boom boom

2020-06-25 02:10:13 +00:00 · 2020-06-24 20:44:27 +00:00 · 2020-06-23 16:10:00 -04:00 · 2020-06-23 14:35:54 -04:00 · 2020-06-19 14:46:51 +00:00 · 2020-06-19 09:43:20 -04:00
588 changed files with 12443 additions and 32290 deletions
@@ -12,11 +12,9 @@ jobs:
            - checkout
            - run: sudo pip install .[sklearn,tf-cpu,torch,testing]
            - run: sudo pip install codecov pytest-cov
-            - run: python -m pytest -n 8 --dist=loadfile -s ./tests/ --cov  | tee output.txt
+            - run: python -m pytest -n 8 --dist=loadfile -s -v ./tests/ --cov
            - run: codecov
-            - store_artifacts:
-                  path: ~/transformers/output.txt
-                  destination: test_output.txt
+
    run_tests_torch:
        working_directory: ~/transformers
        docker:
@@ -28,11 +26,9 @@ jobs:
        steps:
            - checkout
            - run: sudo pip install .[sklearn,torch,testing]
-            - run: python -m pytest -n 8 --dist=loadfile -s ./tests/ | tee output.txt
-            - store_artifacts:
-                  path: ~/transformers/output.txt
-                  destination: test_output.txt
-
+            - run: sudo pip install codecov pytest-cov
+            - run: python -m pytest -n 8 --dist=loadfile -s -v ./tests/ --cov
+            - run: codecov
    run_tests_tf:
        working_directory: ~/transformers
        docker:
@@ -44,10 +40,9 @@ jobs:
        steps:
            - checkout
            - run: sudo pip install .[sklearn,tf-cpu,testing]
-            - run: python -m pytest -n 8 --dist=loadfile -s ./tests/ | tee output.txt
-            - store_artifacts:
-               path: ~/transformers/output.txt
-               destination: test_output.txt
+            - run: sudo pip install codecov pytest-cov
+            - run: python -m pytest -n 8 --dist=loadfile -s -v ./tests/ --cov
+            - run: codecov
    run_tests_custom_tokenizers:
        working_directory: ~/transformers
        docker:
@@ -57,10 +52,7 @@ jobs:
        steps:
            - checkout
            - run: sudo pip install .[mecab,testing]
-            - run: python -m pytest -s ./tests/test_tokenization_bert_japanese.py | tee output.txt
-            - store_artifacts:
-                path: ~/transformers/output.txt
-                destination: test_output.txt
+            - run: python -m pytest -sv ./tests/test_tokenization_bert_japanese.py
    run_examples_torch:
        working_directory: ~/transformers
        docker:
@@ -73,10 +65,7 @@ jobs:
            - checkout
            - run: sudo pip install .[sklearn,torch,testing]
            - run: sudo pip install -r examples/requirements.txt
-            - run: python -m pytest -n 8 --dist=loadfile -s ./examples/ | tee output.txt
-            - store_artifacts:
-                  path: ~/transformers/output.txt
-                  destination: test_output.txt
+            - run: python -m pytest -n 8 --dist=loadfile -s -v ./examples/
    build_doc:
        working_directory: ~/transformers
        docker:
@@ -5,26 +5,15 @@ function deploy_doc(){
 	git checkout $1
 	if [ ! -z "$2" ]
 	then
-		if [ "$2" == "master" ]; then
-		    echo "Pushing master"
-			make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir/$2/
-			cp -r _build/html/_static .
-		elif ssh -oStrictHostKeyChecking=no $doc "[ -d $dir/$2 ]"; then
+		if [ -d "$dir/$2" ]; then
 			echo "Directory" $2 "already exists"
-			scp -r -oStrictHostKeyChecking=no _static/* $doc:$dir/$2/_static/
 		else
 			echo "Pushing version" $2
-			make clean && make html
-			rm -rf _build/html/_static
-			cp -r _static _build/html
-			scp -r -oStrictHostKeyChecking=no _build/html $doc:$dir/$2
+			make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html $doc:$dir/$2
 		fi
 	else
-		echo "Pushing stable"
-		make clean && make html
-		rm -rf _build/html/_static
-		cp -r _static _build/html
-		scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir
+		echo "Pushing master"
+		make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir
 	fi
 }

@@ -46,5 +35,4 @@ 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
-deploy_doc "1158e56" #v3.0.2 Latest stable release
+deploy_doc "b42586e" #v2.11.0 Latest stable release
@@ -1,6 +1,6 @@
 ---
 name: "❓ Questions & Help"
-about: Post your general questions on the Hugging Face forum or Stack Overflow tagged huggingface-transformers
+about: Post your general questions on Stack Overflow tagged huggingface-transformers
 title: ''
 labels: ''
 assignees: ''
@@ -11,17 +11,19 @@ assignees: ''

 <!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
     new models and benchmarks, and migration questions. For all other questions,
-     we direct you to the Hugging Face forum: https://discuss.huggingface.co/ .
-     You can also try Stack Overflow (SO) where a whole community of PyTorch and
-     Tensorflow enthusiast can help you out. In this case, make sure to tag your
-     question with the right deep learning framework as well as the
-     huggingface-transformers tag: 
+     we direct you to Stack Overflow (SO) where a whole community of PyTorch and
+     Tensorflow enthusiast can help you out. Make sure to tag your question with the
+     right deep learning framework as well as the huggingface-transformers tag: 
     https://stackoverflow.com/questions/tagged/huggingface-transformers 
+     
+     If your question wasn't answered after a period of time on Stack Overflow, you
+     can always open a question on GitHub. You should then link to the SO question 
+     that you posted.
     -->

 ## Details
 <!-- Description of your issue -->

-<!-- You should first ask your question on the forum or SO, and only if
+<!-- You should first ask your question on SO, and only if
     you didn't get an answer ask it here on GitHub. -->
-**A link to original question on Stack Overflow**:
+**A link to original question on Stack Overflow**:
@@ -51,4 +51,4 @@ jobs:
        USE_CUDA: yes
      run: |
        source .env/bin/activate
-        python -m pytest -n 2 --dist=loadfile -s ./tests/
+        python -m pytest -n 2 --dist=loadfile -s -v ./tests/
@@ -46,14 +46,5 @@ jobs:
        USE_CUDA: yes
      run: |
        source .env/bin/activate
-        python -m pytest -n 1 --dist=loadfile -s ./tests/
-    - name: Run examples tests on GPU
-      env:
-        TF_FORCE_GPU_ALLOW_GROWTH: "true"
-        OMP_NUM_THREADS: 1
-        RUN_SLOW: yes
-        USE_CUDA: yes
-      run: |
-        source .env/bin/activate
-        pip install -r examples/requirements.txt
-        python -m pytest -n 1 --dist=loadfile -s examples
+        python -m pytest -n 1 --dist=loadfile -s -v ./tests/
+        
@@ -66,7 +66,7 @@ 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/tree/master/templates) folder.
+in the [`templates`](https://github.com/huggingface/transformers/templates) folder.

 ### Do you want a new feature (that is not a model)?

@@ -88,7 +88,7 @@ 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/tree/master/templates) 
+models in the library. You can find them in the [`templates`](https://github.com/huggingface/transformers/templates) 
 folder.

 ## Start contributing! (Pull Requests)
@@ -24,7 +24,6 @@

 🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, T5, CTRL...) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over thousands of pretrained models in 100+ languages and deep interoperability between PyTorch & TensorFlow 2.0.

-### Recent contributors
 [![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/0)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/0)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/1)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/1)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/2)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/2)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/3)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/3)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/4)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/4)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/5)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/5)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/6)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/6)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/7)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/7)

 ### Features
@@ -60,7 +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](https://huggingface.co/transformers/) | Full API documentation and more |
+| 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 |

 ## Installation

@@ -288,8 +287,8 @@ pytorch_model = BertForSequenceClassification.from_pretrained('./save/', from_tf
 sentence_0 = "This research was consistent with his findings."
 sentence_1 = "His findings were compatible with this research."
 sentence_2 = "His findings were not compatible with this research."
-inputs_1 = tokenizer(sentence_0, sentence_1, add_special_tokens=True, return_tensors='pt')
-inputs_2 = tokenizer(sentence_0, sentence_2, add_special_tokens=True, return_tensors='pt')
+inputs_1 = tokenizer.encode_plus(sentence_0, sentence_1, add_special_tokens=True, return_tensors='pt')
+inputs_2 = tokenizer.encode_plus(sentence_0, sentence_2, add_special_tokens=True, return_tensors='pt')

 pred_1 = pytorch_model(inputs_1['input_ids'], token_type_ids=inputs_1['token_type_ids'])[0].argmax().item()
 pred_2 = pytorch_model(inputs_2['input_ids'], token_type_ids=inputs_2['token_type_ids'])[0].argmax().item()
@@ -167,7 +167,7 @@ Here's an example showcasing everything so far:

            Indices can be obtained using :class:`transformers.AlbertTokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
-            :func:`transformers.PreTrainedTokenizer.__call__` for details.
+            :func:`transformers.PreTrainedTokenizer.encode_plus` for details.

