""
-````

model_cards/giganticode/StackOBERTflow-comments-small-v1/README.md

@@ -1,39 +0,0 @@
-# StackOBERTflow-comments-small
-
-StackOBERTflow is a RoBERTa model trained on StackOverflow comments.
-A Byte-level BPE tokenizer with dropout was used (using the `tokenizers` package).
-
-The model is *small*, i.e. has only 6-layers and the maximum sequence length was restricted to 256 tokens. 
-The model was trained for 6 epochs on several GBs of comments from the StackOverflow corpus.
-
-## Quick start: masked language modeling prediction
-
-```python
-from transformers import pipeline
-from pprint import pprint
-
-COMMENT = "You really should not do it this way, I would use <mask> instead."
-
-fill_mask = pipeline(
-    "fill-mask",
-    model="giganticode/StackOBERTflow-comments-small-v1",
-    tokenizer="giganticode/StackOBERTflow-comments-small-v1"
-)
-
-pprint(fill_mask(COMMENT))
-# [{'score': 0.019997311756014824,
-#   'sequence': '<s> You really should not do it this way, I would use jQuery instead.</s>',
-#   'token': 1738},
-#  {'score': 0.01693696901202202,
-#   'sequence': '<s> You really should not do it this way, I would use arrays instead.</s>',
-#   'token': 2844},
-#  {'score': 0.013411642983555794,
-#   'sequence': '<s> You really should not do it this way, I would use CSS instead.</s>',
-#   'token': 2254},
-#  {'score': 0.013224546797573566,
-#   'sequence': '<s> You really should not do it this way, I would use it instead.</s>',
-#   'token': 300},
-#  {'score': 0.011984303593635559,
-#   'sequence': '<s> You really should not do it this way, I would use classes instead.</s>',
-#   'token': 1779}]
-```

model_cards/google/mobilebert-uncased/README.md

@@ -1,32 +0,0 @@
----
-language: english
-thumbnail: https://huggingface.co/front/thumbnails/google.png
-
-license: apache-2.0
----
-
-## MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
-
-MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance
-between self-attentions and feed-forward networks.
-
-This checkpoint is the original MobileBert Optimized Uncased English: 
-[uncased_L-24_H-128_B-512_A-4_F-4_OPT](https://storage.googleapis.com/cloud-tpu-checkpoints/mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT.tar.gz) 
-checkpoint.
-
-## How to use MobileBERT in `transformers`
-
-```python
-from transformers import pipeline
-
-fill_mask = pipeline(
-	"fill-mask",
-	model="google/mobilebert-uncased",
-	tokenizer="google/mobilebert-uncased"
-)
-
-print(
-	fill_mask(f"HuggingFace is creating a {fill_mask.tokenizer.mask_token} that the community uses to solve NLP tasks.")
-)
-
-```

model_cards/google/reformer-crime-and-punishment/README.md

@@ -10,8 +10,8 @@ The ReformerLM model was trained in flax using colab notebook proposed by author
 The model is a language model that operates on small sub-word units. Text can be generated as follows:
 
 ```python
-model = ReformerModelWithLMHead.from_pretrained("google/reformer-crime-and-punishment")
-tok = ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
+model = ReformerModelWithLMHead.from_pretrained("patrickvonplaten/reformer-crime-and-punish")
+tok = ReformerTokenizer.from_pretrained("patrickvonplaten/reformer-crime-and-punish")
 tok.decode(model.generate(tok.encode("A few months later", return_tensors="pt"), do_sample=True,temperature=0.7, max_length=100)[0])
 
 # gives:'A few months later on was more than anything in the flat. 

model_cards/microsoft/MiniLM-L12-H384-uncased/README.md

@@ -1,43 +0,0 @@
----
-thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
-tags:
-- text-classification
-license: mit
----
-
-## MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation
-
-MiniLM is a distilled model from the paper "[MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://arxiv.org/abs/2002.10957)".
-
-Please find the information about preprocessing, training and full details of the MiniLM in the [original MiniLM repository](https://github.com/microsoft/unilm/blob/master/minilm/).
-
-Please note: This checkpoint can be an inplace substitution for BERT and it needs to be fine-tuned before use!
-
-### English Pre-trained Models
-We release the **uncased** **12**-layer model with **384** hidden size distilled from an in-house pre-trained [UniLM v2](/unilm) model in BERT-Base size.
-
-- MiniLMv1-L12-H384-uncased: 12-layer, 384-hidden, 12-heads, 33M parameters, 2.7x faster than BERT-Base
-
-#### Fine-tuning on NLU tasks
-
-We present the dev results on SQuAD 2.0 and several GLUE benchmark tasks.