            `What are input IDs? <../glossary.html#input-ids>`__
 ```
@@ -9,8 +9,4 @@

 .highlight .kn, .highlight .nv, .highlight .s2, .highlight .ow {
    color: #6670FF;
-}
-
-.highlight .gp {
-    color: #FB8D68;
 }
@@ -1,50 +1,9 @@
 /* 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;
    justify-content: flex-end;
-    margin-right: 30px;
 }

 .framework-selector > button {
@@ -61,12 +20,6 @@
    padding: 5px;
 }

-/* Copy button */
-
-a.copybtn {
-    margin: 3px;
-}
-
 /* The literal code blocks */
 .rst-content tt.literal, .rst-content tt.literal, .rst-content code.literal {
    color: #6670FF;
@@ -85,7 +38,6 @@ a.copybtn {

 /* The research field on top of the toc tree */
 .wy-side-nav-search{
-    padding-top: 0;
    background-color: #6670FF;
 }

@@ -1,27 +1,3 @@
-// These two things need to be updated at each release for the version selector.
-// Last stable version
-const stableVersion = "v3.0.2"
-// Dictionary doc folder to label
-const versionMapping = {
-    "master": "master",
-    "": "v3.0.0/v3.0.1/v3.0.2 (stable)",
-    "v2.11.0": "v2.11.0",
-    "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");
@@ -82,74 +58,11 @@ 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;
-    // Index page may not have a last part with filename.html so we need to go up
-    if (parts[parts.length - 1] != "" && ! parts[parts.length - 1].match(/\.html$|^search.html?/)) {
-        versionIndex = parts.length - 1;
-    }
-    // Main classes and models are nested so we need to go deeper
-    else if (parts[versionIndex] == "main_classes" || parts[versionIndex] == "model_doc") {
-        versionIndex = versionIndex - 1;
-    } 
-    const version = parts[versionIndex];
-
-    // Menu with all the links,
-    const versionMenu = document.createElement("div");
-
-    const htmlLines = [];
-    for (const [key, value] of Object.entries(versionMapping)) {
-        let baseUrlIndex = (version == "transformers") ? versionIndex + 1: versionIndex;
-        var urlParts = parts.slice(0, baseUrlIndex);
-        if (key != "") {
-            urlParts = urlParts.concat([key]);
-        }
-        urlParts = urlParts.concat(parts.slice(versionIndex+1));
-        htmlLines.push(`<a href="${urlParts.join('/')}">${value}</a>`);
-    }
-
-    versionMenu.classList.add("version-dropdown");
-    versionMenu.innerHTML = htmlLines.join('\n');
-    
-    // Button for version selection
-    const versionButton = document.createElement("div");
-    versionButton.classList.add("version-button");
-    let label = (version == "transformers") ? stableVersion : version
-    versionButton.innerText = label.concat(" ▼");
-
-    // Toggle the menu when we click on the button
-    versionButton.addEventListener("click", () => {
-        versionMenu.classList.toggle("version-show");
-    });
-
-    // Hide the menu when we click elsewhere
-    window.addEventListener("click", (event) => {
-        if (event.target != versionButton){
-            versionMenu.classList.remove('version-show');
-        }
-    });
-
-    // Container
-    const div = document.createElement("div");
-    div.appendChild(versionButton);
-    div.appendChild(versionMenu);
-    div.style.paddingTop = '25px';
-    div.style.backgroundColor = '#6670FF';
-    div.style.display = 'block';
-    div.style.textAlign = 'center';
-
-    const scrollDiv = document.querySelector(".wy-side-scroll");
-    scrollDiv.insertBefore(div, scrollDiv.children[1]);
-}
-
 function addHfMenu() {
    const div = `
    <div class="menu">
        <a href="/welcome">🔥 Sign in</a>
        <a href="/models">🚀 Models</a>
-        <a href="http://discuss.huggingface.co">💬 Forum</a>
    </div>
    `;
    document.body.insertAdjacentHTML('afterbegin', div);
@@ -159,8 +72,6 @@ function platformToggle() {
    const codeBlocks = Array.from(document.getElementsByClassName("highlight"));
    const pytorchIdentifier = "## PYTORCH CODE";
    const tensorflowIdentifier = "## TENSORFLOW CODE";
-
-    const promptSpanIdentifier = `<span class="gp">&gt;&gt;&gt; </span>`
    const pytorchSpanIdentifier = `<span class="c1">${pytorchIdentifier}</span>`;
    const tensorflowSpanIdentifier = `<span class="c1">${tensorflowIdentifier}</span>`;

@@ -173,22 +84,10 @@ function platformToggle() {
        let tensorflowSpans;

        if(pytorchSpanPosition < tensorflowSpanPosition){
-            const isPrompt = spans.slice(
-                spans.indexOf(tensorflowSpanIdentifier) - promptSpanIdentifier.length,
-                spans.indexOf(tensorflowSpanIdentifier)
-            ) == promptSpanIdentifier;
-            const finalTensorflowSpanPosition = isPrompt ? tensorflowSpanPosition - promptSpanIdentifier.length : tensorflowSpanPosition;
-
-            pytorchSpans = spans.slice(pytorchSpanPosition + pytorchSpanIdentifier.length + 1, finalTensorflowSpanPosition);
+            pytorchSpans = spans.slice(pytorchSpanPosition + pytorchSpanIdentifier.length + 1, tensorflowSpanPosition);
            tensorflowSpans = spans.slice(tensorflowSpanPosition + tensorflowSpanIdentifier.length + 1, spans.length);
        }else{
-            const isPrompt = spans.slice(
-                spans.indexOf(pytorchSpanIdentifier) - promptSpanIdentifier.length,
-                spans.indexOf(pytorchSpanIdentifier)
-            ) == promptSpanIdentifier;
-            const finalPytorchSpanPosition = isPrompt ? pytorchSpanPosition - promptSpanIdentifier.length : pytorchSpanPosition;
-
-            tensorflowSpans = spans.slice(tensorflowSpanPosition + tensorflowSpanIdentifier.length + 1, finalPytorchSpanPosition);
+            tensorflowSpans = spans.slice(tensorflowSpanPosition + tensorflowSpanIdentifier.length + 1, pytorchSpanPosition);
            pytorchSpans = spans.slice(pytorchSpanPosition + pytorchSpanIdentifier.length + 1, spans.length);
        }

@@ -250,7 +149,6 @@ function parseGithubButtons (){"use strict";var e=window.document,t=e.location,o

 function onLoad() {
    addIcon();
-    addVersionControl();
    addCustomFooter();
    addGithubButton();
    parseGithubButtons();
@@ -0,0 +1,54 @@
+# Benchmarks
+
+This section is dedicated to the Benchmarks done by the library, both by maintainers, contributors and users. These 
+benchmark will help keep track of the preformance improvements that are brought to our models across versions.
+
+## Benchmarking all models for inference
+
+As of version 2.1 we have benchmarked all models for inference, across many different settings: using PyTorch, with
+and without TorchScript, using TensorFlow, with and without XLA. All of those tests were done across CPUs (except for
+TensorFlow XLA) and GPUs.
+
+The approach is detailed in the [following blogpost](https://medium.com/huggingface/benchmarking-transformers-pytorch-and-tensorflow-e2917fb891c2)
+
+The results are available [here](https://docs.google.com/spreadsheets/d/1sryqufw2D0XlUH4sq3e9Wnxu5EAQkaohzrJbd5HdQ_w/edit?usp=sharing).
+
+## TF2 with mixed precision, XLA, Distribution (@tlkh)
+
+This work was done by [Timothy Liu](https://github.com/tlkh).
+
+There are very positive results to be gained from the various TensorFlow 2.0 features:
+
+- Automatic Mixed Precision (AMP)
+- XLA compiler
+- Distribution strategies (multi-GPU)
+
+The benefits are listed here (tested on CoLA, MRPC, SST-2):
+
+- AMP: Between 1.4x to 1.6x decrease in overall time without change in batch size
+- AMP+XLA: Up to 2.5x decrease in overall time on SST-2 (larger dataset)
+- Distribution: Between 1.4x to 3.4x decrease in overall time on 4xV100
+- Combined: Up to 5.7x decrease in overall training time, or 9.1x training throughput
+
+The model quality (measured by the validation accuracy) fluctuates slightly. Taking an average of 4 training runs 
+on a single GPU gives the following results:
+
+- CoLA: AMP results in slighter lower acc (0.820 vs 0.824)
+- MRPC: AMP results in lower acc (0.823 vs 0.835)
+- SST-2: AMP results in slighter lower acc (0.918 vs 0.922)
+
+However, in a distributed setting with 4xV100 (4x batch size), AMP can yield in better results:
+
+CoLA: AMP results in higher acc (0.828 vs 0.812)
+MRPC: AMP results in lower acc (0.817 vs 0.827)
+SST-2: AMP results in slightly lower acc (0.926 vs 0.929)
+
+The benchmark script is available [here](https://github.com/NVAITC/benchmarking/blob/master/tf2/bert_dist.py).
+
+Note: on some tasks (e.g. MRPC), the dataset is too small. The overhead due to the model compilation with XLA as well
+as the distribution strategy setup does not speed things up. The XLA compile time is also the reason why although throughput 
+can increase a lot (e.g. 2.7x for single GPU), overall (end-to-end) training speed-up is not as fast (as low as 1.4x)
+
+The benefits as seen on SST-2 (larger dataset) is much clear.
+
+All results can be seen on this [Google Sheet](https://docs.google.com/spreadsheets/d/1538MN224EzjbRL239sqSiUy6YY-rAjHyXhTzz_Zptls/edit#gid=960868445).
@@ -1,322 +0,0 @@
-Benchmarks
-==========
-
-Let's take a look at how 🤗 Transformer models can be benchmarked, best practices, and already available benchmarks.
-
-A notebook explaining in more detail how to benchmark 🤗 Transformer models can be found `here <https://github.com/huggingface/transformers/blob/master/notebooks/05-benchmark.ipynb>`__.
-
-How to benchmark 🤗 Transformer models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The classes :class:`~transformers.PyTorchBenchmark` and :class:`~transformers.TensorFlowBenchmark` allow to flexibly benchmark 🤗 Transformer models.
-The benchmark classes allow us to measure the `peak memory usage` and `required time` for both 
-`inference` and `training`. 
-
-.. note::
-
-  Hereby, `inference` is defined by a single forward pass, and `training` is defined by a single forward pass and backward pass.
-
-The benchmark classes :class:`~transformers.PyTorchBenchmark` and :class:`~transformers.TensorFlowBenchmark` expect an object of type :class:`~transformers.PyTorchBenchmarkArguments` and :class:`~transformers.TensorFlowBenchmarkArguments`, respectively, for instantiation. :class:`~transformers.PyTorchBenchmarkArguments` and :class:`~transformers.TensorFlowBenchmarkArguments` are data classes and contain all relevant configurations for their corresponding benchmark class.
-In the following example, it is shown how a BERT model of type `bert-base-cased` can be benchmarked.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
-
-    >>> args = PyTorchBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
-    >>> benchmark = PyTorchBenchmark(args)
-
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
-
-    >>> args = TensorFlowBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
-    >>> benchmark = TensorFlowBenchmark(args)
-
-
-Here, three arguments are given to the benchmark argument data classes, namely ``models``, ``batch_sizes``, and ``sequence_lengths``. The argument ``models`` is required and expects a :obj:`list` of model identifiers from the `model hub <https://huggingface.co/models>`__
-The :obj:`list` arguments ``batch_sizes`` and ``sequence_lengths`` define the size of the ``input_ids`` on which the model is benchmarked. 
-There are many more parameters that can be configured via the benchmark argument data classes. For more detail on these one can either directly consult the files 
-``src/transformers/benchmark/benchmark_args_utils.py``, ``src/transformers/benchmark/benchmark_args.py`` (for PyTorch) and ``src/transformers/benchmark/benchmark_args_tf.py`` (for Tensorflow). 
-Alternatively, running the following shell commands from root will print out a descriptive list of all configurable parameters for PyTorch and Tensorflow respectively.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    python examples/benchmarking/run_benchmark.py --help
-
-    >>> ## TENSORFLOW CODE
-    python examples/benchmarking/run_benchmark_tf.py --help
-
-
-An instantiated benchmark object can then simply be run by calling ``benchmark.run()``.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> results = benchmark.run()
-    >>> print(results)
-    ====================       INFERENCE - SPEED - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length     Time in s                  
-    --------------------------------------------------------------------------------
-    bert-base-uncased          8               8             0.006     
-    bert-base-uncased          8               32            0.006     
-    bert-base-uncased          8              128            0.018     
-    bert-base-uncased          8              512            0.088     
-    --------------------------------------------------------------------------------
-    
-    ====================      INFERENCE - MEMORY - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length    Memory in MB 
-    --------------------------------------------------------------------------------
-    bert-base-uncased          8               8             1227
-    bert-base-uncased          8               32            1281
-    bert-base-uncased          8              128            1307
-    bert-base-uncased          8              512            1539
-    --------------------------------------------------------------------------------
-    
-    ====================        ENVIRONMENT INFORMATION         ====================
-    - transformers_version: 2.11.0
-    - framework: PyTorch
-    - use_torchscript: False
-    - framework_version: 1.4.0
-    - python_version: 3.6.10
-    - system: Linux
-    - cpu: x86_64
-    - architecture: 64bit
-    - date: 2020-06-29
-    - time: 08:58:43.371351
-    - fp16: False
-    - use_multiprocessing: True
-    - only_pretrain_model: False
-    - cpu_ram_mb: 32088
-    - use_gpu: True
-    - num_gpus: 1
-    - gpu: TITAN RTX
-    - gpu_ram_mb: 24217
-    - gpu_power_watts: 280.0
-    - gpu_performance_state: 2
-    - use_tpu: False
-    
-    >>> ## TENSORFLOW CODE
-    >>> results = benchmark.run()
-    >>> print(results)
-    ====================       INFERENCE - SPEED - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length     Time in s                  
-    --------------------------------------------------------------------------------
-    bert-base-uncased          8               8             0.005
-    bert-base-uncased          8               32            0.008
-    bert-base-uncased          8              128            0.022
-    bert-base-uncased          8              512            0.105
-    --------------------------------------------------------------------------------
-    
-    ====================      INFERENCE - MEMORY - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length    Memory in MB 
-    --------------------------------------------------------------------------------
-    bert-base-uncased          8               8             1330
-    bert-base-uncased          8               32            1330
-    bert-base-uncased          8              128            1330
-    bert-base-uncased          8              512            1770
-    --------------------------------------------------------------------------------
-    
-    ====================        ENVIRONMENT INFORMATION         ====================
-    - transformers_version: 2.11.0
-    - framework: Tensorflow
-    - use_xla: False
-    - framework_version: 2.2.0
-    - python_version: 3.6.10
-    - system: Linux
-    - cpu: x86_64
-    - architecture: 64bit
-    - date: 2020-06-29
-    - time: 09:26:35.617317
-    - fp16: False
-    - use_multiprocessing: True
-    - only_pretrain_model: False
-    - cpu_ram_mb: 32088
-    - use_gpu: True
-    - num_gpus: 1
-    - gpu: TITAN RTX
-    - gpu_ram_mb: 24217
-    - gpu_power_watts: 280.0
-    - gpu_performance_state: 2
-    - use_tpu: False
-
-By default, the `time` and the `required memory` for `inference` are benchmarked. 
-In the example output above the first two sections show the result corresponding to `inference time` and `inference memory`. 
-In addition, all relevant information about the computing environment, `e.g.` the GPU type, the system, the library versions, etc... are printed out in the third section under `ENVIRONMENT INFORMATION`.
-This information can optionally be saved in a `.csv` file when adding the argument :obj:`save_to_csv=True` to :class:`~transformers.PyTorchBenchmarkArguments` and :class:`~transformers.TensorFlowBenchmarkArguments` respectively.
-In this case, every section is saved in a separate `.csv` file. The path to each `.csv` file can optionally be defined via the argument data classes.
-
-Instead of benchmarking pre-trained models via their model identifier, `e.g.` `bert-base-uncased`, the user can alternatively benchmark an arbitrary configuration of any available model class. 
-In this case, a :obj:`list` of configurations must be inserted with the benchmark args as follows.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments, BertConfig
-
-    >>> args = PyTorchBenchmarkArguments(models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
-    >>> config_base = BertConfig()
-    >>> config_384_hid = BertConfig(hidden_size=384)
-    >>> config_6_lay = BertConfig(num_hidden_layers=6)
-
-    >>> benchmark = PyTorchBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
-    >>> benchmark.run()
-    ====================       INFERENCE - SPEED - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length       Time in s                  
-    --------------------------------------------------------------------------------
-    bert-base                  8              128            0.006
-    bert-base                  8              512            0.006
-    bert-base                  8              128            0.018     
-    bert-base                  8              512            0.088     
-    bert-384-hid              8               8             0.006     
-    bert-384-hid              8               32            0.006     
-    bert-384-hid              8              128            0.011     
-    bert-384-hid              8              512            0.054     
-    bert-6-lay                 8               8             0.003     
-    bert-6-lay                 8               32            0.004     
-    bert-6-lay                 8              128            0.009     
-    bert-6-lay                 8              512            0.044
-    --------------------------------------------------------------------------------
-    
-    ====================      INFERENCE - MEMORY - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length      Memory in MB 
-    --------------------------------------------------------------------------------
-    bert-base                  8               8             1277
-    bert-base                  8               32            1281
-    bert-base                  8              128            1307     
-    bert-base                  8              512            1539     
-    bert-384-hid              8               8             1005     
-    bert-384-hid              8               32            1027     
-    bert-384-hid              8              128            1035     
-    bert-384-hid              8              512            1255     
-    bert-6-lay                 8               8             1097     
-    bert-6-lay                 8               32            1101     
-    bert-6-lay                 8              128            1127     
-    bert-6-lay                 8              512            1359
-    --------------------------------------------------------------------------------
-    
-    ====================        ENVIRONMENT INFORMATION         ====================
-    - transformers_version: 2.11.0
-    - framework: PyTorch
-    - use_torchscript: False
-    - framework_version: 1.4.0
-    - python_version: 3.6.10
-    - system: Linux
-    - cpu: x86_64
-    - architecture: 64bit
-    - date: 2020-06-29
-    - time: 09:35:25.143267
-    - fp16: False
-    - use_multiprocessing: True
-    - only_pretrain_model: False
-    - cpu_ram_mb: 32088
-    - use_gpu: True
-    - num_gpus: 1
-    - gpu: TITAN RTX
-    - gpu_ram_mb: 24217
-    - gpu_power_watts: 280.0
-    - gpu_performance_state: 2
-    - use_tpu: False
-
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments, BertConfig
-
-    >>> args = TensorFlowBenchmarkArguments(models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
-    >>> config_base = BertConfig()
-    >>> config_384_hid = BertConfig(hidden_size=384)
-    >>> config_6_lay = BertConfig(num_hidden_layers=6)
-
-    >>> benchmark = TensorFlowBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
-    >>> benchmark.run()
-    ====================       INFERENCE - SPEED - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length       Time in s                  
-    --------------------------------------------------------------------------------
-    bert-base                  8               8             0.005
-    bert-base                  8               32            0.008
-    bert-base                  8              128            0.022
-    bert-base                  8              512            0.106
-    bert-384-hid              8               8             0.005
-    bert-384-hid              8               32            0.007
-    bert-384-hid              8              128            0.018
-    bert-384-hid              8              512            0.064
-    bert-6-lay                 8               8             0.002
-    bert-6-lay                 8               32            0.003
-    bert-6-lay                 8              128            0.0011
-    bert-6-lay                 8              512            0.074
-    --------------------------------------------------------------------------------
-    
-    ====================      INFERENCE - MEMORY - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length      Memory in MB 
-    --------------------------------------------------------------------------------
-    bert-base                  8               8             1330
-    bert-base                  8               32            1330
-    bert-base                  8              128            1330
-    bert-base                  8              512            1770
-    bert-384-hid              8               8             1330
-    bert-384-hid              8               32            1330
-    bert-384-hid              8              128            1330
-    bert-384-hid              8              512            1540
-    bert-6-lay                 8               8             1330
-    bert-6-lay                 8               32            1330
-    bert-6-lay                 8              128            1330
-    bert-6-lay                 8              512            1540
-    --------------------------------------------------------------------------------
-    
-    ====================        ENVIRONMENT INFORMATION         ====================
-    - transformers_version: 2.11.0
-    - framework: Tensorflow
-    - use_xla: False
-    - framework_version: 2.2.0
-    - python_version: 3.6.10
-    - system: Linux
-    - cpu: x86_64
-    - architecture: 64bit
-    - date: 2020-06-29
-    - time: 09:38:15.487125
-    - fp16: False
-    - use_multiprocessing: True
-    - only_pretrain_model: False
-    - cpu_ram_mb: 32088
-    - use_gpu: True
-    - num_gpus: 1
-    - gpu: TITAN RTX
-    - gpu_ram_mb: 24217
-    - gpu_power_watts: 280.0
-    - gpu_performance_state: 2
-    - use_tpu: False
-
-
-Again, `inference time` and `required memory` for `inference` are measured, but this time for customized configurations of the :obj:`BertModel` class. This feature can especially be helpful when 
-deciding for which configuration the model should be trained.
-
-
-Benchmark best practices
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-This section lists a couple of best practices one should be aware of when benchmarking a model.
-
- Currently, only single device benchmarking is supported. When benchmarking on GPU, it is recommended that the user 
-  specifies on which device the code should be run by setting the ``CUDA_VISIBLE_DEVICES`` environment variable in the shell, `e.g.` ``export CUDA_VISIBLE_DEVICES=0`` before running the code.
- The option :obj:`no_multi_processing` should only be set to :obj:`True` for testing and debugging. To ensure accurate memory measurement it is recommended to run each memory benchmark in a separate process by making sure :obj:`no_multi_processing` is set to :obj:`True`.
- One should always state the environment information when sharing the results of a model benchmark. Results can vary heavily between different GPU devices, library versions, etc., so that benchmark results on their own are not very useful for the community.
-
-
-Sharing your benchmark
-~~~~~~~~~~~~~~~~~~~~~~
-
-Previously all available core models (10 at the time) have been benchmarked for `inference time`, across many different settings: using PyTorch, with
-and without TorchScript, using TensorFlow, with and without XLA. All of those tests were done across CPUs (except for
-TensorFlow XLA) and GPUs.
-
-The approach is detailed in the `following blogpost <https://medium.com/huggingface/benchmarking-transformers-pytorch-and-tensorflow-e2917fb891c2>`__ and the results are available `here <https://docs.google.com/spreadsheets/d/1sryqufw2D0XlUH4sq3e9Wnxu5EAQkaohzrJbd5HdQ_w/edit?usp=sharing>`__.
-
-With the new `benchmark` tools, it is easier than ever to share your benchmark results with the community `here <https://github.com/huggingface/transformers/blob/master/examples/benchmarking/README.md>`__.
@@ -26,7 +26,7 @@ author = u'huggingface'
 # The short X.Y version
 version = u''
 # The full version, including alpha/beta/rc tags
-release = u'3.0.2'
+release = u'2.11.0'


 # -- General configuration ---------------------------------------------------
@@ -44,8 +44,7 @@ extensions = [
    'sphinx.ext.napoleon',
    'recommonmark',
    'sphinx.ext.viewcode',
-    'sphinx_markdown_tables',
-    'sphinx_copybutton'
+    'sphinx_markdown_tables'
 ]

 # Add any paths that contain templates here, relative to this directory.
@@ -75,8 +74,6 @@ exclude_patterns = [u'_build', 'Thumbs.db', '.DS_Store']
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = None

-# Remove the prompt when copying examples
-copybutton_prompt_text = ">>> "

 # -- Options for HTML output -------------------------------------------------

@@ -190,8 +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 -------------------------------------------------
@@ -11,7 +11,7 @@ General terms
  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 that combines texts with another kind of inputs (for instance images).
+- 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".
@@ -45,16 +45,17 @@ tokenizer, which is a `WordPiece <https://arxiv.org/pdf/1609.08144.pdf>`__ token

 ::

-    >>> from transformers import BertTokenizer
-    >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+    from transformers import BertTokenizer
+    tokenizer = BertTokenizer.from_pretrained("bert-base-cased")

-    >>> sequence = "A Titan RTX has 24GB of VRAM"
+    sequence = "A Titan RTX has 24GB of VRAM"

 The tokenizer takes care of splitting the sequence into tokens available in the tokenizer vocabulary.

 ::

-    >>> tokenized_sequence = tokenizer.tokenize(sequence)
+    tokenized_sequence = tokenizer.tokenize(sequence)
+    print(tokenized_sequence)

 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
@@ -62,7 +63,6 @@ added for "RA" and "M":

 ::

-    >>> print(tokenized_sequence)
    ['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
@@ -71,14 +71,14 @@ the sentence to the tokenizer, which leverages the Rust implementation of

 ::

-    >>> encoded_sequence = tokenizer(sequence)["input_ids"]
+    encoded_sequence = tokenizer(sequence)["input_ids"]
+    print(encoded_sequence)

 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":

 ::

-    >>> print(encoded_sequence)
    [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
@@ -86,14 +86,13 @@ IDs the model sometimes uses. If we decode the previous sequence of ids,

 ::

-    >>> decoded_sequence = tokenizer.decode(encoded_sequence)
+    tokenizer.decode(encoded_sequence)

 we will see 

 ::

-    >>> print(decoded_sequence)
-    [CLS] A Titan RTX has 24GB of VRAM [SEP]
+    '[CLS] A Titan RTX has 24GB of VRAM [SEP]'

 because this is the way a :class:`~transformers.BertModel` is going to expect its inputs.

@@ -109,20 +108,21 @@ For example, consider these two sequences:

 ::

-    >>> from transformers import BertTokenizer
-    >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+    from transformers import BertTokenizer
+    tokenizer = BertTokenizer.from_pretrained("bert-base-cased")

-    >>> sequence_a = "This is a short sequence."
-    >>> sequence_b = "This is a rather long sequence. It is at least longer than the sequence A."
+    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"]
+    encoded_sequence_a = tokenizer(sequence_a)["input_ids"]
+    encoded_sequence_b = tokenizer(sequence_b)["input_ids"]
+    
+    len(encoded_sequence_a), len(encoded_sequence_b)

 The encoded versions have different lengths:

 ::

-    >>> len(encoded_sequence_a), len(encoded_sequence_b)
    (8, 19)

 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
@@ -133,14 +133,15 @@ it to pad like this:

 ::

-    >>> padded_sequences = tokenizer([sequence_a, sequence_b], padding=True)
+    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:

 ::

-    >>> padded_sequences["input_ids"]
-    [[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]]
+    [[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
 the position of the padded indices so that the model does not attend to them. For the
@@ -149,8 +150,14 @@ a padded value. This attention mask is in the dictionary returned by the tokeniz

 ::

-    >>> padded_sequences["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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
+    padded_sequences["attention_mask"]
+
+will give back
+
+::
+
+    [[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:

@@ -163,27 +170,26 @@ tokens. For example, the BERT model builds its two sequence input as such:

 ::

-   >>> # [CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]
+   # [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")
-    >>> sequence_a = "HuggingFace is based in NYC"
-    >>> sequence_b = "Where is HuggingFace based?"
+    from transformers import BertTokenizer
+    tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+    sequence_a = "HuggingFace is based in NYC"
+    sequence_b = "Where is HuggingFace based?"

-    >>> encoded_dict = tokenizer(sequence_a, sequence_b)
-    >>> decoded = tokenizer.decode(encoded_dict["input_ids"])
+    encoded_dict = tokenizer(sequence_a, sequence_b)
+    tokenizer.decode(encoded_dict["input_ids"])

 which will return:

 ::

-    >>> print(decoded)
-    [CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]
+    "[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
@@ -193,7 +199,12 @@ The tokenizer returns in the dictionary under the key "token_type_ids":

 ::

-    >>> encoded_dict['token_type_ids']
+    encoded_dict['token_type_ids']
+
+will return
+
+::
+
    [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
@@ -42,7 +42,7 @@ The documentation is organized in five parts:

 - **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.
+- **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
@@ -121,10 +121,7 @@ conversion utilities for the following 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. `DPR <https://github.com/facebookresearch/DPR>`_ (from Facebook) released with the paper `Dense Passage Retrieval
-    for Open-Domain Question Answering <https://arxiv.org/abs/2004.04906>`_ by Vladimir Karpukhin, Barlas Oğuz, Sewon
-    Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
-23. `Other community models <https://huggingface.co/models>`_, contributed by the `community
+22. `Other community models <https://huggingface.co/models>`_, contributed by the `community
    <https://huggingface.co/users>`_.

 .. toctree::
@@ -138,14 +135,12 @@ conversion utilities for the following models:

 .. toctree::
    :maxdepth: 2
-    :caption: Using 🤗 Transformers
+    :caption: Using Transformers

    task_summary
    model_summary
-    preprocessing
-    training
+    serialization
    model_sharing
-    tokenizer_summary
    multilingual

 .. toctree::
@@ -165,7 +160,6 @@ conversion utilities for the following models:
    :caption: Research

    bertology
-    perplexity
    benchmarks

 .. toctree::
@@ -173,13 +167,11 @@ conversion utilities for the following models:
    :caption: Package Reference

    main_classes/configuration
-    main_classes/output
    main_classes/model
    main_classes/tokenizer
    main_classes/pipelines
    main_classes/optimizer_schedules
    main_classes/processors
-    main_classes/trainer
    model_doc/auto
    model_doc/encoderdecoder
    model_doc/bert
@@ -204,4 +196,3 @@ conversion utilities for the following models:
    model_doc/longformer
    model_doc/retribert
    model_doc/mobilebert
-    model_doc/dpr
@@ -1,9 +1,7 @@
 Configuration
 ----------------------------------------------------

-The base class ``PretrainedConfig`` implements the common methods for loading/saving a configuration either from a
-local file or directory, or from a pretrained model configuration provided by the library (downloaded from
-HuggingFace's AWS S3 repository).
+The base class ``PretrainedConfig`` implements the common methods for loading/saving a configuration either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace's AWS S3 repository).

 ``PretrainedConfig``
 ~~~~~~~~~~~~~~~~~~~~~
@@ -1,4 +1,4 @@
-Optimization
+Optimizer
 ----------------------------------------------------

 The ``.optimization`` module provides:
@@ -7,25 +7,24 @@ The ``.optimization`` module provides:
 - several schedules in the form of schedule objects that inherit from ``_LRSchedule``:
 - a gradient accumulation class to accumulate the gradients of multiple batches

-``AdamW`` (PyTorch)
-~~~~~~~~~~~~~~~~~~~
+``AdamW``
+~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AdamW
    :members:

-``AdamWeightDecay`` (TensorFlow)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``AdamWeightDecay``
+~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AdamWeightDecay

 .. autofunction:: transformers.create_optimizer

 Schedules
-~~~~~~~~~~~~~~~~~~~
-
-Learning Rate Schedules (Pytorch)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+----------------------------------------------------

+Learning Rate Schedules
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. autofunction:: transformers.get_constant_schedule


@@ -57,16 +56,16 @@ Learning Rate Schedules (Pytorch)
    :target: /imgs/warmup_linear_schedule.png
    :alt:

-``Warmup`` (TensorFlow)
-^^^^^^^^^^^^^^^^^^^^^^^
+``Warmup``
+~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.WarmUp
    :members:

 Gradient Strategies
-~~~~~~~~~~~~~~~~~~~~
+----------------------------------------------------

-``GradientAccumulator`` (TensorFlow)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+``GradientAccumulator``
+~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GradientAccumulator
@@ -1,141 +0,0 @@
-Model outputs
-------------
-
-PyTorch models have outputs that are instances of subclasses of :class:`~transformers.file_utils.ModelOutput`. Those
-are data structures containing all the information returned by the model, but that can also be used as tuples or
-dictionaries.
-
-Let's see of this looks on an example:
-
-.. code-block::
-
-    from transformers import BertTokenizer, BertForSequenceClassification
-    import torch
-
-    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-    model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
-
-    inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-    labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
-    outputs = model(**inputs, labels=labels)
-
-The ``outputs`` object is a :class:`~transformers.modeling_outputs.SequenceClassifierOutput`, as we can see in the
-documentation of that class below, it means it has an optional ``loss``, a ``logits`` an optional ``hidden_states`` and
-an optional ``attentions`` attribute. Here we have the ``loss`` since we passed along ``labels``, but we don't have
-``hidden_states`` and ``attentions`` because we didn't pass ``output_hidden_states=True`` or
-``output_attentions=True``.
-
-You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you
-will get ``None``. Here for instance ``outputs.loss`` is the loss computed by the model, and ``outputs.attentions`` is
-``None``.
-
-When considering our ``outputs`` object as tuple, it only considers the attributes that don't have ``None`` values.
-Here for instance, it has two elements, ``loss`` then ``logits``, so
-
-.. code-block::
-
-    outputs[:2]
-
-will return the tuple ``(outputs.loss, outputs.logits)`` for instance.
-
-When considering our ``outputs`` object as dictionary, it only considers the attributes that don't have ``None``
-values. Here for instance, it has two keys that are ``loss`` and ``logits``.
-
-We document here the generic model outputs that are used by more than one model type. Specific output types are
-documented on their corresponding model page.
-
-``ModelOutput``
-~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.file_utils.ModelOutput
-    :members:
-
-``BaseModelOutput``
-~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.BaseModelOutput
-    :members:
-
-``BaseModelOutputWithPooling``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPooling
-    :members:
-
-``BaseModelOutputWithPast``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPast
-    :members:
-
-``Seq2SeqModelOutput``
-~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.Seq2SeqModelOutput
-    :members:
-
-``CausalLMOutput``
-~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.CausalLMOutput
-    :members:
-
-``CausalLMOutputWithPast``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.CausalLMOutputWithPast
-    :members:
-
-``MaskedLMOutput``
-~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.MaskedLMOutput
-    :members:
-
-``Seq2SeqLMOutput``
-~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.Seq2SeqLMOutput
-    :members:
-
-``NextSentencePredictorOutput``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.NextSentencePredictorOutput
-    :members:
-
-``SequenceClassifierOutput``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.SequenceClassifierOutput
-    :members:
-
-``Seq2SeqSequenceClassifierOutput``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput
-    :members:
-
-``MultipleChoiceModelOutput``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.MultipleChoiceModelOutput
-    :members:
-
-``TokenClassifierOutput``
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.TokenClassifierOutput
-    :members:
-
-``QuestionAnsweringModelOutput``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.QuestionAnsweringModelOutput
-    :members:
-
-``Seq2SeqQuestionAnsweringModelOutput``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput
-    :members:
@@ -11,7 +11,7 @@ The base classes ``PreTrainedTokenizer`` and ``PreTrainedTokenizerFast`` impleme
 - adding new tokens to the vocabulary in a way that is independant of the underlying structure (BPE, SentencePiece...),
 - managing special tokens like mask, beginning-of-sentence, etc tokens (adding them, assigning them to attributes in the tokenizer for easy access and making sure they are not split during tokenization)

-``BatchEncoding`` holds the output of the tokenizer's encoding methods (``__call__``, ``encode_plus`` and ``batch_encode_plus``) and is derived from a Python dictionary. When the tokenizer is a pure python tokenizer, this class behave just like a standard python dictionary and hold the various model inputs computed by these methodes (``input_ids``, ``attention_mask``...). When the tokenizer is a "Fast" tokenizer (i.e. backed by HuggingFace tokenizers library), this class provides in addition several advanced alignement methods which can be used to map between the original string (character and words) and the token space (e.g. getting the index of the token comprising a given character or the span of characters corresponding to a given token).
+``BatchEncoding`` holds the output of the tokenizer's encoding methods (``encode_plus`` and ``batch_encode_plus``) and is derived from a Python dictionary. When the tokenizer is a pure python tokenizer, this class behave just like a standard python dictionary and hold the various model inputs computed by these methodes (``input_ids``, ``attention_mask``...). When the tokenizer is a "Fast" tokenizer (i.e. backed by HuggingFace tokenizers library), this class provides in addition several advanced alignement methods which can be used to map between the original string (character and words) and the token space (e.g. getting the index of the token comprising a given character or the span of characters corresponding to a given token).

 ``PreTrainedTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~
@@ -1,45 +0,0 @@
-Trainer
----------
-
-The :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` classes provide an API for feature-complete
-training in most standard use cases. It's used in most of the :doc:`example scripts <../examples>`.
-
-Before instantiating your :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer`, create a 
-:class:`~transformers.TrainingArguments`/:class:`~transformers.TFTrainingArguments` to access all the points of
-customization during training.
-
-The API supports distributed training on multiple GPUs/TPUs, mixed precision through `NVIDIA Apex
-<https://github.com/NVIDIA/apex>`__ for PyTorch and :obj:`tf.keras.mixed_precision` for TensorFlow.
-
-``Trainer`` 
-~~~~~~~~~~~
-
-.. autoclass:: transformers.Trainer
-    :members:
-
-``TFTrainer`` 
-~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFTrainer
-    :members:
-
-``TrainingArguments``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TrainingArguments
-    :members:
-
-``TFTrainingArguments``
-~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFTrainingArguments
-    :members:
-
-Utilities
-~~~~~~~~~
-
-.. autoclass:: transformers.EvalPrediction
-
-.. autofunction:: transformers.set_seed
-
-.. autofunction:: transformers.torch_distributed_zero_first
@@ -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:
@@ -47,13 +47,6 @@ AlbertTokenizer
        create_token_type_ids_from_sequences, save_vocabulary


-Albert specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_albert.AlbertForPretrainingOutput
-    :members:
-
-
 AlbertModel
 ~~~~~~~~~~~~~~~~~~~~

@@ -61,13 +54,6 @@ AlbertModel
    :members:


-AlbertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AlbertForPreTraining
-    :members:
-
-
 AlbertForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -110,13 +96,6 @@ TFAlbertModel
    :members:


-TFAlbertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAlbertForPreTraining
-    :members:
-
-
 TFAlbertForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -39,18 +39,6 @@ BartTokenizer
    :members:


-MBartTokenizer
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MBartTokenizer
-    :members: build_inputs_with_special_tokens, prepare_translation_batch
-
-BartForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BartForConditionalGeneration
-    :members: generate, forward
-
 BartModel
 ~~~~~~~~~~~~~

@@ -74,3 +62,10 @@ BartForQuestionAnswering
    :members: forward


+BartForConditionalGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BartForConditionalGeneration
+    :members: generate, forward
+
+
@@ -59,13 +59,6 @@ BertTokenizerFast
    :members:


-Bert specific outputs
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_bert.BertForPretrainingOutput
-    :members:
-
-
 BertModel
 ~~~~~~~~~~~~~~~~~~~~

@@ -1,102 +0,0 @@
-DPR
----------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~
-
-Dense Passage Retrieval (DPR) - is a set of tools and models for state-of-the-art open-domain Q&A research.
-It is based on the following paper:
-
-Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih, Dense Passage Retrieval for Open-Domain Question Answering.
-
-The abstract from the paper is the following:
-
-*Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional
-sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can
-be practically implemented using dense representations alone, where embeddings are learned from a small number of
-questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets,
-our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage
-retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA
-benchmarks.*
-
-The original code can be found `here <https://github.com/facebookresearch/DPR>`_.
-
-
-DPRConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DPRConfig
-    :members:
-
-
-DPRContextEncoderTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DPRContextEncoderTokenizer
-    :members:
-
-
-DPRContextEncoderTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DPRContextEncoderTokenizerFast
-    :members:
-
-DPRQuestionEncoderTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DPRQuestionEncoderTokenizer
-    :members:
-
-
-DPRQuestionEncoderTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DPRQuestionEncoderTokenizerFast
-    :members:
-
-DPRReaderTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DPRReaderTokenizer
-    :members:
-
-
-DPRReaderTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DPRReaderTokenizerFast
-    :members:
-
-
-DPR specific outputs
-~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_dpr.DPRContextEncoderOutput
-    :members:
-
-.. autoclass:: transformers.modeling_dpr.DPRQuestionEncoderOutput
-    :members:
-
-.. autoclass:: transformers.modeling_dpr.DPRReaderOutput
-    :members:
-
-
-DPRContextEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DPRContextEncoder
-    :members:
-
-DPRQuestionEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DPRQuestionEncoder
-    :members:
-
-
-DPRReader
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DPRReader
-    :members:
@@ -71,13 +71,6 @@ ElectraTokenizerFast
    :members:


-Electra specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_electra.ElectraForPretrainingOutput
-    :members:
-
-
 ElectraModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -61,13 +61,6 @@ FlaubertForSequenceClassification
    :members:


-FlaubertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaubertForTokenClassification
-    :members:
-
-
 FlaubertForQuestionAnsweringSimple
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -121,4 +114,4 @@ TFFlaubertForQuestionAnsweringSimple
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFFlaubertForQuestionAnsweringSimple
-    :members:
+    :members:
@@ -71,13 +71,6 @@ OpenAIGPTTokenizerFast
    :members:


-OpenAI specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_openai.OpenAIGPTDoubleHeadsModelOutput
-    :members:
-
-
 OpenAIGPTModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -58,13 +58,6 @@ GPT2TokenizerFast
    :members:


-GPT2 specific outputs
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_gpt2.GPT2DoubleHeadsModelOutput
-    :members:
-
-
 GPT2Model
 ~~~~~~~~~~~~~~~~~~~~~

@@ -56,13 +56,6 @@ MobileBertTokenizerFast
    :members:


-MobileBert specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_mobilebert.MobileBertForPretrainingOutput
-    :members:
-
-
 MobileBertModel
 ~~~~~~~~~~~~~~~~~~~~

@@ -112,17 +112,3 @@ ReformerModelWithLMHead

 .. autoclass:: transformers.ReformerModelWithLMHead
    :members:
-
-
-ReformerForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ReformerForMaskedLM
-    :members:
-
-
-ReformerForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ReformerForQuestionAnswering
-    :members:
@@ -54,16 +54,6 @@ TransfoXLTokenizerFast
    :members:


-TransfoXL specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_transfo_xl.TransfoXLModelOutput
-    :members:
-
-.. autoclass:: transformers.modeling_transfo_xl.TransfoXLLMHeadModelOutput
-    :members:
-
-
 TransfoXLModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -46,14 +46,6 @@ XLMTokenizer
    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
        create_token_type_ids_from_sequences, save_vocabulary

-
-XLM specific outputs
-~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_xlm.XLMForQuestionAnsweringOutput
-    :members:
-
-
 XLMModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -50,31 +50,6 @@ XLNetTokenizer
        create_token_type_ids_from_sequences, save_vocabulary


-XLNet specific outputs
-~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_xlnet.XLNetModelOutput
-    :members:
-
-.. autoclass:: transformers.modeling_xlnet.XLNetLMHeadModelOutput
-    :members:
-
-.. autoclass:: transformers.modeling_xlnet.XLNetForSequenceClassificationOutput
-    :members:
-
-.. autoclass:: transformers.modeling_xlnet.XLNetForMultipleChoiceOutput
-    :members:
-
-.. autoclass:: transformers.modeling_xlnet.XLNetForTokenClassificationOutput
-    :members:
-
-.. autoclass:: transformers.modeling_xlnet.XLNetForQuestionAnsweringSimpleOutput
-    :members:
-
-.. autoclass:: transformers.modeling_xlnet.XLNetForQuestionAnsweringOutput
-    :members:
-
-
 XLNetModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -0,0 +1,55 @@
+# Model upload and sharing
+
+Starting with `v2.2.2`, you can now upload and share your fine-tuned models with the community, using the <abbr title="Command-line interface">CLI</abbr> that's built-in to the library.
+
+**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Optionally, join an existing organization or create a new one. Then:
+
+```shell
+transformers-cli login
+# log in using the same credentials as on huggingface.co
+```
+Upload your model:
+```shell
+transformers-cli upload ./path/to/pretrained_model/
+
+# ^^ Upload folder containing weights/tokenizer/config
+# saved via `.save_pretrained()`
+
+transformers-cli upload ./config.json [--filename folder/foobar.json]
+
+# ^^ Upload a single file
+# (you can optionally override its filename, which can be nested inside a folder)
+```
+
+If you want your model to be namespaced by your organization name rather than your username, add the following flag to any command:
+```shell
+--organization organization_name
+```
+
+Your model will then be accessible through its identifier, a concatenation of your username (or organization name) and the folder name above:
+```python
+"username/pretrained_model"
+# or if an org:
+"organization_name/pretrained_model"
+```
+
+**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hardware used, hyperparameters), evaluation results, intended uses & limitations, etc.
+
+Your model now has a page on huggingface.co/models 🔥
+
+Anyone can load it from code:
+```python
+tokenizer = AutoTokenizer.from_pretrained("namespace/pretrained_model")
+model = AutoModel.from_pretrained("namespace/pretrained_model")
+```
+
+List all your files on S3:
+```shell
+transformers-cli s3 ls
+```
+
+You can also delete unneeded files:
+
+```shell
+transformers-cli s3 rm …
+```
@@ -1,217 +0,0 @@
-Model sharing and uploading
-===========================
-
-In this page, we will show you how to share a model you have trained or fine-tuned on new data with the community on
-the `model hub <https://huggingface.co/models>`__.
-
-.. note::
-
-    You will need to create an account on `huggingface.co <https://huggingface.co/join>`__ for this.
-
-    Optionally, you can join an existing organization or create a new one.
-
-Prepare your model for uploading
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-We have seen in the :doc:`training tutorial <training>`: how to fine-tune a model on a given task. You have probably
-done something similar on your task, either using the model directly in your own training loop or using the
-:class:`~.transformers.Trainer`/:class:`~.transformers.TFTrainer` class. Let's see how you can share the result on
-the `model hub <https://huggingface.co/models>`__.
-
-Basic steps
-^^^^^^^^^^^
-
-.. 
-    When #5258 is merged, we can remove the need to create the directory.
-
-First, pick a directory with the name you want your model to have on the model hub (its full name will then be
-`username/awesome-name-you-picked` or `organization/awesome-name-you-picked`) and create it with either
-
-::
-
-    mkdir path/to/awesome-name-you-picked
-
-or in python
-
-::
-
-    import os
-    os.makedirs("path/to/awesome-name-you-picked")
-
-then you can save your model and tokenizer with:
-
-::
-
-    model.save_pretrained("path/to/awesome-name-you-picked")
-    tokenizer.save_pretrained("path/to/awesome-name-you-picked")
-
-Or, if you're using the Trainer API
-
-::
-
-    trainer.save_model("path/to/awesome-name-you-picked")
-    tokenizer.save_pretrained("path/to/awesome-name-you-picked")
-
-Make your model work on all frameworks
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. 
-    TODO Sylvain: make this automatic during the upload
-
-You probably have your favorite framework, but so will other users! That's why it's best to upload your model with both
-PyTorch `and` TensorFlow checkpoints to make it easier to use (if you skip this step, users will still be able to load
-your model in another framework, but it will be slower, as it will have to be converted on the fly). Don't worry, it's super easy to do (and in a future version,
-it will all be automatic). You will need to install both PyTorch and TensorFlow for this step, but you don't need to
-worry about the GPU, so it should be very easy. Check the
-`TensorFlow installation page <https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available>`__ 
-and/or the `PyTorch installation page <https://pytorch.org/get-started/locally/#start-locally>`__ to see how.
-
-First check that your model class exists in the other framework, that is try to import the same model by either adding
-or removing TF. For instance, if you trained a :class:`~transformers.DistilBertForSequenceClassification`, try to
-type
-
-::
-
-    from transformers import TFDistilBertForSequenceClassification
-
-and if you trained a :class:`~transformers.TFDistilBertForSequenceClassification`, try to
-type
-
-::
-
-    from transformers import DistilBertForSequenceClassification
-
-This will give back an error if your model does not exist in the other framework (something that should be pretty rare
-since we're aiming for full parity between the two frameworks). In this case, skip this and go to the next step.
-
-Now, if you trained your model in PyTorch and have to create a TensorFlow version, adapt the following code to your
-model class:
-
-::
-
-    tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True)
-    tf_model.save_pretrained("path/to/awesome-name-you-picked")
-
-and if you trained your model in TensorFlow and have to create a PyTorch version, adapt the following code to your
-model class:
-
-::
-
-    pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True)
-    pt_model.save_pretrained("path/to/awesome-name-you-picked")
-
-That's all there is to it!
-
-Check the directory before uploading
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Make sure there are no garbage files in the directory you'll upload. It should only have:
-
- a `config.json` file, which saves the :doc:`configuration <main_classes/configuration>` of your model ;
- a `pytorch_model.bin` file, which is the PyTorch checkpoint (unless you can't have it for some reason) ;
- a `tf_model.h5` file, which is the TensorFlow checkpoint (unless you can't have it for some reason) ;
- a `special_tokens_map.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save;
- a `tokenizer_config.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save;
- a `vocab.txt`, which is the vocabulary of your tokenizer, part of your :doc:`tokenizer <main_classes/tokenizer>`
-  save;
- maybe a `added_tokens.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save.
-
-Other files can safely be deleted.
-
-Upload your model with the CLI
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Now go in a terminal and run the following command. It should be in the virtual enviromnent where you installed 🤗
-Transformers, since that command :obj:`transformers-cli` comes from the library.
-
-::
-
-    transformers-cli login
-
-Then log in using the same credentials as on huggingface.co. To upload your model, just type
-
-::
-
-    transformers-cli upload path/to/awesome-name-you-picked/
-
-This will upload the folder containing the weights, tokenizer and configuration we prepared in the previous section.
-
-If you want to upload a single file (a new version of your model, or the other framework checkpoint you want to add),
-just type:
-
-::
-
-    transformers-cli upload path/to/awesome-name-you-picked/that-file 
-
-or
-
-::
-
-   transformers-cli upload path/to/awesome-name-you-picked/that-file --filename awesome-name-you-picked/new_name
-
-if you want to change its filename.
-
-This uploads the model to your personal account. If you want your model to be namespaced by your organization name
-rather than your username, add the following flag to any command:
-
-::
-
-    --organization organization_name
-
-so for instance:
-
-::
-
-    transformers-cli upload path/to/awesome-name-you-picked/ --organization organization_name
-
-Your model will then be accessible through its identifier, which is, as we saw above,
-`username/awesome-name-you-picked` or `organization/awesome-name-you-picked`.
-
-Add a model card
-^^^^^^^^^^^^^^^^
-
-To make sure everyone knows what your model can do, what its limitations and potential bias or ethetical
-considerations, please add a README.md model card to the 🤗 Transformers repo under `model_cards/`. It should then be
-placed in a subfolder with your username or organization, then another subfolder named like your model
-(`awesome-name-you-picked`). Or just click on the "Create a model card on GitHub" button on the model page, it will
-get you directly to the right location. If you need one, `here <https://github.com/huggingface/model_card>`__ is a
-model card template (meta-suggestions are welcome).
-
-If your model is fine-tuned from another model coming from the model hub (all 🤗 Transformers pretrained models do),
-don't forget to link to its model card so that people can fully trace how your model was built.
-
-If you have never made a pull request to the 🤗 Transformers repo, look at the
-:doc:`contributing guide <contributing>` to see the steps to follow.
-
-.. Note::
-
-    You can also send your model card in the folder you uploaded with the CLI by placing it in a `README.md` file
-    inside `path/to/awesome-name-you-picked/`.
-
-Using your model
-^^^^^^^^^^^^^^^^
-
-Your model now has a page on huggingface.co/models 🔥
-
-Anyone can load it from code:
-
-::
-
-    tokenizer = AutoTokenizer.from_pretrained("namespace/awesome-name-you-picked")
-    model = AutoModel.from_pretrained("namespace/awesome-name-you-picked")
-
-Additional commands
-^^^^^^^^^^^^^^^^^^^
-
-You can list all the files you uploaded on the hub like this:
-
-::
-
-    transformers-cli s3 ls
-
-You can also delete unneeded files with
-
-::
-
-    transformers-cli s3 rm awesome-name-you-picked/filename
-
@@ -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 
@@ -55,7 +55,7 @@ Original GPT
   <a href="https://huggingface.co/models?filter=openai-gpt">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-openai--gpt-blueviolet">
   </a>
-   <a href="model_doc/gpt.html">
+   <a href="/model_doc/gpt">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-openai--gpt-blueviolet">
   </a>

@@ -75,7 +75,7 @@ GPT-2
   <a href="https://huggingface.co/models?filter=gpt2">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-gpt2-blueviolet">
   </a>
-   <a href="model_doc/gpt2.html">
+   <a href="/model_doc/gpt2">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-gpt2-blueviolet">
   </a>

@@ -96,7 +96,7 @@ CTRL
   <a href="https://huggingface.co/models?filter=ctrl">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-ctrl-blueviolet">
   </a>
-   <a href="model_doc/ctrl.html">
+   <a href="/model_doc/ctrl">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-ctrl-blueviolet">
   </a>

@@ -117,7 +117,7 @@ Transformer-XL
   <a href="https://huggingface.co/models?filter=transfo-xl">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-transfo--xl-blueviolet">
   </a>
-   <a href="model_doc/transformerxl.html">
+   <a href="/model_doc/transformerxl">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-transfo--xl-blueviolet">
   </a>

@@ -148,7 +148,7 @@ Reformer
   <a href="https://huggingface.co/models?filter=reformer">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-reformer-blueviolet">
   </a>
-   <a href="model_doc/reformer.html">
+   <a href="/model_doc/reformer">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-reformer-blueviolet">
   </a>

@@ -183,7 +183,7 @@ XLNet
   <a href="https://huggingface.co/models?filter=xlnet">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlnet-blueviolet">
   </a>
-   <a href="model_doc/xlnet.html">
+   <a href="/model_doc/xlnet">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlnet-blueviolet">
   </a>

@@ -217,7 +217,7 @@ BERT
   <a href="https://huggingface.co/models?filter=bert">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-bert-blueviolet">
   </a>
-   <a href="model_doc/bert.html">
+   <a href="/model_doc/bert">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-bert-blueviolet">
   </a>

@@ -246,7 +246,7 @@ ALBERT
   <a href="https://huggingface.co/models?filter=albert">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-albert-blueviolet">
   </a>
-   <a href="model_doc/albert.html">
+   <a href="/model_doc/albert">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-albert-blueviolet">
   </a>

@@ -275,7 +275,7 @@ RoBERTa
   <a href="https://huggingface.co/models?filter=roberta">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-roberta-blueviolet">
   </a>
-   <a href="model_doc/roberta.html">
+   <a href="/model_doc/roberta">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-roberta-blueviolet">
   </a>

@@ -301,7 +301,7 @@ DistilBERT
   <a href="https://huggingface.co/models?filter=distilbert">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-distilbert-blueviolet">
   </a>
-   <a href="model_doc/distilbert.html">
+   <a href="/model_doc/distilbert">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-distilbert-blueviolet">
   </a>

@@ -326,7 +326,7 @@ XLM
   <a href="https://huggingface.co/models?filter=xlm">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlm-blueviolet">
   </a>
-   <a href="model_doc/xlm.html">
+   <a href="/model_doc/xlm">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlm-blueviolet">
   </a>

@@ -361,7 +361,7 @@ XLM-RoBERTa
   <a href="https://huggingface.co/models?filter=xlm-roberta">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlm--roberta-blueviolet">
   </a>
-   <a href="model_doc/xlmroberta.html">
+   <a href="/model_doc/xlmroberta">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlm--roberta-blueviolet">
   </a>

@@ -383,7 +383,7 @@ FlauBERT
   <a href="https://huggingface.co/models?filter=flaubert">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-flaubert-blueviolet">
   </a>
-   <a href="model_doc/flaubert.html">
+   <a href="/model_doc/flaubert">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-flaubert-blueviolet">
   </a>

@@ -401,7 +401,7 @@ ELECTRA
   <a href="https://huggingface.co/models?filter=electra">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-electra-blueviolet">
   </a>
-   <a href="model_doc/electra.html">
+   <a href="/model_doc/electra">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-electra-blueviolet">
   </a>

@@ -427,7 +427,7 @@ Longformer
   <a href="https://huggingface.co/models?filter=longformer">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-longformer-blueviolet">
   </a>
-   <a href="model_doc/longformer.html">
+   <a href="/model_doc/longformer">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-longformer-blueviolet">
   </a>

@@ -461,7 +461,7 @@ BART
   <a href="https://huggingface.co/models?filter=bart">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-bart-blueviolet">
   </a>
-   <a href="model_doc/bart.html">
+   <a href="/model_doc/bart">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-bart-blueviolet">
   </a>

@@ -488,7 +488,7 @@ MarianMT
   <a href="https://huggingface.co/models?filter=marian">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-marian-blueviolet">
   </a>
-   <a href="model_doc/marian.html">
+   <a href="/model_doc/marian">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-marian-blueviolet">
   </a>

@@ -506,7 +506,7 @@ T5
   <a href="https://huggingface.co/models?filter=t5">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-t5-blueviolet">
   </a>
-   <a href="model_doc/t5.html">
+   <a href="/model_doc/t5">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-t5-blueviolet">
   </a>

@@ -555,7 +555,7 @@ model know which part of the input vector corresponds to the text or the image.
 The pretrained model only works for classification.

 ..
-    More information in this :doc:`model documentation </model_doc/mmbt.html>`.
+    More information in this :doc:`model documentation </model_doc/mmbt>`.
    TODO: write this page

 More technical aspects
@@ -36,11 +36,10 @@ Here is an example using the ``xlm-clm-enfr-1024`` checkpoint (Causal language m

 .. code-block::

-    >>> import torch
-    >>> from transformers import XLMTokenizer, XLMWithLMHeadModel
+    import torch
+    from transformers import XLMTokenizer, XLMWithLMHeadModel

-    >>> tokenizer = XLMTokenizer.from_pretrained("xlm-clm-enfr-1024")
-    >>> model = XLMWithLMHeadModel.from_pretrained("xlm-clm-enfr-1024")
+    tokenizer = XLMTokenizer.from_pretrained("xlm-clm-1024-enfr")


 The different languages this model/tokenizer handles, as well as the ids of these languages are visible using the
@@ -48,15 +47,16 @@ The different languages this model/tokenizer handles, as well as the ids of thes

 .. code-block::

-    >>> print(tokenizer.lang2id)
-    {'en': 0, 'fr': 1}
+    # Continuation of the previous script
+    print(tokenizer.lang2id)  # {'en': 0, 'fr': 1}


 These ids should be used when passing a language parameter during a model pass. Let's define our inputs:

 .. code-block::

-    >>> input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1
+    # Continuation of the previous script
+    input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1


 We should now define the language embedding by using the previously defined language id. We want to create a tensor
@@ -64,18 +64,20 @@ filled with the appropriate language ids, of the same size as input_ids. For eng

 .. code-block::

-    >>> language_id = tokenizer.lang2id['en']  # 0
-    >>> langs = torch.tensor([language_id] * input_ids.shape[1])  # torch.tensor([0, 0, 0, ..., 0])
+    # Continuation of the previous script
+    language_id = tokenizer.lang2id['en']  # 0
+    langs = torch.tensor([language_id] * input_ids.shape[1])  # torch.tensor([0, 0, 0, ..., 0])

-    >>> # We reshape it to be of size (batch_size, sequence_length)
-    >>> langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1)
+    # We reshape it to be of size (batch_size, sequence_length)
+    langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1)


 You can then feed it all as input to your model:

 .. code-block::

-    >>> outputs = model(input_ids, langs=langs)
+    # Continuation of the previous script
+    outputs = model(input_ids, langs=langs)


 The example `run_generation.py <https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py>`__
@@ -1,151 +0,0 @@
-Perplexity of fixed-length models
-=================================
-
-Perplexity (PPL) is one of the most common metrics for evaluating language
-models. Before diving in, we should note that the metric applies specifically
-to classical language models (sometimes called autoregressive or causal
-language models) and is not well defined for masked language models like BERT
-(see :doc:`summary of the models <model_summary>`).
-
-Perplexity is defined as the exponentiated average log-likelihood of a
-sequence. If we have a tokenized sequence :math:`X = (x_0, x_1, \dots, x_t)`,
-then the perplexity of :math:`X` is,
-
-.. math::
-
-    \text{PPL}(X)
-    = \exp \left\{ {-\frac{1}{t}\sum_i^t \log p_\theta (x_i|x_{<i}) } \right\}
-
-where :math:`\log p_\theta (x_i|x_{<i})` is the log-likelihood of the ith
-token conditioned on the preceding tokens :math:`x_{<i}` according to our
-model. Intuitively, it can be thought of as an evaluation of the model's
-ability to predict uniformly among the set of specified tokens in a corpus.
-Importantly, this means that the tokenization procedure has a direct impact
-on a model's perplexity which should always be taken into consideration when
-comparing different models.
-
-This is also equivalent to the exponentiation of the cross-entropy between
-the data and model predictions. For more intuition about perplexity and its
-relationship to Bits Per Character (BPC) and data compression, check out this
-`fantastic blog post on The Gradient
-<https://thegradient.pub/understanding-evaluation-metrics-for-language-models/>`_.
-
-Calculating PPL with fixed-length models
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-If we weren't limited by a model's context size, we would evaluate the
-model's perplexity by autoregressively factorizing a sequence and
-conditioning on the entire preceding subsequence at each step, as shown
-below.
-
-.. image:: imgs/ppl_full.gif
-    :width: 600
-    :alt: Full decomposition of a sequence with unlimited context length
-
-When working with approximate models, however, we typically have a constraint
-on the number of tokens the model can process. The largest version
-of :doc:`GPT-2 <model_doc/gpt2>`, for example, has a fixed length of 1024
-tokens, so we cannot calculate :math:`p_\theta(x_t|x_{<t})` directly when
-:math:`t` is greater than 1024.
-
-Instead, the sequence is typically broken into subsequences equal to the
-model's maximum input size. If a model's max input size is :math:`k`, we
-then approximate the likelihood of a token :math:`x_t` by conditioning only
-on the :math:`k-1` tokens that precede it rather than the entire context.
-When evaluating the model's perplexity of a sequence, a tempting but
-suboptimal approach is to break the sequence into disjoint chunks and
-add up the decomposed log-likelihoods of each segment independently.
-
-.. image:: imgs/ppl_chunked.gif
-    :width: 600
-    :alt: Suboptimal PPL not taking advantage of full available context
-
-This is quick to compute since the perplexity of each segment can be computed
-in one forward pass, but serves as a poor approximation of the
-fully-factorized perplexity and will typically yield a higher (worse) PPL
-because the model will have less context at most of the prediction steps.
-
-Instead, the PPL of fixed-length models should be evaluated with a
-sliding-window strategy. This involves repeatedly sliding the
-context window so that the model has more context when making each
-prediction.
-
-.. image:: imgs/ppl_sliding.gif
-    :width: 600
-    :alt: Sliding window PPL taking advantage of all available context
-
-This is a closer approximation to the true decomposition of the
-sequence probability and will typically yield a more favorable score.
-The downside is that it requires a separate forward pass for each token in
-the corpus. A good practical compromise is to employ a strided sliding
-window, moving the context by larger strides rather than sliding by 1 token a
-time. This allows computation to procede much faster while still giving the
-model a large context to make predictions at each step.
-
-Example: Calculating perplexity with GPT-2 in 🤗 Transformers
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Let's demonstrate this process with GPT-2.
-
-.. code-block:: python
-
-    from transformers import GPT2LMHeadModel, GPT2TokenizerFast
-    device = 'cuda'
-    model_id = 'gpt2-large'
-    model = GPT2LMHeadModel.from_pretrained(model_id).to(device)
-    tokenizer = GPT2TokenizerFast.from_pretrained(model_id)
-
-We'll load in the WikiText-2 dataset and evaluate the perplexity using a few
-different sliding-window strategies. Since this dataset is small and we're
-just doing one forward pass over the set, we can just load and encode the
-entire dataset in memory.
-
-.. code-block:: python
-
-    from nlp import load_dataset
-    test = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test')
-    encodings = tokenizer('\n\n'.join(test['text']), return_tensors='pt')
-
-With 🤗 Transformers, we can simply pass the ``input_ids`` as the ``labels``
-to our model, and the average log-likelihood for each token is returned as
-the loss. With our sliding window approach, however, there is overlap in the
-tokens we pass to the model at each iteration. We don't want the
-log-likelihood for the tokens we're just treating as context to be included
-in our loss, so we can set these targets to ``-100`` so that they are
-ignored. The following is an example of how we could do this with a stride of
-``512``. This means that the model will have at least 512 tokens for context
-when calculating the conditional likelihood of any one token (provided there
-are 512 preceding tokens available to condition on).
-
-.. code-block:: python
-
-    max_length = model.config.n_positions
-    stride = 512
-
-    lls = []
-    for i in tqdm(range(0, encodings.input_ids.size(1), stride)):
-        begin_loc = max(i + stride - max_length, 0)
-        end_loc = i + stride
-        input_ids = encodings.input_ids[:,begin_loc:end_loc].to(device)
-        target_ids = input_ids.clone()
-        target_ids[:,:-stride] = -100
-
-        with torch.no_grad():
-            outputs = model(input_ids, labels=target_ids)
-            log_likelihood = outputs[0] * stride
-
-        lls.append(log_likelihood)
-    
-    ppl = torch.exp(torch.stack(lls).sum() / i)
-
-Running this with the stride length equal to the max input length is
-equivalent to the suboptimal, non-sliding-window strategy we discussed above.
-The smaller the stride, the more context the model will have in making each
-prediction, and the better the reported perplexity will typically be.
-
-When we run the above with ``stride = 1024``, i.e. no overlap, the resulting
-PPL is ``19.64``, which is about the same as the ``19.93`` reported in the
-GPT-2 paper. By using ``stride = 512`` and thereby employing our striding
-window strategy, this jumps down to ``16.53``. This is not only a more
-favorable score, but is calculated in a way that is closer to the true
-autoregressive decomposition of a sequence likelihood.
@@ -1,7 +1,7 @@
 Philosophy
 ==========

-🤗 Transformers is an opinionated library built for:
+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
@@ -1,373 +0,0 @@
-Preprocessing data
-==================
-
-In this tutorial, we'll explore how to preprocess your data using 🤗 Transformers. The main tool for this is what we
-
-call a :doc:`tokenizer <main_classes/tokenizer>`. You can build one using the tokenizer class associated to the model
-you would like to use, or directly with the :class:`~transformers.AutoTokenizer` class.
-
-As we saw in the :doc:`quicktour </quicktour>`, the tokenizer will first split a given text in words (or part of words,
-punctuation symbols, etc.) usually called `tokens`. Then it will convert those `tokens` into numbers, to be able to
-build a tensor out of them and feed them to the model. It will also add any additional inputs the model might expect to
-work properly.
-
-.. note::
-
-    If you plan on using a pretrained model, it's important to use the associated pretrained tokenizer: it will split
-    the text you give it in tokens the same way for the pretraining corpus, and it will use the same correspondence
-    token to index (that we usually call a `vocab`) as during pretraining.
-
-To automatically download the vocab used during pretraining or fine-tuning a given model, you can use the 
-:func:`~transformers.AutoTokenizer.from_pretrained` method:
-
-::
-
-    from transformers import AutoTokenizer
-    tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
-
-Base use
-~~~~~~~~
-
-A :class:`~transformers.PreTrainedTokenizer` has many methods, but the only one you need to remember for preprocessing
-is its ``__call__``: you just need to feed your sentence to your tokenizer object.
-
-::
-
-    encoded_input = tokenizer("Hello, I'm a single sentence!")
-    print(encoded_input)
-
-This will return a dictionary string to list of ints like this one:
-
-::
-
-    {'input_ids': [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102], 
-     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
-     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
-
-The `input_ids <glossary.html#input-ids>`__ are the indices corresponding to each token in our sentence. We will see
-below what the `attention_mask <glossary.html#attention-mask>`__ is used for and in
-:ref:`the next section <sentence-pairs>` the goal of `token_type_ids <glossary.html#token-type-ids>`__.
-
-The tokenizer can decode a list of token ids in a proper sentence:
-
-::
-
-    tokenizer.decode(encoded_input["input_ids"])
-
-which should return
-
-::
-
-    "[CLS] Hello, I'm a single sentence! [SEP]"
-
-As you can see, the tokenizer automatically added some special tokens that the model expect. Not all model need special
-tokens; for instance, if we had used` gtp2-medium` instead of `bert-base-cased` to create our tokenizer, we would have
-
-seen the same sentence as the original one here. You can disable this behavior (which is only advised if you have added
-those special tokens yourself) by passing ``add_special_tokens=False``.
-
-If you have several sentences you want to process, you can do this efficiently by sending them as a list to the
-tokenizer:
-
-::
-
-    batch_sentences = ["Hello I'm a single sentence",
-                       "And another sentence",
-                       "And the very very last one"]
-    encoded_inputs = tokenizer(batch_sentences)
-    print(encoded_inputs)
-
-We get back a dictionary once again, this time with values being list of list of ints:
-
-::
-
-    {'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
-                   [101, 1262, 1330, 5650, 102],
-                   [101, 1262, 1103, 1304, 1304, 1314, 1141, 102]],
-     'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0],
-                        [0, 0, 0, 0, 0],
-                        [0, 0, 0, 0, 0, 0, 0, 0]],
-     'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1],
-                        [1, 1, 1, 1, 1],
-                        [1, 1, 1, 1, 1, 1, 1, 1]]}
-
-If the purpose of sending several sentences at a time to the tokenizer is to build a batch to feed the model, you will
-probably want:
-
- To pad each sentence to the maximum length there is in your batch.
- To truncate each sentence to the maximum length the model can accept (if applicable).
- To return tensors.
-
-You can do all of this by using the following options when feeding your list of sentences to the tokenizer:
-
-::
-
-    ## PYTORCH CODE
-    batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
-    print(batch)
-    ## TENSORFLOW CODE
-    batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="tf")
-    print(batch)
-
-which should now return a dictionary string to tensor like this:
-
-::
-
-    {'input_ids': tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
-                          [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
-                          [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
-     'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
-     'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
-                               [1, 1, 1, 1, 1, 0, 0, 0, 0],
-                               [1, 1, 1, 1, 1, 1, 1, 1, 0]])}
-
-We can now see what the `attention_mask <glossary.html#attention-mask>`__ is all about: it points out which tokens the
-model should pay attention to and which ones it should not (because they represent padding in this case).
-
-
-Note that if your model does not have a maximum length associated to it, the command above will throw a warning. You
-can safely ignore it. You can also pass ``verbose=False`` to stop the tokenizer to throw those kinds of warnings.
-
-.. _sentence-pairs:
-
-Preprocessing pairs of sentences
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Sometimes you need to feed pair of sentences to your model. For instance, if you want to classify if two sentences in a
-pair are similar, or for question-answering models, which take a context and a question. For BERT models, the input is
-then represented like this:
-
-::
-
-    [CLS] Sequence A [SEP] Sequence B [SEP]
-
-You can encode a pair of sentences in the format expected by your model by supplying the two sentences as two arguments
-
-(not a list since a list of two sentences will be interpreted as a batch of two single sentences, as we saw before).
-
-
-::
-
-    encoded_input = tokenizer("How old are you?", "I'm 6 years old")
-    print(encoded_input)
-
-This will once again return a dict string to list of ints:
-
-::
-
-    {'input_ids': [101, 1731, 1385, 1132, 1128, 136, 102, 146, 112, 182, 127, 1201, 1385, 102], 
-     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], 
-     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
-
-This shows us what the `token_type_ids <glossary.html#token-type-ids>`__ are for: they indicate to the model which part
-of the inputs correspond to the first sentence and which part corresponds to the second sentence. Note that
-`token_type_ids` are not required or handled by all models. By default, a tokenizer will only return the inputs that
-its associated model expects. You can force the return (or the non-return) of any of those special arguments by
-using ``return_input_ids`` or ``return_token_type_ids``.
-
-If we decode the token ids we obtained, we will see that the special tokens have been properly added.
-
-::
-
-    tokenizer.decode(encoded_input["input_ids"])
-
-will return:
-
-::
-
-    "[CLS] How old are you? [SEP] I'm 6 years old [SEP]"
-
-If you have a list of pairs of sequences you want to process, you should feed them as two lists to your tokenizer: the
-list of first sentences and the list of second sentences:
-
-::
-
-    batch_sentences = ["Hello I'm a single sentence",
-                       "And another sentence",
-                       "And the very very last one"]
-    batch_of_second_sentences = ["I'm a sentence that goes with the first sentence",
-                                 "And I should be encoded with the second sentence",
-                                 "And I go with the very last one"]
-    encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences)
-    print(encoded_inputs)
-
-will return a dict with the values being list of lists of ints:
-
-::
-
-    {'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102, 146, 112, 182, 170, 5650, 1115, 2947, 1114, 1103, 1148, 5650, 102], 
-                   [101, 1262, 1330, 5650, 102, 1262, 146, 1431, 1129, 12544, 1114, 1103, 1248, 5650, 102], 
-                   [101, 1262, 1103, 1304, 1304, 1314, 1141, 102, 1262, 146, 1301, 1114, 1103, 1304, 1314, 1141, 102]], 
-    'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                       [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 
-    'attention_mask': [[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, 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, 1, 1, 1]]}
-
-To double-check what is fed to the model, we can decode each list in `input_ids` one by one:
-
-::
-
-    for ids in encoded_inputs["input_ids"]:
-        print(tokenizer.decode(ids))
-
-which will return:
-
-::
-
-    [CLS] Hello I'm a single sentence [SEP] I'm a sentence that goes with the first sentence [SEP]
-    [CLS] And another sentence [SEP] And I should be encoded with the second sentence [SEP]
-    [CLS] And the very very last one [SEP] And I go with the very last one [SEP]
-
-Once again, you can automatically pad your inputs to the maximum sentence length in the batch, truncate to the maximum
-length the model can accept and return tensors directly with the following:
-
-::
-
-    ## PYTORCH CODE
-    batch = tokenizer(batch_sentences, batch_of_second_sentences, padding=True, truncation=True, return_tensors="pt")
-    ## TENSORFLOW CODE
-    batch = tokenizer(batch_sentences, batch_of_second_sentences, padding=True, truncation=True, return_tensors="tf")
-
-Everything you always wanted to know about padding and truncation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-We have seen the commands that will work for most cases (pad your batch to the length of the maximum sentence and
-
-truncate to the maximum length the mode can accept). However, the API supports more strategies if you need them. The
-three arguments you need to know for this are :obj:`padding`, :obj:`truncation` and :obj:`max_length`.
-
- :obj:`padding` controls the padding. It can be a boolean or a string which should be:
-
-    - :obj:`True` or :obj:`'longest'` to pad to the longest sequence in the batch (doing no padding if you only provide
-      a single sequence).
-    - :obj:`'max_length'` to pad to a length specified by the :obj:`max_length` argument or the maximum length accepted
-      by the model if no :obj:`max_length` is provided (``max_length=None``). If you only provide a single sequence,
-      padding will still be applied to it. 
-    - :obj:`False` or :obj:`'do_not_pad'` to not pad the sequences. As we have seen before, this is the default
-      behavior.
-
- :obj:`truncation` controls the truncation. It can be a boolean or a string which should be:
-
-    - :obj:`True` or :obj:`'only_first'` truncate to a maximum length specified by the :obj:`max_length` argument or
-      the maximum length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will
-      only truncate the first sentence of a pair if a pair of sequence (or a batch of pairs of sequences) is provided.
-    - :obj:`'only_second'` truncate to a maximum length specified by the :obj:`max_length` argument or the maximum
-      length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will only truncate
-      the second sentence of a pair if a pair of sequence (or a batch of pairs of sequences) is provided.
-    - :obj:`'longest_first'` truncate to a maximum length specified by the :obj:`max_length` argument or the maximum
-      length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will truncate token
-      by token, removing a token from the longest sequence in the pair until the proper length is reached.
-    - :obj:`False` or :obj:`'do_not_truncate'` to not truncate the sequences. As we have seen before, this is the
-      default behavior.
-
- :obj:`max_length` to control the length of the padding/truncation. It can be an integer or :obj:`None`, in which case
-  it will default to the maximum length the model can accept. If the model has no specific maximum input length,
-  truncation/padding to :obj:`max_length` is deactivated.
-
-Here is a table summarizing the recommend way to setup padding and truncation. If you use pair of inputs sequence in
-any of the following examples, you can replace :obj:`truncation=True` by a :obj:`STRATEGY` selected in 
-:obj:`['only_first', 'only_second', 'longest_first']`, i.e. :obj:`truncation='only_second'` or
-:obj:`truncation= 'longest_first'` to control how both sequence in the pair are truncated as detailed before.
-
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-| Truncation                           | Padding                           | Instruction                                                                                 |
-+======================================+===================================+=============================================================================================+
-| no truncation                        | no padding                        | :obj:`tokenizer(batch_sentences)`                                                           |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True)` or                                          |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='longest')`                                        |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max model input length | :obj:`tokenizer(batch_sentences, padding='max_length')`                                     |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to specific length        | :obj:`tokenizer(batch_sentences, padding='max_length', max_length=42)`                      |
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-| truncation to max model input length | no padding                        | :obj:`tokenizer(batch_sentences, truncation=True)` or                                       |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, truncation=STRATEGY)`                                      |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True, truncation=True)` or                         |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding=True, truncation=STRATEGY)`                        |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max model input length | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=True)` or                 |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY)`                |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to specific length        | Not possible                                                                                |
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-| truncation to specific length        | no padding                        | :obj:`tokenizer(batch_sentences, truncation=True, max_length=42)` or                        |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, truncation=STRATEGY, max_length=42)`                       |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True, truncation=True, max_length=42)` or          |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding=True, truncation=STRATEGY, max_length=42)`         |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max model input length | Not possible                                                                                |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to specific length        | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=True, max_length=42)` or  |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY, max_length=42)` |
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-
-Pre-tokenized inputs
-~~~~~~~~~~~~~~~~~~~~
-
-The tokenizer also accept pre-tokenized inputs. This is particularly useful when you want to compute labels and extract
-predictions in `named entity recognition (NER) <https://en.wikipedia.org/wiki/Named-entity_recognition>`__ or
-`part-of-speech tagging (POS tagging) <https://en.wikipedia.org/wiki/Part-of-speech_tagging>`__.
-
-If you want to use pre-tokenized inputs, just set :obj:`is_pretokenized=True` when passing your inputs to the
-tokenizer. For instance:
-
-::
-
-    encoded_input = tokenizer(["Hello", "I'm", "a", "single", "sentence"], is_pretokenized=True)
-    print(encoded_input)
-
-will return:
-
-::
-
-    {'input_ids': [101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
-     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0], 
-     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}
-
-Note that the tokenizer still adds the ids of special tokens (if applicable) unless you pass
-``add_special_tokens=False``.
-
-This works exactly as before for batch of sentences or batch of pairs of sentences. You can encode a batch of sentences
-like this:
-
-::
-
-    batch_sentences = [["Hello", "I'm", "a", "single", "sentence"],
-                       ["And", "another", "sentence"],
-                       ["And", "the", "very", "very", "last", "one"]]
-    encoded_inputs = tokenizer(batch_sentences, is_pretokenized=True)
-
-or a batch of pair sentences like this:
-
-::
-
-    batch_of_second_sentences = [["I'm", "a", "sentence", "that", "goes", "with", "the", "first", "sentence"],
-                                 ["And", "I", "should", "be", "encoded", "with", "the", "second", "sentence"],
-                                 ["And", "I", "go", "with", "the", "very", "last", "one"]]
-    encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences, is_pretokenized=True)
-
-And you can add padding, truncation as well as directly return tensors like before:
-
-::
-
-    ## PYTORCH CODE
-    batch = tokenizer(batch_sentences,
-                      batch_of_second_sentences,
-                      is_pretokenized=True,
-                      padding=True,
-                      truncation=True,
-                      return_tensors="pt")
-    ## TENSORFLOW CODE
-    batch = tokenizer(batch_sentences,
-                      batch_of_second_sentences,
-                      is_pretokenized=True,
-                      padding=True,
-                      truncation=True,
-                      return_tensors="tf")
@@ -1,407 +1,379 @@
-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>`):
-
-.. code-block::
-
-    >>> 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:
-
-
-.. code-block::
-
-    >>> classifier('We are very happy to show you the 🤗 Transformers library.')
-    [{'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`, returning a list of dictionaries like this one:
-
-.. code-block::
-
-    >>> results = classifier(["We are very happy to show you the 🤗 Transformers library.",
-    ...            "We hope you don't hate it."])
-    >>> for result in results:
-    ...     print(f"label: {result['label']}, with score: {round(result['score'], 4)}")
-    label: POSITIVE, with score: 0.9998
-    label: NEGATIVE, with score: 0.5309
-
-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`:
-
-.. code-block::
-
-    >>> 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.
-
-.. code-block::
-
-    >>> ## 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):
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
-    >>> model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    >>> tokenizer = AutoTokenizer.from_pretrained(model_name)
-    >>> classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
-    >>> ## TENSORFLOW CODE
-    >>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
-    >>> # This model only exists in PyTorch, so we use the `from_pt` flag to import that model in TensorFlow.
-    >>> model = TFAutoModelForSequenceClassification.from_pretrained(model_name, from_pt=True)
-    >>> 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"
-    >>> pt_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"
-    >>> tf_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 (you can learn more about them in the :doc:`tokenizer_summary <tokenizer_summary>`, 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:
-
-.. code-block::
-
-    >>> inputs = tokenizer("We are very happy to show you the 🤗 Transformers library.")
-
-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:
-
-
-.. code-block::
-
-    >>> print(inputs)
-    {'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:
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> pt_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"
-    ... )
-    >>> ## TENSORFLOW CODE
-    >>> tf_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"
-    ... )
-
-The padding is automatically applied on the side expected by the model (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:
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> for key, value in pt_batch.items():
-    ...     print(f"{key}: {value.numpy().tolist()}")
-    input_ids: [[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: [[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]]
-    >>> ## TENSORFLOW CODE
-    >>> for key, value in tf_batch.items():
-    ...     print(f"{key}: {value.numpy().tolist()}")
-    input_ids: [[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: [[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 :doc:`here <preprocessing>`.
-
-Using the model
-^^^^^^^^^^^^^^^
-
-Once your input has been preprocessed by the tokenizer, you can send it directly 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 pass the
-dictionary keys directly to tensor, for a PyTorch model, you need to unpack the dictionary by adding :obj:`**`.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> pt_outputs = pt_model(**pt_batch)
-    >>> ## TENSORFLOW CODE
-    >>> tf_outputs = tf_model(tf_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.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> print(pt_outputs)
-    SequenceClassifierOutput(loss=None, logits=tensor([[-4.0833,  4.3364],
-            [ 0.0818, -0.0418]], grad_fn=<AddmmBackward>), hidden_states=None, attentions=None)
-    >>> ## TENSORFLOW CODE
-    >>> print(tf_outputs)
-    (<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
-    array([[-4.0832963 ,  4.336414  ],
-           [ 0.08181786, -0.04179301]], dtype=float32)>,)
-
-The model can return more than just the final activations, which is why the PyTorch output is a special class and the
-TensorFlow output is a tuple. Here we only asked for the final activations, so we get a tuple with one element on the
-TensorFlow side and a :class:`~transformers.modeling_outputs.SequenceClassifierOutput` with just the ``logits`` field
-filled on the PyTorch side.
-
-.. 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.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> import torch.nn.functional as F
-    >>> pt_predictions = F.softmax(pt_outputs.logits, dim=-1)
-    >>> ## TENSORFLOW CODE
-    >>> import tensorflow as tf
-    >>> tf_predictions = tf.nn.softmax(tf_outputs[0], axis=-1)
-
-We can see we get the numbers from before:
-
-.. code-block::
-
-    >>> ## TENSORFLOW CODE
-    >>> print(tf_predictions)
-    tf.Tensor(
-    [[2.2042994e-04 9.9977952e-01]
-     [5.3086340e-01 4.6913657e-01]], shape=(2, 2), dtype=float32)
-    >>> ## PYTORCH CODE
-    >>> print(pt_predictions)
-    tensor([[2.2043e-04, 9.9978e-01],
-            [5.3086e-01, 4.6914e-01]], grad_fn=<SoftmaxBackward>)
-
-If you have labels, you can provide them to the model, it will return a tuple with the loss and the final activations.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> import torch
-    >>> pt_outputs = pt_model(**pt_batch, labels = torch.tensor([1, 0]))
-    >>> ## TENSORFLOW CODE
-    >>> import tensorflow as tf
-    >>> tf_outputs = tf_model(tf_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 :doc:`training tutorial <training>` for more details.
-
-.. note::
-
-    Pytorch model outputs are special dataclasses so that you can get autocompletion for their attributes in an IDE.
-    They also behave like a tuple or a dictionary (e.g., you can index with an integer, a slice or a string) in which
-    case the attributes not set (that have :obj:`None` values) are ignored.
-
-Once your model is fine-tuned, you can save it with its tokenizer in 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
-    >>> pt_outputs = pt_model(**pt_batch, output_hidden_states=True, output_attentions=True)
-    >>> all_hidden_states, all_attentions = pt_outputs[-2:]
-    >>> ## TENSORFLOW CODE
-    >>> tf_outputs = tf_model(tf_batch, output_hidden_states=True, output_attentions=True)
-    >>> all_hidden_states, all_attentions = tf_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. As
-:class:`~transformers.AutoModelForSequenceClassification` (or  :class:`~transformers.TFAutoModelForSequenceClassification`
-if you are using TensorFlow)` was used, 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:
-
-.. code-block::
-
-    >>> ## 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).
-
-.. code-block::
-
-    >>> ## 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:
-
-.. code-block::
-
-    >>> ## 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)
+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': 0.999799370765686}]
+
+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': 0.999799370765686},
+     {'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, 19081, 3075, 1012, 102],
+     'attention_mask': [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, 19081, 3075,  1012,   102],
+                          [  101,  2057,  3246,  2017,  2123,  1005,  1056,  5223,  2009,  1012,  102,     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, 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)
@@ -0,0 +1,89 @@
+Serialization best-practices
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+This section explain how you can save and re-load a fine-tuned model (BERT, GPT, GPT-2 and Transformer-XL).
+There are three types of files you need to save to be able to reload a fine-tuned model:
+
+
+* the model itself which should be saved following PyTorch serialization `best practices <https://pytorch.org/docs/stable/notes/serialization.html#best-practices>`__\ ,
+* the configuration file of the model which is saved as a JSON file, and
+* the vocabulary (and the merges for the BPE-based models GPT and GPT-2).
+
+The *default filenames* of these files are as follow:
+
+
+* the model weights file: ``pytorch_model.bin``\ ,
+* the configuration file: ``config.json``\ ,
+* the vocabulary file: ``vocab.txt`` for BERT and Transformer-XL, ``vocab.json`` for GPT/GPT-2 (BPE vocabulary),
+* for GPT/GPT-2 (BPE vocabulary) the additional merges file: ``merges.txt``.
+
+**If you save a model using these *default filenames*\ , you can then re-load the model and tokenizer using the ``from_pretrained()`` method.**
+
+Here is the recommended way of saving the model, configuration and vocabulary to an ``output_dir`` directory and reloading the model and tokenizer afterwards:
+
+.. code-block:: python
+
+   from transformers import WEIGHTS_NAME, CONFIG_NAME
+
+   output_dir = "./models/"
+
+   # Step 1: Save a model, configuration and vocabulary that you have fine-tuned
+
+   # If we have a distributed model, save only the encapsulated model
+   # (it was wrapped in PyTorch DistributedDataParallel or DataParallel)
+   model_to_save = model.module if hasattr(model, 'module') else model
+
+   # If we save using the predefined names, we can load using `from_pretrained`
+   output_model_file = os.path.join(output_dir, WEIGHTS_NAME)
+   output_config_file = os.path.join(output_dir, CONFIG_NAME)
+
+   torch.save(model_to_save.state_dict(), output_model_file)
+   model_to_save.config.to_json_file(output_config_file)
+   tokenizer.save_pretrained(output_dir)
+
+   # Step 2: Re-load the saved model and vocabulary
+
+   # Example for a Bert model
+   model = BertForQuestionAnswering.from_pretrained(output_dir)
+   tokenizer = BertTokenizer.from_pretrained(output_dir)  # Add specific options if needed
+   # Example for a GPT model
+   model = OpenAIGPTDoubleHeadsModel.from_pretrained(output_dir)
+   tokenizer = OpenAIGPTTokenizer.from_pretrained(output_dir)
+
+Here is another way you can save and reload the model if you want to use specific paths for each type of files:
+
+.. code-block:: python
+
+   output_model_file = "./models/my_own_model_file.bin"
+   output_config_file = "./models/my_own_config_file.bin"
+   output_vocab_file = "./models/my_own_vocab_file.bin"
+
+   # Step 1: Save a model, configuration and vocabulary that you have fine-tuned
+
+   # If we have a distributed model, save only the encapsulated model
+   # (it was wrapped in PyTorch DistributedDataParallel or DataParallel)
+   model_to_save = model.module if hasattr(model, 'module') else model
+
+   torch.save(model_to_save.state_dict(), output_model_file)
+   model_to_save.config.to_json_file(output_config_file)
+   tokenizer.save_vocabulary(output_vocab_file)
+
+   # Step 2: Re-load the saved model and vocabulary
+
+   # We didn't save using the predefined WEIGHTS_NAME, CONFIG_NAME names, we cannot load using `from_pretrained`.
+   # Here is how to do it in this situation:
+
+   # Example for a Bert model
+   config = BertConfig.from_json_file(output_config_file)
+   model = BertForQuestionAnswering(config)
+   state_dict = torch.load(output_model_file)
+   model.load_state_dict(state_dict)
+   tokenizer = BertTokenizer(output_vocab_file, do_lower_case=args.do_lower_case)
+
+   # Example for a GPT model
+   config = OpenAIGPTConfig.from_json_file(output_config_file)
+   model = OpenAIGPTDoubleHeadsModel(config)
+   state_dict = torch.load(output_model_file)
+   model.load_state_dict(state_dict)
+   tokenizer = OpenAIGPTTokenizer(output_vocab_file)
+
@@ -1,243 +0,0 @@
-Tokenizer summary
-----------------
-
-In this page, we will have a closer look at tokenization. As we saw in
-:doc:`the preprocessing tutorial <preprocessing>`, tokenizing a text is splitting it into words or subwords, which then
-are converted to ids. The second part is pretty straightforward, here we will focus on the first part. More
-specifically, we will look at the three main different kinds of tokenizers used in 🤗 Transformers:
-:ref:`Byte-Pair Encoding (BPE) <byte-pair-encoding>`, :ref:`WordPiece <wordpiece>` and
-:ref:`SentencePiece <sentencepiece>`, and provide examples of models using each of those.
-
-Note that on each model page, you can look at the documentation of the associated tokenizer to know which of those
-algorithms the pretrained model used. For instance, if we look at :class:`~transformers.BertTokenizer`, we can see it's
-using :ref:`WordPiece <wordpiece>`.
-
-Introduction to tokenization
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Splitting a text in smaller chunks is a task that's harder than it looks, and there are multiple ways of doing it. For
-instance, let's look at the sentence "Don't you love 🤗 Transformers? We sure do." A first simple way of tokenizing
-this text is just to split it by spaces, which would give:
-
-::
-
-    ["Don't", "you", "love", "🤗", "Transformers?", "We", "sure", "do."]
-
-This is a nice first step, but if we look at the tokens "Transformers?" or "do.", we can see we can do better. Those
-will be different than the tokens "Transformers" and "do" for our model, so we should probably take the punctuation
-into account. This would give:
-
-::
-
-    ["Don", "'", "t", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
-
-which is better already. One thing that is annoying though is how it dealt with "Don't". "Don't" stands for do not, so
-it should probably be better tokenized as ``["Do", "n't"]``. This is where things start getting more complicated, and
-part of the reason each kind of model has its own tokenizer class. Depending on the rules we apply to split our texts
-into tokens, we'll get different tokenized versions of the same text. And of course, a given pretrained model won't
-perform properly if you don't use the exact same rules as the persons who pretrained it.
-
-`spaCy <https://spacy.io/>`__ and `Moses <http://www.statmt.org/moses/?n=Development.GetStarted>`__ are two popular
-rule-based tokenizers. On the text above, they'd output something like:
-
-::
-
-    ["Do", "n't", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
-
-Space/punctuation-tokenization and rule-based tokenization are both examples of word tokenization, which is splitting a
-sentence into words. While it's the most intuitive way to separate texts in smaller chunks, it can have a problem when
-you have a huge corpus: it usually yields a very big vocabulary (the set of all unique tokens used).
-:doc:`Transformer XL <model_doc/transformerxl>` for instance uses space/punctuation-tokenization, and has a vocabulary
-size of 267,735!
-
-A huge vocabulary size means a huge embedding matrix at the start of the model, which will cause memory problems.
-TransformerXL deals with it by using a special kind of embeddings called adaptive embeddings, but in general,
-transformers models rarely have a vocabulary size greater than 50,000, especially if they are trained on a single
-language.
-
-So if tokenizing on words is unsatisfactory, we could go on the opposite direction and simply tokenize on characters.
-While it's very simple and would save a lot of memory, this doesn't allow the model to learn representations of texts
-as meaningful as when using a word tokenization, leading to a loss of performance. So to get the best of both worlds,
-all transformers models use a hybrid between word-level and character-level tokenization called subword tokenization.
-
-Subword tokenization
-^^^^^^^^^^^^^^^^^^^^
-
-Subword tokenization algorithms rely on the principle that most common words should be left as is, but rare words
-should be decomposed in meaningful subword units. For instance "annoyingly" might be considered a rare word and
-decomposed as "annoying" and "ly". This is especially useful in agglutinative languages such as Turkish, where you can
-form (almost) arbitrarily long complex words by stringing together some subwords.
-
-This allows the model to keep a reasonable vocabulary while still learning useful representations for common words or
-subwords. This also enables the model to process words it has never seen before, by decomposing them into
-subwords it knows. For instance, the base :class:`~transformers.BertTokenizer` will tokenize "I have a new GPU!" like
-this:
-
-::
-
-    >>> from transformers import BertTokenizer
-    >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-    >>> tokenizer.tokenize("I have a new GPU!")
-    ['i', 'have', 'a', 'new', 'gp', '##u', '!']
-
-Since we are considering the uncased model, the sentence was lowercased first. Then all the words were present in the
-vocabulary of the tokenizer, except for "gpu", so the tokenizer split it in subwords it knows: "gp" and "##u". The "##"
-means that the rest of the token should be attached to the previous one, without space (for when we need to decode
-predictions and reverse the tokenization).
-
-Another example is when we use the base :class:`~transformers.XLNetTokenizer` to tokenize our previous text:
-
-::
-
-    >>> from transformers import XLNetTokenizer
-    >>> tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
-    >>> tokenizer.tokenize("Don't you love 🤗 Transformers? We sure do.")
-    ['▁Don', "'", 't', '▁you', '▁love', '▁', '🤗', '▁', 'Transform', 'ers', '?', '▁We', '▁sure', '▁do', '.']
-
-We'll get back to the meaning of those '▁' when we look at :ref:`SentencePiece <sentencepiece>` but you can see
-Transformers has been split into "Transform" and "ers".
-
-Let's now look at how the different subword tokenization algorithms work. Note that they all rely on some form of
-training which is usually done on the corpus the corresponding model will be trained on.
-
-.. _byte-pair-encoding:
-
-Byte-Pair Encoding
-~~~~~~~~~~~~~~~~~~
-
-Byte-Pair Encoding was introduced in `this paper <https://arxiv.org/abs/1508.07909>`__. It relies on a pretokenizer
-splitting the training data into words, which can be a simple space tokenization
-(:doc:`GPT-2 <model_doc/gpt2>` and :doc:`Roberta <model_doc/roberta>` uses this for instance) or a rule-based tokenizer
-(:doc:`XLM <model_doc/xlm>` use Moses for most languages, as does :doc:`FlauBERT <model_doc/flaubert>`),
-
-:doc:`GPT <model_doc/gpt>` uses Spacy and ftfy, and counts the frequency of each word in the training corpus.
-
-It then begins from the list of all characters, and will learn merge rules to form a new token from two symbols in the
-vocabulary until it has learned a vocabulary of the desired size (this is a hyperparameter to pick).
-
-Let's say that after the pre-tokenization we have the following words (the number indicating the frequency of each
-word):
-
-::
-
-    ('hug', 10), ('pug', 5), ('pun', 12), ('bun', 4), ('hugs', 5)
-
-Then the base vocabulary is ['b', 'g', 'h', 'n', 'p', 's', 'u'] and all our words are first split by character:
-
-::
-
-    ('h' 'u' 'g', 10), ('p' 'u' 'g', 5), ('p' 'u' 'n', 12), ('b' 'u' 'n', 4), ('h' 'u' 'g' 's', 5)
-
-We then take each pair of symbols and look at the most frequent. For instance 'hu' is present `10 + 5 = 15` times (10
-times in the 10 occurrences of 'hug', 5 times in the 5 occurrences of 'hugs'). The most frequent here is 'ug', present
-`10 + 5 + 2 + 5 = 22` times in total. So the first merge rule the tokenizer learns is to group all 'u' and 'g' together
-then it adds 'ug' to the vocabulary. Our corpus then becomes
-
-::
-
-    ('h' 'ug', 10), ('p' 'ug', 5), ('p' 'u' 'n', 12), ('b' 'u' 'n', 4), ('h' 'ug' 's', 5)
-
-and we continue by looking at the next most common pair of symbols. It's 'un', present 16 times, so we merge those two
-and add 'un' to the vocabulary. Then it's 'hug' (as 'h' + 'ug'), present 15 times, so we merge those two and add 'hug'
-to the vocabulary.
-
-At this stage, the vocabulary is ``['b', 'g', 'h', 'n', 'p', 's', 'u', 'ug', 'un', 'hug']`` and our corpus is
-represented as
-
-::
-
-    ('hug', 10), ('p' 'ug', 5), ('p' 'un', 12), ('b' 'un', 4), ('hug' 's', 5)
-
-If we stop there, the tokenizer can apply the rules it learned to new words (as long as they don't contain characters that
-were not in the base vocabulary). For instance 'bug' would be tokenized as ``['b', 'ug']`` but mug would be tokenized as
-``['<unk>', 'ug']`` since the 'm' is not in the base vocabulary. This doesn't happen to letters in general (since the
-base corpus uses all of them), but to special characters like emojis.
-
-As we said before, the vocabulary size (which is the base vocabulary size + the number of merges) is a hyperparameter
-to choose. For instance :doc:`GPT <model_doc/gpt>` has a vocabulary size of 40,478 since they have 478 base characters
-and chose to stop the training of the tokenizer at 40,000 merges.
-
-Byte-level BPE
-^^^^^^^^^^^^^^
-
-To deal with the fact the base vocabulary needs to get all base characters, which can be quite big if one allows for
-all unicode characters, the
-`GPT-2 paper <https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`__
-introduces a clever trick, which is to use bytes as the base vocabulary (which gives a size of 256). With some
-additional rules to deal with punctuation, this manages to be able to tokenize every text without needing an unknown
-token. For instance, the :doc:`GPT-2 model <model_doc/gpt>` has a vocabulary size of 50,257, which corresponds to the
-256 bytes base tokens, a special end-of-text token and the symbols learned with 50,000 merges.
-
-.. _wordpiece:
-
-WordPiece
-=========
-
-WordPiece is the subword tokenization algorithm used for :doc:`BERT <model_doc/bert>` (as well as
-:doc:`DistilBERT <model_doc/distilbert>` and :doc:`Electra <model_doc/electra>`) and was outlined in
-`this paper <https://static.googleusercontent.com/media/research.google.com/ja//pubs/archive/37842.pdf>`__. It relies
-on the same base as BPE, which is to initialize the vocabulary to every character present in the corpus and
-progressively learn a given number of merge rules, the difference is that it doesn't choose the pair that is the most
-frequent but the one that will maximize the likelihood on the corpus once merged.
-
-What does this mean? Well, in the previous example, it means we would only merge 'u' and 'g' if the probability of
-having 'ug' divided by the probability of having 'u' then 'g' is greater than for any other pair of symbols. It's
-subtly different from what BPE does in the sense that it evaluates what it "loses" by merging two symbols and makes
-sure it's `worth it`.
-
-.. _unigram:
-
-Unigram
-=======
-
-Unigram is a subword tokenization algorithm introduced in `this paper <https://arxiv.org/pdf/1804.10959.pdf>`__.
-Instead of starting with a group of base symbols and learning merges with some rule, like BPE or WordPiece, it starts
-from a large vocabulary (for instance, all pretokenized words and the most common substrings) that it will trim down
-progressively. It's not used directly for any of the pretrained models in the library, but it's used in conjunction
-with :ref:`SentencePiece <sentencepiece>`.
-
-More specifically, at a given step, unigram computes a loss from the corpus we have and the current vocabulary, then,
-for each subword, evaluate how much the loss would augment if the subword was removed from the vocabulary. It then
-sorts the subwords by this quantity (that represents how worse the loss becomes if the token is removed) and removes
-all the worst p tokens (for instance p could be 10% or 20%). It then repeats the process until the vocabulary has
-reached the desired size, always keeping the base characters (to be able to tokenize any word written with them, like
-BPE or WordPiece).
-
-Contrary to BPE and WordPiece that work out rules in a certain order that you can then apply in the same order when
-tokenizing new text, Unigram will have several ways of tokenizing a new text. For instance, if it ends up with the
-vocabulary
-
-::
-
-    ['b', 'g', 'h', 'n', 'p', 's', 'u', 'ug', 'un', 'hug']
-
-we had before, it could tokenize "hugs" as ``['hug', 's']``, ``['h', 'ug', 's']`` or ``['h', 'u', 'g', 's']``. So which
-one choose? On top of saving the vocabulary, the trained tokenizer will save the probability of each token in the
-training corpus. You can then give a probability to each tokenization (which is the product of the probabilities of the
-tokens forming it) and pick the most likely one (or if you want to apply some data augmentation, you could sample one
-of the tokenization according to their probabilities).
-
-Those probabilities define the loss that trains the tokenizer: if our corpus consists of the
-words :math:`x_{1}, \dots, x_{N}` and if for the word :math:`x_{i}` we note :math:`S(x_{i})` the set of all possible
-tokenizations of :math:`x_{i}` (with the current vocabulary), then the loss is defined as
-
-.. math::
-    \mathcal{L} = -\sum_{i=1}^{N} \log \left ( \sum_{x \in S(x_{i})} p(x) \right )
-
-.. _sentencepiece:
-
-SentencePiece
-=============
-
-All the methods we have been looking at so far required some form of pretokenization, which has a central problem: not
-all languages use spaces to separate words. This is a problem :doc:`XLM <model_doc/xlm>` solves by using specific
-pretokenizers for each of those languages (in this case, Chinese, Japanese and Thai). To solve this problem,
-SentencePiece (introduced in `this paper <https://arxiv.org/pdf/1808.06226.pdf>`__) treats the input as a raw stream,
-includes the space in the set of characters to use, then uses BPE or unigram to construct the appropriate vocabulary.
-
-That's why in the example we saw before using :class:`~transformers.XLNetTokenizer` (which uses SentencePiece), we had
-the '▁' character, that represents space. Decoding a tokenized text is then super easy: we just have to concatenate
-all of them together and replace '▁' with space.
-
-All transformers models in the library that use SentencePiece use it with unigram. Examples of models using it are
-:doc:`ALBERT <model_doc/albert>`, :doc:`XLNet <model_doc/xlnet>` or the :doc:`Marian framework <model_doc/marian>`.
@@ -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.

@@ -1,323 +0,0 @@
-Training and fine-tuning
-========================
-
-Model classes in 🤗 Transformers are designed to be compatible with native
-PyTorch and TensorFlow 2 and can be used seemlessly with either. In this
-quickstart, we will show how to fine-tune (or train from scratch) a model
-using the standard training tools available in either framework. We will also
-show how to use our included :func:`~transformers.Trainer` class which
-handles much of the complexity of training for you.
-
-This guide assume that you are already familiar with loading and use our
-models for inference; otherwise, see the :doc:`task summary <task_summary>`. We also assume
-that you are familiar with training deep neural networks in either PyTorch or
-TF2, and focus specifically on the nuances and tools for training models in
-🤗 Transformers.
-
-Sections:
-
-  * :ref:`pytorch`
-  * :ref:`tensorflow`
-  * :ref:`trainer`
-  * :ref:`additional-resources`
-
-.. _pytorch:
-
-Fine-tuning in native PyTorch
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Model classes in 🤗 Transformers that don't begin with ``TF`` are
-`PyTorch Modules <https://pytorch.org/docs/master/generated/torch.nn.Module.html>`_,
-meaning that you can use them just as you would any model in PyTorch for
-both inference and optimization.
-
-Let's consider the common task of fine-tuning a masked language model like
-BERT on a sequence classification dataset. When we instantiate a model with
-:func:`~transformers.PreTrainedModel.from_pretrained`, the model
-configuration and pre-trained weights
-of the specified model are used to initialize the model. The
-library also includes a number of task-specific final layers or 'heads' whose
-weights are instantiated randomly when not present in the specified
-pre-trained model. For example, instantiating a model with
-``BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)``
-will create a BERT model instance with encoder weights copied from the
-``bert-base-uncased`` model and a randomly initialized sequence
-classification head on top of the encoder with an output size of 2. Models
-are initialized in ``eval`` mode by default. We can call ``model.train()`` to
-put it in train mode.
-
-.. code-block:: python
-
-    from transformers import BertForSequenceClassification
-    model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
-    model.train()
-
-This is useful because it allows us to make use of the pre-trained BERT
-encoder and easily train it on whatever sequence classification dataset we
-choose. We can use any PyTorch optimizer, but our library also provides the
-:func:`~transformers.AdamW` optimizer which implements gradient bias
-correction as well as weight decay.
-
-.. code-block:: python
-
-    from transformers import AdamW
-    optimizer = AdamW(model.parameters(), lr=1e-5)
-
-The optimizer allows us to apply different hyperpameters for specific
-parameter groups. For example, we can apply weight decay to all parameters
-other than bias and layer normalization terms:
-
-.. code-block:: python
-
-    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': 0.01},
-        {'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=1e-5)
-    
-Now we can set up a simple dummy training batch using
-:func:`~transformers.PreTrainedTokenizer.__call__`. This returns a
-:func:`~transformers.BatchEncoding` instance which
-prepares everything we might need to pass to the model.
-
-.. code-block:: python
-
-    from transformers import BertTokenizer
-    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-    text_batch = ["I love Pixar.", "I don't care for Pixar."]
-    encoding = tokenizer(text_batch, return_tensors='pt', padding=True, truncation=True)
-    input_ids = encoding['input_ids']
-    attention_mask = encoding['attention_mask']
-
-When we call a classification model with the ``labels`` argument, the first
-returned element is the Cross Entropy loss between the predictions and the
-passed labels. Having already set up our optimizer, we can then do a
-backwards pass and update the weights:
-
-.. code-block:: python
-
-    labels = torch.tensor([1,0]).unsqueeze(0)
-    outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
-    loss = outputs.loss
-    loss.backward()
-    optimizer.step()
-
-Alternatively, you can just get the logits and calculate the loss yourself.
-The following is equivalent to the previous example:
-
-.. code-block:: python
-
-    from torch.nn import functional as F
-    labels = torch.tensor([1,0]).unsqueeze(0)
-    outputs = model(input_ids, attention_mask=attention_mask)
-    loss = F.cross_entropy(labels, outputs.logitd)
-    loss.backward()
-    optimizer.step()
-
-Of course, you can train on GPU by calling ``to('cuda')`` on the model and
-inputs as usual.
-
-We also provide a few learning rate scheduling tools. With the following, we
-can set up a scheduler which warms up for ``num_warmup_steps`` and then
-linearly decays to 0 by the end of training.
-
-.. code-block:: python
-
-    from transformers import get_linear_schedule_with_warmup
-    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_train_steps)
-
-Then all we have to do is call ``scheduler.step()`` after ``optimizer.step()``.
-
-.. code-block:: python
-
-    ...
-    loss.backward()
-    optimizer.step()
-    scheduler.step()
-
-We highly recommend using :func:`~transformers.Trainer`, discussed below,
-which conveniently handles the moving parts of training 🤗 Transformers models
-with features like mixed precision and easy tensorboard logging.
-
-
-Freezing the encoder
--------------------
-
-In some cases, you might be interested in keeping the weights of the
-pre-trained encoder frozen and optimizing only the weights of the head
-layers. To do so, simply set the ``requires_grad`` attribute to ``False`` on
-the encoder parameters, which can be accessed with the ``base_model``
-submodule on any task-specific model in the library:
-
-.. code-block:: python
-   
-    for param in model.base_model.parameters():
-        param.requires_grad = False
-
-
-.. _tensorflow:
-
-Fine-tuning in native TensorFlow 2
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Models can also be trained natively in TensorFlow 2. Just as with PyTorch,
-TensorFlow models can be instantiated with
-:func:`~transformers.PreTrainedModel.from_pretrained` to load the weights of
-the encoder from a pretrained model.
-
-.. code-block:: python
-
-    from transformers import TFBertForSequenceClassification
-    model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')
-
-Let's use ``tensorflow_datasets`` to load in the `MRPC dataset
-<https://www.tensorflow.org/datasets/catalog/glue#gluemrpc>`_ from GLUE. We
-can then use our built-in
-:func:`~transformers.data.processors.glue.glue_convert_examples_to_features`
-to tokenize MRPC and convert it to a TensorFlow ``Dataset`` object. Note that
-tokenizers are framework-agnostic, so there is no need to prepend ``TF`` to
-the pretrained tokenizer name.
-
-.. code-block:: python
-
-    from transformers import BertTokenizer, glue_convert_examples_to_features
-    import tensorflow_datasets as tfds
-    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-    data = tfds.load('glue/mrpc')
-    train_dataset = glue_convert_examples_to_features(data['train'], tokenizer, max_length=128, task='mrpc')
-    train_dataset = train_dataset.shuffle(100).batch(32).repeat(2)
-
-The model can then be compiled and trained as any Keras model:
-
-.. code-block:: python
-    
-    optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5)
-    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-    model.compile(optimizer=optimizer, loss=loss)
-    model.fit(train_dataset, epochs=2, steps_per_epoch=115)
-
-With the tight interoperability between TensorFlow and PyTorch models, you
-can even save the model and then reload it as a PyTorch model (or vice-versa):
-
-.. code-block:: python
-
-    from transformers import BertForSequenceClassification
-    model.save_pretrained('./my_mrpc_model/')
-    pytorch_model = BertForSequenceClassification.from_pretrained('./my_mrpc_model/', from_tf=True)
-
-
-.. _trainer:
-
-Trainer
-^^^^^^^
-
-We also provide a simple but feature-complete training and evaluation
-interface through :func:`~transformers.Trainer` and
-:func:`~transformers.TFTrainer`. You can train, fine-tune,
-and evaluate any 🤗 Transformers model with a wide range of training options and
-with built-in features like logging, gradient accumulation, and mixed
-precision.
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    from transformers import BertForSequenceClassification, Trainer, TrainingArguments
-
-    model = BertForSequenceClassification.from_pretrained("bert-large-uncased")
-
-    training_args = TrainingArguments(
-        output_dir='./results',          # output directory
-        num_train_epochs=3,              # total # of training epochs
-        per_device_train_batch_size=16,  # batch size per device during training
-        per_device_eval_batch_size=64,   # batch size for evaluation
-        warmup_steps=500,                # number of warmup steps for learning rate scheduler
-        weight_decay=0.01,               # strength of weight decay
-        logging_dir='./logs',            # directory for storing logs
-    )
-
-    trainer = Trainer(
-        model=model,                         # the instantiated 🤗 Transformers model to be trained
-        args=training_args,                  # training arguments, defined above
-        train_dataset=train_dataset,         # training dataset
-        eval_dataset=test_dataset            # evaluation dataset
-    )
-    ## TENSORFLOW CODE
-    from transformers import TFBertForSequenceClassification, TFTrainer, TFTrainingArguments
-
-    model = TFBertForSequenceClassification.from_pretrained("bert-large-uncased")
-
-    training_args = TFTrainingArguments(
-        output_dir='./results',          # output directory
-        num_train_epochs=3,              # total # of training epochs
-        per_device_train_batch_size=16,  # batch size per device during training
-        per_device_eval_batch_size=64,   # batch size for evaluation
-        warmup_steps=500,                # number of warmup steps for learning rate scheduler
-        weight_decay=0.01,               # strength of weight decay
-        logging_dir='./logs',            # directory for storing logs
-    )
-
-    trainer = TFTrainer(
-        model=model,                         # the instantiated 🤗 Transformers model to be trained
-        args=training_args,                  # training arguments, defined above
-        train_dataset=tfds_train_dataset,    # tensorflow_datasets training dataset
-        eval_dataset=tfds_test_dataset       # tensorflow_datasets evaluation dataset
-    )
-
-Now simply call ``trainer.train()`` to train and ``trainer.evaluate()`` to
-evaluate. You can use your own module as well, but the first
-argument returned from ``forward`` must be the loss which you wish to
-optimize.
-
-:func:`~transformers.Trainer` uses a built-in default function to collate
-batches and prepare them to be fed into the model. If needed, you can also
-use the ``data_collator`` argument to pass your own collator function which
-takes in the data in the format provided by your dataset and returns a
-batch ready to be fed into the model. Note that
-:func:`~transformers.TFTrainer` expects the passed datasets to be dataset
-objects from ``tensorflow_datasets``.
-
-To calculate additional metrics in addition to the loss, you can also define
-your own ``compute_metrics`` function and pass it to the trainer.
-
-.. code-block:: python
-
-    from sklearn.metrics import precision_recall_fscore_support
-
-    def compute_metrics(pred):
-        labels = pred.label_ids
-        preds = pred.predictions.argmax(-1)
-        precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='binary')
-        acc = accuracy_score(labels, preds)
-        return {
-            'accuracy': acc,
-            'f1': f1,
-            'precision': precision,
-            'recall': recall
-        }
-
-Finally, you can view the results, including any calculated metrics, by
-launching tensorboard in your specified ``logging_dir`` directory.
-
-
-.. _additional-resources:
-
-Additional resources
-^^^^^^^^^^^^^^^^^^^^
-
-    * `A lightweight colab demo
-      <https://colab.research.google.com/drive/1-JIJlao4dI-Ilww_NnTc0rxtp-ymgDgM?usp=sharing>`_
-      which uses ``Trainer`` for IMDb sentiment classification.
-
-    * `🤗 Transformers Examples <https://github.com/huggingface/transformers/tree/master/examples>`_
-      including scripts for training and fine-tuning on GLUE, SQuAD, and
-      several other tasks.
-
-    * `How to train a language model
-      <https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb>`_,
-      a detailed colab notebook which uses ``Trainer`` to train a masked
-      language model from scratch on Esperanto.
-
-    * `🤗 Transformers Notebooks <./notebooks.html>`_ which contain dozens
-      of example notebooks from the community for training and using
-      🤗 Transformers on a variety of tasks.
@@ -1,7 +1,7 @@
-# Examples
+## 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.
-Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.1+.
+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:
 - **grouped by task** (all official examples work for multiple models)
@@ -13,7 +13,7 @@ Here is the list of all our examples:
 This is still a work-in-progress – in particular documentation is still sparse – so please **contribute improvements/pull requests.**


-## The Big Table of Tasks
+# The Big Table of Tasks

 | Task | Example datasets | Trainer support | TFTrainer support | pytorch-lightning | Colab
 |---|---|:---:|:---:|:---:|:---:|
@@ -21,11 +21,11 @@ This is still a work-in-progress – in particular documentation is still sparse
 | [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/text-classification)   | GLUE, XNLI      | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01_text_classification.ipynb)
 | [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER       | ✅ | ✅ | ✅ | -
 | [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice)           | SWAG, RACE, ARC | ✅ | ✅ | -  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
-| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering)     | SQuAD           | ✅ | ✅ | -  | -
+| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering)     | SQuAD           | -  | ✅ | -  | -
 | [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation)           | -               | n/a | n/a | n/a | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
 | [**`distillation`**](https://github.com/huggingface/transformers/tree/master/examples/distillation)       | All               | -  | -  | -  | -
-| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)     | CNN/Daily Mail    | -  | -  | ✅  | -
-| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)         | WMT               | -  | -  | ✅  | -
+| [**`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              | ✅ | -  | -  | -

@@ -78,32 +78,3 @@ python examples/xla_spawn.py --num_cores 8 \
 ```