-
-| Model                                             | #Param | SQuAD 2.0 | MNLI-m | SST-2 | QNLI | CoLA | RTE  | MRPC | QQP  |
-|---------------------------------------------------|--------|-----------|--------|-------|------|------|------|------|------|
-| [BERT-Base](https://arxiv.org/pdf/1810.04805.pdf) | 109M   | 76.8      | 84.5   | 93.2  | 91.7 | 58.9 | 68.6 | 87.3 | 91.3 |
-| **MiniLM-L12xH384**                               | 33M    | 81.7      | 85.7   | 93.0  | 91.5 | 58.5 | 73.3 | 89.5 | 91.3 |
-
-### Citation
-
-If you find MiniLM useful in your research, please cite the following paper:
-
-``` latex
-@misc{wang2020minilm,
-    title={MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers},
-    author={Wenhui Wang and Furu Wei and Li Dong and Hangbo Bao and Nan Yang and Ming Zhou},
-    year={2020},
-    eprint={2002.10957},
-    archivePrefix={arXiv},
-    primaryClass={cs.CL}
-}
-```

model_cards/microsoft/Multilingual-MiniLM-L12-H384/README.md

@@ -1,89 +0,0 @@
----
-thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
-tags:
-- text-classification
-license: mit
----
-
-## MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation
-
-MiniLM is a distilled model from the paper "[MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://arxiv.org/abs/2002.10957)".
-
-Please find the information about preprocessing, training and full details of the MiniLM in the [original MiniLM repository](https://github.com/microsoft/unilm/blob/master/minilm/).
-
-Please note: This checkpoint uses `BertModel` with `XLMRobertaTokenizer` so `AutoTokenizer` won't work with this checkpoint!
-
-### Multilingual Pretrained Model
-- Multilingual-MiniLMv1-L12-H384: 12-layer, 384-hidden, 12-heads, 21M Transformer parameters, 96M embedding parameters
-
-Multilingual MiniLM uses the same tokenizer as XLM-R. But the Transformer architecture of our model is the same as BERT. We provide the fine-tuning code on XNLI based on [huggingface/transformers](https://github.com/huggingface/transformers). Please replace `run_xnli.py` in transformers with [ours](https://github.com/microsoft/unilm/blob/master/minilm/examples/run_xnli.py) to fine-tune multilingual MiniLM.  
-
-We evaluate the multilingual MiniLM on cross-lingual natural language inference benchmark (XNLI) and cross-lingual question answering benchmark (MLQA).
-
-#### Cross-Lingual Natural Language Inference - [XNLI](https://arxiv.org/abs/1809.05053)
-
-We evaluate our model on cross-lingual transfer from English to other languages. Following [Conneau et al. (2019)](https://arxiv.org/abs/1911.02116), we select the best single model on the joint dev set of all the languages.
-
-| Model                                                                                       | #Layers | #Hidden | #Transformer Parameters | Average | en   | fr   | es   | de   | el   | bg   | ru   | tr   | ar   | vi   | th   | zh   | hi   | sw   | ur   |
-|---------------------------------------------------------------------------------------------|---------|---------|-------------------------|---------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|
-| [mBERT](https://github.com/google-research/bert)                                            | 12      | 768     | 85M                     | 66.3    | 82.1 | 73.8 | 74.3 | 71.1 | 66.4 | 68.9 | 69.0 | 61.6 | 64.9 | 69.5 | 55.8 | 69.3 | 60.0 | 50.4 | 58.0 |
-| [XLM-100](https://github.com/facebookresearch/XLM#pretrained-cross-lingual-language-models) | 16      | 1280    | 315M                    | 70.7    | 83.2 | 76.7 | 77.7 | 74.0 | 72.7 | 74.1 | 72.7 | 68.7 | 68.6 | 72.9 | 68.9 | 72.5 | 65.6 | 58.2 | 62.4 |
-| [XLM-R Base](https://arxiv.org/abs/1911.02116)                                              | 12      | 768     | 85M                     | 74.5    | 84.6 | 78.4 | 78.9 | 76.8 | 75.9 | 77.3 | 75.4 | 73.2 | 71.5 | 75.4 | 72.5 | 74.9 | 71.1 | 65.2 | 66.5 |
-| **mMiniLM-L12xH384**                                                                        | 12      | 384     | 21M                     | 71.1    | 81.5 | 74.8 | 75.7 | 72.9 | 73.0 | 74.5 | 71.3 | 69.7 | 68.8 | 72.1 | 67.8 | 70.0 | 66.2 | 63.3 | 64.2 |
-
-This example code fine-tunes **12**-layer multilingual MiniLM on XNLI.