 Feedback and more use cases and benchmarks involving TPUs are welcome, please share with the community.
-
-## Logging & Experiment tracking
-
-You can easily log and monitor your runs code. [TensorBoard](https://www.tensorflow.org/tensorboard) and [Weights & Biases](https://docs.wandb.com/library/integrations/huggingface) are currently supported.
-
-To use Weights & Biases, install the wandb package with:
-
-```bash
-pip install wandb
-```
-
-Then log in the command line:
-
-```bash
-wandb login
-```
-
-If you are in Jupyter or Colab, you should login with:
-
-```python
-import wandb
-wandb.login()
-```
-
-Whenever you use `Trainer` or `TFTrainer` classes, your losses, evaluation metrics, model topology and gradients (for `Trainer` only) will automatically be logged.
-
-For advanced configuration and examples, refer to the [W&B documentation](https://docs.wandb.com/library/integrations/huggingface).
-
-When using 🤗 Transformers with PyTorch Lightning, runs can be tracked through `WandbLogger`. Refer to related [documentation & examples](https://docs.wandb.com/library/frameworks/pytorch/lightning).
@@ -33,7 +33,7 @@ from transformers import (
    default_data_collator,
    set_seed,
 )
-from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels
+from utils_hans import HansDataset, InputFeatures, hans_processors