-
-```bash
-# run fine-tuning on XNLI
-DATA_DIR=/{path_of_data}/
-OUTPUT_DIR=/{path_of_fine-tuned_model}/
-MODEL_PATH=/{path_of_pre-trained_model}/
-
-python ./examples/run_xnli.py --model_type minilm \
- --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
- --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
- --tokenizer_name xlm-roberta-base \
- --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
- --do_train \
- --do_eval \
- --max_seq_length 128 \
- --per_gpu_train_batch_size 128 \
- --learning_rate 5e-5 \
- --num_train_epochs 5 \
- --per_gpu_eval_batch_size 32 \
- --weight_decay 0.001 \
- --warmup_steps 500 \
- --save_steps 1500 \
- --logging_steps 1500 \
- --eval_all_checkpoints \
- --language en \
- --fp16 \
- --fp16_opt_level O2
-```
-
-#### Cross-Lingual Question Answering - [MLQA](https://arxiv.org/abs/1910.07475)
-
-Following [Lewis et al. (2019b)](https://arxiv.org/abs/1910.07475), we adopt SQuAD 1.1 as training data and use MLQA English development data for early stopping.
-
-| Model F1 Score                                                                             | #Layers | #Hidden | #Transformer Parameters | Average | en   | es   | de   | ar   | hi   | vi   | zh   |
-|--------------------------------------------------------------------------------------------|---------|---------|-------------------------|---------|------|------|------|------|------|------|------|
-| [mBERT](https://github.com/google-research/bert)                                           | 12      | 768     | 85M                     | 57.7    | 77.7 | 64.3 | 57.9 | 45.7 | 43.8 | 57.1 | 57.5 |
-| [XLM-15](https://github.com/facebookresearch/XLM#pretrained-cross-lingual-language-models) | 12      | 1024    | 151M                    | 61.6    | 74.9 | 68.0 | 62.2 | 54.8 | 48.8 | 61.4 | 61.1 |
-| [XLM-R Base](https://arxiv.org/abs/1911.02116) (Reported)                                  | 12      | 768     | 85M                     | 62.9    | 77.8 | 67.2 | 60.8 | 53.0 | 57.9 | 63.1 | 60.2 |
-| [XLM-R Base](https://arxiv.org/abs/1911.02116) (Our fine-tuned)                            | 12      | 768     | 85M                     | 64.9    | 80.3 | 67.0 | 62.7 | 55.0 | 60.4 | 66.5 | 62.3 |
-| **mMiniLM-L12xH384**                                                                       | 12      | 384     | 21M                     | 63.2    | 79.4 | 66.1 | 61.2 | 54.9 | 58.5 | 63.1 | 59.0 |
-
-### Citation
-
-If you find MiniLM useful in your research, please cite the following paper:
-
-``` latex
-@misc{wang2020minilm,
-    title={MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers},
-    author={Wenhui Wang and Furu Wei and Li Dong and Hangbo Bao and Nan Yang and Ming Zhou},
-    year={2020},
-    eprint={2002.10957},
-    archivePrefix={arXiv},
-    primaryClass={cs.CL}
-}
-```

model_cards/mrm8488/t5-base-finetuned-emotion/README.md

@@ -1,81 +0,0 @@
---
-language: english
----
-
-# T5-base fine-tuned for Emotion Recognition 😂😢😡😃😯
-
-
-[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [emotion recognition](https://github.com/dair-ai/emotion_dataset) dataset for **Emotion Recognition** downstream task.
-
-## Details of T5
-
-The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
-
-Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
-
-![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67)
-
-## Details of the downstream task (Sentiment Recognition) - Dataset 📚
-
-[Elvis Saravia](https://twitter.com/omarsar0) has gathered a great [dataset](https://github.com/dair-ai/emotion_dataset) for emotion recognition. It allows to classifiy the text into one of the following **6** emotions:
-
- - sadness 😢
- - joy 😃
- - love 🥰
- - anger 😡
- - fear 😱
- - surprise 😯
-
-## Model fine-tuning 🏋️‍
-
-The training script is a slightly modified version of [this Colab Notebook](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) created by [Suraj Patil](https://github.com/patil-suraj), so all credits to him!
-
-## Test set metrics 🧾
-
-               |precision | recall  | f1-score |support|
-    |----------|----------|---------|----------|-------|
-    |anger     |      0.93|     0.92|      0.93|    275|
-    |fear      |      0.91|     0.87|      0.89|    224|
-    |joy       |      0.97|     0.94|      0.95|    695|
-    |love      |      0.80|     0.91|      0.85|    159|
-    |sadness   |      0.97|     0.97|      0.97|    521|
-    |surpirse  |      0.73|     0.89|      0.80|     66|
-    |----------|----------|---------|----------|-------|
-    |accuracy|            |         |      0.93|   2000|
-    |macro avg|       0.89|     0.92|      0.90|   2000|
-    |weighted avg|    0.94|     0.93|      0.93|   2000|
-    
-    
-                 
-    
-
-
-
-## Model in Action 🚀
-
-```python
-from transformers import AutoTokenizer, AutoModelWithLMHead
-
-tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-emotion")
-
-model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-emotion")
-
-def get_emotion(text):
-  input_ids = tokenizer.encode(text + '</s>', return_tensors='pt')
-
-  output = model.generate(input_ids=input_ids,
-               max_length=2)
-  
-  dec = [tokenizer.decode(ids) for ids in output]
-  label = dec[0]
-  return label
-  
- get_emotion("i feel as if i havent blogged in ages are at least truly blogged i am doing an update cute") # Output: 'joy'
- 
- get_emotion("i have a feeling i kinda lost my best friend") # Output: 'sadness'
-
-```
-
-> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
-
-> Made with <span style="color: #e25555;">&hearts;</span> in Spain

model_cards/mrm8488/t5-base-finetuned-imdb-sentiment/README.md

@@ -1,68 +0,0 @@
---
-language: english
-datasets:
-- imdb
----
-
-# T5-base fine-tuned for Sentiment Anlalysis 🎞️👍👎
-
-
-[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [IMDB](https://huggingface.co/datasets/imdb) dataset for **Sentiment Analysis** downstream task.
-
-## Details of T5
-
-The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
-
-Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
-
-![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67)
-
-## Details of the downstream task (Sentiment analysis) - Dataset 📚
-
-[IMDB](https://huggingface.co/datasets/imdb)
-
-This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of **25,000** highly polar movie reviews for training, and **25,000** for testing.
-
-## Model fine-tuning 🏋️‍
-
-The training script is a slightly modified version of [this Colab Notebook](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) created by [Suraj Patil](https://github.com/patil-suraj), so all credits to him!
-
-## Test set metrics 🧾
-
-               |precision | recall  | f1-score |support|
-    |----------|----------|---------|----------|-------|
-    |negative  |     0.95 |     0.95|      0.95|  12500|
-    |positive  |     0.95 |     0.95|      0.95|  12500|
-    |----------|----------|---------|----------|-------|
-    |accuracy|            |         |      0.95|  25000|
-    |macro avg|       0.95|     0.95|      0.95|  25000|
-    |weighted avg|    0.95|     0.95|     0.95 |  25000|
-
-
-## Model in Action 🚀
-
-```python
-from transformers import AutoTokenizer, AutoModelWithLMHead
-
-tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-imdb-sentiment")
-
-model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-imdb-sentiment")
-
-def get_sentiment(text):
-  input_ids = tokenizer.encode(text + '</s>', return_tensors='pt')
-
-  output = model.generate(input_ids=input_ids,
-               max_length=2)
-  
-  dec = [tokenizer.decode(ids) for ids in output]
-  label = dec[0]
-  return label
-  
-get_sentiment("I dislike a lot that film")
-
-# Output: 'negative'
-```
-
-> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
-
-> Made with <span style="color: #e25555;">&hearts;</span> in Spain

model_cards/nyu-mll/roberta-base-100M-1/README.md

@@ -1 +0,0 @@
-../roberta_1M_to_1B/README.md

model_cards/nyu-mll/roberta-base-100M-2/README.md

@@ -1 +0,0 @@
-../roberta_1M_to_1B/README.md

model_cards/nyu-mll/roberta-base-100M-3/README.md

@@ -1 +0,0 @@
-../roberta_1M_to_1B/README.md

model_cards/nyu-mll/roberta-base-10M-1/README.md

@@ -1 +0,0 @@
-../roberta_1M_to_1B/README.md

model_cards/nyu-mll/roberta-base-10M-2/README.md

@@ -1 +0,0 @@
-../roberta_1M_to_1B/README.md

model_cards/nyu-mll/roberta-base-10M-3/README.md

@@ -1 +0,0 @@
-../roberta_1M_to_1B/README.md

model_cards/nyu-mll/roberta-base-1B-1/README.md

@@ -1 +0,0 @@
-../roberta_1M_to_1B/README.md

model_cards/nyu-mll/roberta-base-1B-2/README.md

@@ -1 +0,0 @@
-../roberta_1M_to_1B/README.md

model_cards/nyu-mll/roberta-base-1B-3/README.md

@@ -1 +0,0 @@
-../roberta_1M_to_1B/README.md

model_cards/nyu-mll/roberta-med-small-1M-1/README.md

@@ -1 +0,0 @@
-../roberta_1M_to_1B/README.md

model_cards/nyu-mll/roberta-med-small-1M-2/README.md

@@ -1 +0,0 @@
-../roberta_1M_to_1B/README.md

model_cards/nyu-mll/roberta-med-small-1M-3/README.md

@@ -1 +0,0 @@
-../roberta_1M_to_1B/README.md

model_cards/nyu-mll/roberta_1M_to_1B/README.md

@@ -1,49 +0,0 @@
-# RoBERTa Pretrained on Smaller Datasets
-
-We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
-
-### Hyperparameters and Validation Perplexity
-
-The hyperparameters and validation perplexities corresponding to each model are as follows:
-
-| Model Name               | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
-|--------------------------|---------------|------------|-----------|------------|-----------------------|
-| [roberta-base-1B-1][link-roberta-base-1B-1]        | 1B            | BASE       | 100K      | 512        | 3.93                  |
-| [roberta-base-1B-2][link-roberta-base-1B-2]        | 1B            | BASE       | 31K       | 1024       | 4.25                  |
-| [roberta-base-1B-3][link-roberta-base-1B-3]        | 1B            | BASE       | 31K       | 4096       | 3.84                  |
-| [roberta-base-100M-1][link-roberta-base-100M-1]      | 100M          | BASE       | 100K      | 512        | 4.99                  |
-| [roberta-base-100M-2][link-roberta-base-100M-2]      | 100M          | BASE       | 31K       | 1024       | 4.61                  |
-| [roberta-base-100M-3][link-roberta-base-100M-3]      | 100M          | BASE       | 31K       | 512        | 5.02                  |
-| [roberta-base-10M-1][link-roberta-base-10M-1]       | 10M           | BASE       | 10K       | 1024       | 11.31                 |
-| [roberta-base-10M-2][link-roberta-base-10M-2]       | 10M           | BASE       | 10K       | 512        | 10.78                 |
-| [roberta-base-10M-3][link-roberta-base-10M-3]       | 10M           | BASE       | 31K       | 512        | 11.58                 |
-| [roberta-med-small-1M-1][link-roberta-med-small-1M-1]   | 1M            | MED-SMALL  | 100K      | 512        | 153.38                |
-| [roberta-med-small-1M-2][link-roberta-med-small-1M-2]   | 1M            | MED-SMALL  | 10K       | 512        | 134.18                |
-| [roberta-med-small-1M-3][link-roberta-med-small-1M-3]   | 1M            | MED-SMALL  | 31K       | 512        | 139.39                |
-
-The hyperparameters corresponding to model sizes mentioned above are as follows:
-
-| Model Size | L  | AH | HS  | FFN  | P    |
-|------------|----|----|-----|------|------|
-| BASE       | 12 | 12 | 768 | 3072 | 125M |
-| MED-SMALL  | 6  | 8  | 512 | 2048 | 45M  |
-
-(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
-
-For other hyperparameters, we select:
-- Peak Learning rate: 5e-4
-- Warmup Steps: 6% of max steps
-- Dropout: 0.1
-
-[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
-[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
-[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
-[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
-[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
-[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
-[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
-[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
-[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
-[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
-[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
-[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3

notebooks/02-transformers.ipynb

@@ -221,7 +221,7 @@
    },
    "source": [
     "The code you saw in the previous section introduced all the steps required to do simple model invocation.\n",
-    "For more day-to-day usage, transformers provides you higher-level methods which will makes your NLP journey easier.\n",
+    "For more day-to-day usage, transformers provides you higher-level methods which will makes your NLP journey easier\n",
     "Let's improve our previous example"
    ]
   },
@@ -268,7 +268,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As you can see above, the method `encode_plus` provides a convenient way to generate all the required parameters\n",
+    "As you can see above, the methode `encode_plus` provides a convenient way to generate all the required parameters\n",
     "that will go through the model. \n",
     "\n",
     "Moreover, you might have noticed it generated some additional tensors: \n",