 logger = logging.getLogger(__name__)
@@ -130,7 +130,9 @@ def main():
    set_seed(training_args.seed)

    try:
-        num_labels = hans_tasks_num_labels[data_args.task_name]
+        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))

@@ -212,7 +214,6 @@ def main():

        pair_ids = [ex.pairID for ex in eval_dataset]
        output_eval_file = os.path.join(training_args.output_dir, "hans_predictions.txt")
-        label_list = eval_dataset.get_labels()
        if trainer.is_world_master():
            with open(output_eval_file, "w") as writer:
                writer.write("pairID,gold_label\n")
@@ -22,17 +22,7 @@ from typing import List, Optional, Union
 import tqdm
 from filelock import FileLock

-from transformers import (
-    BartTokenizer,
-    BartTokenizerFast,
-    DataProcessor,
-    PreTrainedTokenizer,
-    RobertaTokenizer,
-    RobertaTokenizerFast,
-    XLMRobertaTokenizer,
-    is_tf_available,
-    is_torch_available,
-)
+from transformers import DataProcessor, PreTrainedTokenizer, is_tf_available, is_torch_available


 logger = logging.getLogger(__name__)
@@ -115,17 +105,6 @@ if is_torch_available():
                    "dev" if evaluate else "train", tokenizer.__class__.__name__, str(max_seq_length), task,
                ),
            )
-            label_list = processor.get_labels()
-            if tokenizer.__class__ in (
-                RobertaTokenizer,
-                RobertaTokenizerFast,
-                XLMRobertaTokenizer,
-                BartTokenizer,
-                BartTokenizerFast,
-            ):
-                # HACK(label indices are swapped in RoBERTa pretrained model)
-                label_list[1], label_list[2] = label_list[2], label_list[1]
-            self.label_list = label_list

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
@@ -137,6 +116,7 @@ if is_torch_available():
                    self.features = torch.load(cached_features_file)
                else:
                    logger.info(f"Creating features from dataset file at {data_dir}")
+                    label_list = processor.get_labels()

                    examples = (
                        processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
@@ -153,9 +133,6 @@ if is_torch_available():
        def __getitem__(self, i) -> InputFeatures:
            return self.features[i]

-        def get_labels(self):
-            return self.label_list
-

 if is_tf_available():
    import tensorflow as tf
@@ -179,16 +156,6 @@ if is_tf_available():
        ):
            processor = hans_processors[task]()
            label_list = processor.get_labels()
-            if tokenizer.__class__ in (
-                RobertaTokenizer,
-                RobertaTokenizerFast,
-                XLMRobertaTokenizer,
-                BartTokenizer,
-                BartTokenizerFast,
-            ):
-                # HACK(label indices are swapped in RoBERTa pretrained model)
-                label_list[1], label_list[2] = label_list[2], label_list[1]
-            self.label_list = label_list

            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)
@@ -239,9 +206,6 @@ if is_tf_available():
        def __getitem__(self, i) -> InputFeatures:
            return self.features[i]

-        def get_labels(self):
-            return self.label_list
-

 class HansProcessor(DataProcessor):
    """Processor for the HANS data set."""
@@ -298,13 +262,12 @@ def hans_convert_examples_to_features(
        if ex_index % 10000 == 0:
            logger.info("Writing example %d" % (ex_index))

-        inputs = tokenizer(
+        inputs = tokenizer.encode_plus(
            example.text_a,
            example.text_b,
            add_special_tokens=True,
            max_length=max_length,
-            padding="max_length",
-            truncation=True,
+            pad_to_max_length=True,
            return_overflowing_tokens=True,
        )

@@ -1,10 +0,0 @@
-# 🤗 Benchmark results
-
-Here, you can find a list of the different benchmark results created by the community.
-
-If you would like to list benchmark results on your favorite models of the [model hub](https://huggingface.co/models) here, please open a Pull Request and add it below.
-
-| Benchmark description | Results | Environment info |      Author      |
-|:----------|:-------------|:-------------|------:|
-| PyTorch Benchmark on inference for `bert-base-cased` |[memory](https://github.com/patrickvonplaten/files_to_link_to/blob/master/bert_benchmark/inference_memory.csv) | [env](https://github.com/patrickvonplaten/files_to_link_to/blob/master/bert_benchmark/env.csv) | [Partick von Platen](https://github.com/patrickvonplaten) | 
-| PyTorch Benchmark on inference for `bert-base-cased` |[time](https://github.com/patrickvonplaten/files_to_link_to/blob/master/bert_benchmark/inference_time.csv) | [env](https://github.com/patrickvonplaten/files_to_link_to/blob/master/bert_benchmark/env.csv) | [Partick von Platen](https://github.com/patrickvonplaten) | 
@@ -1,19 +1,14 @@
 import csv
 from collections import defaultdict
 from dataclasses import dataclass, field
-from typing import List, Optional
+from typing import Optional

 import matplotlib.pyplot as plt
 import numpy as np
-from matplotlib.ticker import ScalarFormatter

 from transformers import HfArgumentParser


-def list_field(default=None, metadata=None):
-    return field(default_factory=lambda: default, metadata=metadata)
-
-
@dataclass
 class PlotArguments:
    """
@@ -29,9 +24,6 @@ class PlotArguments:
        default=False,
        metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."},
    )
-    no_log_scale: bool = field(
-        default=False, metadata={"help": "Disable logarithmic scale when plotting"},
-    )
    is_train: bool = field(
        default=False,
        metadata={
@@ -41,25 +33,6 @@ class PlotArguments:
    figure_png_file: Optional[str] = field(
        default=None, metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."},
    )
-    short_model_names: Optional[List[str]] = list_field(
-        default=None, metadata={"help": "List of model names that are used instead of the ones in the csv file."}
-    )
-
-
-def can_convert_to_int(string):
-    try:
-        int(string)
-        return True
-    except ValueError:
-        return False
-
-
-def can_convert_to_float(string):
-    try:
-        float(string)
-        return True
-    except ValueError:
-        return False


 class Plot:
@@ -73,31 +46,16 @@ class Plot:
                model_name = row["model"]
                self.result_dict[model_name]["bsz"].append(int(row["batch_size"]))
                self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]))
-                if can_convert_to_int(row["result"]):
-                    # value is not None
-                    self.result_dict[model_name]["result"][
-                        (int(row["batch_size"]), int(row["sequence_length"]))
-                    ] = int(row["result"])
-                elif can_convert_to_float(row["result"]):
-                    # value is not None
-                    self.result_dict[model_name]["result"][
-                        (int(row["batch_size"]), int(row["sequence_length"]))
-                    ] = float(row["result"])
+                self.result_dict[model_name]["result"][(int(row["batch_size"]), int(row["sequence_length"]))] = row[
+                    "result"
+                ]

    def plot(self):
        fig, ax = plt.subplots()
        title_str = "Time usage" if self.args.is_time else "Memory usage"
        title_str = title_str + " for training" if self.args.is_train else title_str + " for inference"

-        if not self.args.no_log_scale:
-            # set logarithm scales
-            ax.set_xscale("log")
-            ax.set_yscale("log")
-
-        for axis in [ax.xaxis, ax.yaxis]:
-            axis.set_major_formatter(ScalarFormatter())
-
-        for model_name_idx, model_name in enumerate(self.result_dict.keys()):
+        for model_name in self.result_dict.keys():
            batch_sizes = sorted(list(set(self.result_dict[model_name]["bsz"])))
            sequence_lengths = sorted(list(set(self.result_dict[model_name]["seq_len"])))
            results = self.result_dict[model_name]["result"]
@@ -106,33 +64,28 @@ class Plot:
                (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
            )

-            label_model_name = (
-                model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
-            )
+            plt.xlim(min(x_axis_array), max(x_axis_array))

            for inner_loop_value in inner_loop_array:
                if self.args.plot_along_batch:
-                    y_axis_array = np.asarray(
-                        [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results],
-                        dtype=np.int,
-                    )
+                    y_axis_array = np.asarray([results[(x, inner_loop_value)] for x in x_axis_array], dtype=np.int)
                else:
-                    y_axis_array = np.asarray(
-                        [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results],
-                        dtype=np.float32,
-                    )
+                    y_axis_array = np.asarray([results[(inner_loop_value, x)] for x in x_axis_array], dtype=np.float32)
+
+                ax.set_xscale("log", basex=2)
+                ax.set_yscale("log", basey=10)

                (x_axis_label, inner_loop_label) = (
-                    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
+                    ("batch_size", "sequence_length in #tokens")
+                    if self.args.plot_along_batch
+                    else ("sequence_length in #tokens", "batch_size")
                )

-                x_axis_array = np.asarray(x_axis_array, np.int)[: len(y_axis_array)]
-                plt.scatter(
-                    x_axis_array, y_axis_array, label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"
-                )
+                x_axis_array = np.asarray(x_axis_array, np.int)
+                plt.scatter(x_axis_array, y_axis_array, label=f"{model_name} - {inner_loop_label}: {inner_loop_value}")
                plt.plot(x_axis_array, y_axis_array, "--")

-            title_str += f" {label_model_name} vs."
+            title_str += f" {model_name} vs."

        title_str = title_str[:-4]
        y_axis_label = "Time in s" if self.args.is_time else "Memory in MB"
@@ -13,15 +13,15 @@
 # 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"""
+""" Benchmarking the library on inference and training in Tensorflow"""

-from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
+from transformers import HfArgumentParser, TensorflowBenchmark, TensorflowBenchmarkArguments


 def main():
-    parser = HfArgumentParser(TensorFlowBenchmarkArguments)
+    parser = HfArgumentParser(TensorflowBenchmarkArguments)
    benchmark_args = parser.parse_args_into_dataclasses()[0]
-    benchmark = TensorFlowBenchmark(args=benchmark_args)
+    benchmark = TensorflowBenchmark(args=benchmark_args)
    benchmark.run()


@@ -226,6 +226,8 @@ def train(args, train_dataset, model, tokenizer):
                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                    # Save model checkpoint
                    output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
+                    if not os.path.exists(output_dir):
+                        os.makedirs(output_dir)
                    model_to_save = (
                        model.module if hasattr(model, "module") else model
                    )  # Take care of distributed/parallel training
@@ -252,15 +254,12 @@ def train(args, train_dataset, model, tokenizer):
    return global_step, tr_loss / global_step


-def evaluate(args, model, tokenizer, prefix="", patience=0):
+def evaluate(args, model, tokenizer, prefix=""):

+    # PABEE STATS
    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()
@@ -332,7 +331,7 @@ def evaluate(args, model, tokenizer, prefix="", patience=0):
                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.eval_all_checkpoints:
        if args.model_type == "albert":
            model.albert.log_stats()
        elif args.model_type == "bert":
@@ -647,6 +646,10 @@ def main():

    # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
+        # Create output directory if needed
+        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
+            os.makedirs(args.output_dir)
+
        logger.info("Saving model checkpoint to %s", args.output_dir)
        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
@@ -687,7 +690,15 @@ def main():

            print(f"Evaluation for checkpoint {prefix}")
            for patience in patience_list:
-                result = evaluate(args, model, tokenizer, prefix=prefix, patience=patience)
+                if args.model_type == "albert":
+                    model.albert.set_regression_threshold(args.regression_threshold)
+                    model.albert.set_patience(patience)
+                elif args.model_type == "bert":
+                    model.bert.set_regression_threshold(args.regression_threshold)
+                    model.bert.set_patience(patience)
+                else:
+                    raise NotImplementedError()
+                result = evaluate(args, model, tokenizer, prefix=prefix)
                result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
                results.update(result)
    return results
@@ -521,6 +521,10 @@ def main():

    # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
+        # Create output directory if needed
+        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
+            os.makedirs(args.output_dir)
+
        logger.info("Saving model checkpoint to %s", args.output_dir)
        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
@@ -383,6 +383,8 @@ def train(args, train_dataset, model, tokenizer):
                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                    # Save model checkpoint
                    output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
+                    if not os.path.exists(output_dir):
+                        os.makedirs(output_dir)
                    model_to_save = (
                        model.module if hasattr(model, "module") else model
                    )  # Take care of distributed/parallel training
@@ -649,6 +651,10 @@ def main():

    # Save the trained model and the tokenizer
    if args.local_rank == -1 or torch.distributed.get_rank() == 0:
+        # Create output directory if needed
+        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
+            os.makedirs(args.output_dir)
+
        logger.info("Saving model checkpoint to %s", args.output_dir)
        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
@@ -1,54 +0,0 @@
-# DeeBERT: Early Exiting for *BERT
-
-This is the code base for the paper [DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference](https://www.aclweb.org/anthology/2020.acl-main.204/), modified from its [original code base](https://github.com/castorini/deebert).
-
-The original code base also has information for downloading sample models that we have trained in advance.
-
-## Usage
-
-There are three scripts in the folder which can be run directly.
-
-In each script, there are several things to modify before running:
-
-* `PATH_TO_DATA`: path to the GLUE dataset.
-* `--output_dir`: path for saving fine-tuned models. Default: `./saved_models`.
-* `--plot_data_dir`: path for saving evaluation results. Default: `./results`. Results are printed to stdout and also saved to `npy` files in this directory to facilitate plotting figures and further analyses.
-* `MODEL_TYPE`: bert or roberta
-* `MODEL_SIZE`: base or large
-* `DATASET`: SST-2, MRPC, RTE, QNLI, QQP, or MNLI
-
-#### train_deebert.sh
-
-This is for fine-tuning DeeBERT models.
-
-#### eval_deebert.sh
-
-This is for evaluating each exit layer for fine-tuned DeeBERT models.
-
-#### entropy_eval.sh
-
-This is for evaluating fine-tuned DeeBERT models, given a number of different early exit entropy thresholds.
-
-
-
-## Citation
-
-Please cite our paper if you find the resource useful:
-```
-@inproceedings{xin-etal-2020-deebert,
-    title = "{D}ee{BERT}: Dynamic Early Exiting for Accelerating {BERT} Inference",
-    author = "Xin, Ji  and
-      Tang, Raphael  and
-      Lee, Jaejun  and
-      Yu, Yaoliang  and
-      Lin, Jimmy",
-    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
-    month = jul,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.acl-main.204",
-    pages = "2246--2251",
-}
-```
-
@@ -1,33 +0,0 @@
-#!/bin/bash
-export CUDA_VISIBLE_DEVICES=0
-
-PATH_TO_DATA=/h/xinji/projects/GLUE
-
-MODEL_TYPE=bert  # bert or roberta
-MODEL_SIZE=base  # base or large
-DATASET=MRPC  # SST-2, MRPC, RTE, QNLI, QQP, or MNLI
-
-MODEL_NAME=${MODEL_TYPE}-${MODEL_SIZE}
-if [ $MODEL_TYPE = 'bert' ]
-then
-  MODEL_NAME=${MODEL_NAME}-uncased
-fi
-
-ENTROPIES="0 0.1 0.2 0.3 0.4 0.5 0.6 0.7"
-
-for ENTROPY in $ENTROPIES; do
-  python -u run_glue_deebert.py \
-    --model_type $MODEL_TYPE \
-    --model_name_or_path ./saved_models/${MODEL_TYPE}-${MODEL_SIZE}/$DATASET/two_stage \
-    --task_name $DATASET \
-    --do_eval \
-    --do_lower_case \
-    --data_dir $PATH_TO_DATA/$DATASET \
-    --output_dir ./saved_models/${MODEL_TYPE}-${MODEL_SIZE}/$DATASET/two_stage \
-    --plot_data_dir ./results/ \
-    --max_seq_length 128 \
-    --early_exit_entropy $ENTROPY \
-    --eval_highway \
-    --overwrite_cache \
-    --per_gpu_eval_batch_size=1
-done
@@ -1,30 +0,0 @@
-#!/bin/bash
-export CUDA_VISIBLE_DEVICES=0
-
-PATH_TO_DATA=/h/xinji/projects/GLUE
-
-MODEL_TYPE=bert  # bert or roberta
-MODEL_SIZE=base  # base or large
-DATASET=MRPC  # SST-2, MRPC, RTE, QNLI, QQP, or MNLI
-
-MODEL_NAME=${MODEL_TYPE}-${MODEL_SIZE}
-if [ $MODEL_TYPE = 'bert' ]
-then
-  MODEL_NAME=${MODEL_NAME}-uncased
-fi
-
-
-python -u run_glue_deebert.py  \
-  --model_type $MODEL_TYPE \
-  --model_name_or_path ./saved_models/${MODEL_TYPE}-${MODEL_SIZE}/$DATASET/two_stage \
-  --task_name $DATASET \
-  --do_eval \
-  --do_lower_case \
-  --data_dir $PATH_TO_DATA/$DATASET \
-  --output_dir ./saved_models/${MODEL_TYPE}-${MODEL_SIZE}/$DATASET/two_stage \
-  --plot_data_dir ./results/ \
-  --max_seq_length 128 \
-  --eval_each_highway \
-  --eval_highway \
-  --overwrite_cache \
-  --per_gpu_eval_batch_size=1
@@ -1,720 +0,0 @@
-from __future__ import absolute_import, division, print_function
-
-import argparse
-import glob
-import logging
-import os
-import random
-import time
-
-import numpy as np
-import torch
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
-from torch.utils.data.distributed import DistributedSampler
-from tqdm import tqdm, trange
-
-from src.modeling_highway_bert import DeeBertForSequenceClassification
-from src.modeling_highway_roberta import DeeRobertaForSequenceClassification
-from transformers import (
-    WEIGHTS_NAME,
-    AdamW,
-    BertConfig,
-    BertTokenizer,
-    RobertaConfig,
-    RobertaTokenizer,
-    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
-
-
-try:
-    from torch.utils.tensorboard import SummaryWriter
-except ImportError:
-    from tensorboardX import SummaryWriter
-
-
-logger = logging.getLogger(__name__)
-
-
-MODEL_CLASSES = {
-    "bert": (BertConfig, DeeBertForSequenceClassification, BertTokenizer),
-    "roberta": (RobertaConfig, DeeRobertaForSequenceClassification, RobertaTokenizer),
-}
-
-
-def set_seed(args):
-    random.seed(args.seed)
-    np.random.seed(args.seed)
-    torch.manual_seed(args.seed)
-    if args.n_gpu > 0:
-        torch.cuda.manual_seed_all(args.seed)
-
-
-def get_wanted_result(result):
-    if "spearmanr" in result:
-        print_result = result["spearmanr"]
-    elif "f1" in result:
-        print_result = result["f1"]
-    elif "mcc" in result:
-        print_result = result["mcc"]
-    elif "acc" in result:
-        print_result = result["acc"]
-    else:
-        raise ValueError("Primary metric unclear in the results")
-    return print_result
-
-
-def train(args, train_dataset, model, tokenizer, train_highway=False):
-    """ Train the model """
-    if args.local_rank in [-1, 0]:
-        tb_writer = SummaryWriter()
-
-    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)
-
-    if args.max_steps > 0:
-        t_total = args.max_steps
-        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
-    else:
-        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
-
-    # Prepare optimizer and schedule (linear warmup and decay)
-    no_decay = ["bias", "LayerNorm.weight"]
-    if train_highway:
-        optimizer_grouped_parameters = [
-            {
-                "params": [
-                    p
-                    for n, p in model.named_parameters()
-                    if ("highway" in n) and (not any(nd in n for nd in no_decay))
-                ],
-                "weight_decay": args.weight_decay,
-            },
-            {
-                "params": [
-                    p for n, p in model.named_parameters() if ("highway" in n) and (any(nd in n for nd in no_decay))
-                ],
-                "weight_decay": 0.0,
-            },
-        ]
-    else:
-        optimizer_grouped_parameters = [
-            {
-                "params": [
-                    p
-                    for n, p in model.named_parameters()
-                    if ("highway" not in n) and (not any(nd in n for nd in no_decay))
-                ],
-                "weight_decay": args.weight_decay,
-            },
-            {
-                "params": [
-                    p
-                    for n, p in model.named_parameters()
-                    if ("highway" not in n) and (any(nd in n for nd in no_decay))
-                ],
-                "weight_decay": 0.0,
-            },
-        ]
-    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
-    scheduler = get_linear_schedule_with_warmup(
-        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
-    )
-    if args.fp16:
-        try:
-            from apex import amp
-        except ImportError:
-            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
-        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
-
-    # multi-gpu training (should be after apex fp16 initialization)
-    if args.n_gpu > 1:
-        model = torch.nn.DataParallel(model)
-
-    # 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
-        )
-
-    # Train!
-    logger.info("***** Running training *****")
-    logger.info("  Num examples = %d", len(train_dataset))
-    logger.info("  Num Epochs = %d", args.num_train_epochs)
-    logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
-    logger.info(
-        "  Total train batch size (w. parallel, distributed & accumulation) = %d",
-        args.train_batch_size
-        * args.gradient_accumulation_steps
-        * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
-    )
-    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
-    logger.info("  Total optimization steps = %d", t_total)
-
-    global_step = 0
-    tr_loss, logging_loss = 0.0, 0.0
-    model.zero_grad()
-    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):
-            model.train()
-            batch = tuple(t.to(args.device) for t in batch)
-            inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
-            if args.model_type != "distilbert":
-                inputs["token_type_ids"] = (
-                    batch[2] if args.model_type in ["bert", "xlnet"] else None
-                )  # XLM, DistilBERT and RoBERTa don't use segment_ids
-            inputs["train_highway"] = train_highway
-            outputs = model(**inputs)
-            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
-
-            if args.n_gpu > 1:
-                loss = loss.mean()  # mean() to average on multi-gpu parallel training
-            if args.gradient_accumulation_steps > 1:
-                loss = loss / args.gradient_accumulation_steps
-
-            if args.fp16:
-                with amp.scale_loss(loss, optimizer) as scaled_loss:
-                    scaled_loss.backward()
-            else:
-                loss.backward()
-
-            tr_loss += loss.item()
-            if (step + 1) % args.gradient_accumulation_steps == 0:
-                if args.fp16:
-                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
-                else:
-                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
-
-                optimizer.step()
-                scheduler.step()  # Update learning rate schedule
-                model.zero_grad()
-                global_step += 1
-
-                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
-                    # Log metrics
-                    if (
-                        args.local_rank == -1 and args.evaluate_during_training
-                    ):  # Only evaluate when single GPU otherwise metrics may not average well
-                        results = evaluate(args, model, tokenizer)
-                        for key, value in results.items():
-                            tb_writer.add_scalar("eval_{}".format(key), value, global_step)
-                    tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
-                    tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
-                    logging_loss = tr_loss
-
-                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
-                    # Save model checkpoint
-                    output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
-                    if not os.path.exists(output_dir):
-                        os.makedirs(output_dir)
-                    model_to_save = (
-                        model.module if hasattr(model, "module") else model
-                    )  # Take care of distributed/parallel training
-                    model_to_save.save_pretrained(output_dir)
-                    torch.save(args, os.path.join(output_dir, "training_args.bin"))
-                    logger.info("Saving model checkpoint to %s", output_dir)
-
-            if args.max_steps > 0 and global_step > args.max_steps:
-                epoch_iterator.close()
-                break
-        if args.max_steps > 0 and global_step > args.max_steps:
-            train_iterator.close()
-            break
-
-    if args.local_rank in [-1, 0]:
-        tb_writer.close()
-
-    return global_step, tr_loss / global_step
-
-
-def evaluate(args, model, tokenizer, prefix="", output_layer=-1, eval_highway=False):
-    # 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)
-
-        if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(eval_output_dir)
-
-        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) if args.local_rank == -1 else DistributedSampler(eval_dataset)
-        eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
-
-        # multi-gpu eval
-        if args.n_gpu > 1:
-            model = torch.nn.DataParallel(model)
-
-        # Eval!
-        logger.info("***** Running evaluation {} *****".format(prefix))
-        logger.info("  Num examples = %d", len(eval_dataset))
-        logger.info("  Batch size = %d", args.eval_batch_size)
-        eval_loss = 0.0
-        nb_eval_steps = 0
-        preds = None
-        out_label_ids = None
-        exit_layer_counter = {(i + 1): 0 for i in range(model.num_layers)}
-        st = time.time()
-        for batch in tqdm(eval_dataloader, desc="Evaluating"):
-            model.eval()
-            batch = tuple(t.to(args.device) for t in batch)
-
-            with torch.no_grad():
-                inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
-                if args.model_type != "distilbert":
-                    inputs["token_type_ids"] = (
-                        batch[2] if args.model_type in ["bert", "xlnet"] else None
-                    )  # XLM, DistilBERT and RoBERTa don't use segment_ids
-                if output_layer >= 0:
-                    inputs["output_layer"] = output_layer
-                outputs = model(**inputs)
-                if eval_highway:
-                    exit_layer_counter[outputs[-1]] += 1
-                tmp_eval_loss, logits = outputs[:2]
-
-                eval_loss += tmp_eval_loss.mean().item()
-            nb_eval_steps += 1
-            if preds is None:
-                preds = logits.detach().cpu().numpy()
-                out_label_ids = inputs["labels"].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)
-        eval_time = time.time() - st
-        logger.info("Eval time: {}".format(eval_time))
-
-        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)
-
-        if eval_highway:
-            logger.info("Exit layer counter: {}".format(exit_layer_counter))
-            actual_cost = sum([l * c for l, c in exit_layer_counter.items()])
-            full_cost = len(eval_dataloader) * model.num_layers
-            logger.info("Expected saving: {}".format(actual_cost / full_cost))
-            if args.early_exit_entropy >= 0:
-                save_fname = (
-                    args.plot_data_dir
-                    + "/"
-                    + args.model_name_or_path[2:]
-                    + "/entropy_{}.npy".format(args.early_exit_entropy)
-                )
-                if not os.path.exists(os.path.dirname(save_fname)):
-                    os.makedirs(os.path.dirname(save_fname))
-                print_result = get_wanted_result(result)
-                np.save(save_fname, np.array([exit_layer_counter, eval_time, actual_cost / full_cost, print_result]))
-                logger.info("Entropy={}\tResult={:.2f}".format(args.early_exit_entropy, 100 * print_result))
-
-        output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.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]))
-                writer.write("%s = %s\n" % (key, str(result[key])))
-
-    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"]:
-            # 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)
-
-    if features[0].token_type_ids is None:
-        # For RoBERTa (a potential bug!)
-        all_token_type_ids = torch.tensor([[0] * args.max_seq_length for f in features], dtype=torch.long)
-    else:
-        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()
-
-    # Required parameters
-    parser.add_argument(
-        "--data_dir",
-        default=None,
-        type=str,
-        required=True,
-        help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
-    )
-    parser.add_argument(
-        "--model_type",
-        default=None,
-        type=str,
-        required=True,
-        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
-    )
-    parser.add_argument(
-        "--model_name_or_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to pre-trained model or shortcut name.",
-    )
-    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()),
-    )
-    parser.add_argument(
-        "--output_dir",
-        default=None,
-        type=str,
-        required=True,
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument(
-        "--plot_data_dir",
-        default="./plotting/",
-        type=str,
-        required=False,
-        help="The directory to store data for plotting figures.",
-    )
-
-    # Other parameters
-    parser.add_argument(
-        "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        default="",
-        type=str,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--cache_dir",
-        default="",
-        type=str,
-        help="Where do you want to store the pre-trained models downloaded from s3",
-    )
-    parser.add_argument(
-        "--max_seq_length",
-        default=128,
-        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("--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="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."
-    )
-    parser.add_argument("--eval_each_highway", action="store_true", help="Set this flag to evaluate each highway.")
-    parser.add_argument(
-        "--eval_after_first_stage",
-        action="store_true",
-        help="Set this flag to evaluate after training only bert (not highway).",
-    )
-    parser.add_argument("--eval_highway", action="store_true", help="Set this flag if it's evaluating highway models")
-
-    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=8, type=int, help="Batch size per GPU/CPU for evaluation."
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        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("--weight_decay", default=0.0, type=float, help="Weight deay 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."
-    )
-    parser.add_argument(
-        "--max_steps",
-        default=-1,
-        type=int,
-        help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
-    )
-    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
-    parser.add_argument("--early_exit_entropy", default=-1, type=float, help="Entropy threshold for early exit.")
-
-    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",
-        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
-    )
-    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"
-    )
-    parser.add_argument(
-        "--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")
-
-    parser.add_argument(
-        "--fp16",
-        action="store_true",
-        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
-    )
-    parser.add_argument(
-        "--fp16_opt_level",
-        type=str,
-        default="O1",
-        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("--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()
-
-    if (
-        os.path.exists(args.output_dir)
-        and os.listdir(args.output_dir)
-        and args.do_train
-        and not args.overwrite_output_dir
-    ):
-        raise ValueError(
-            "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
-                args.output_dir
-            )
-        )
-
-    # Setup distant debugging if needed
-    if args.server_ip and args.server_port:
-        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
-        import ptvsd
-
-        print("Waiting for debugger attach")
-        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
-        ptvsd.wait_for_attach()
-
-    # 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()
-    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)
-        torch.distributed.init_process_group(backend="nccl")
-        args.n_gpu = 1
-    args.device = device
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO if 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",
-        args.local_rank,
-        device,
-        args.n_gpu,
-        bool(args.local_rank != -1),
-        args.fp16,
-    )
-
-    # Set seed
-    set_seed(args)
-
-    # Prepare GLUE task
-    args.task_name = args.task_name.lower()
-    if args.task_name not in processors:
-        raise ValueError("Task not found: %s" % (args.task_name))
-    processor = processors[args.task_name]()
-    args.output_mode = output_modes[args.task_name]
-    label_list = processor.get_labels()
-    num_labels = len(label_list)
-
-    # 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
-
-    args.model_type = args.model_type.lower()
-    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    config = config_class.from_pretrained(
-        args.config_name if args.config_name else args.model_name_or_path,
-        num_labels=num_labels,
-        finetuning_task=args.task_name,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-    )
-    tokenizer = tokenizer_class.from_pretrained(
-        args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
-        do_lower_case=args.do_lower_case,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-    )
-    model = model_class.from_pretrained(
-        args.model_name_or_path,
-        from_tf=bool(".ckpt" in args.model_name_or_path),
-        config=config,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-    )
-
-    if args.model_type == "bert":
-        model.bert.encoder.set_early_exit_entropy(args.early_exit_entropy)
-        model.bert.init_highway_pooler()
-    elif args.model_type == "roberta":
-        model.roberta.encoder.set_early_exit_entropy(args.early_exit_entropy)
-        model.roberta.init_highway_pooler()
-    else:
-        raise NotImplementedError()
-
-    if args.local_rank == 0:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
-
-    model.to(args.device)
-
-    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)
-        global_step, tr_loss = train(args, train_dataset, model, tokenizer)
-        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
-
-        if args.eval_after_first_stage:
-            result = evaluate(args, model, tokenizer, prefix="")
-            print_result = get_wanted_result(result)
-
-        train(args, train_dataset, model, tokenizer, train_highway=True)
-
-    # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
-    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        # Create output directory if needed
-        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir)
-
-        logger.info("Saving model checkpoint to %s", args.output_dir)
-        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
-        # They can then be reloaded using `from_pretrained()`
-        model_to_save = (
-            model.module if hasattr(model, "module") else model
-        )  # Take care of distributed/parallel training
-        model_to_save.save_pretrained(args.output_dir)
-        tokenizer.save_pretrained(args.output_dir)
-
-        # Good practice: save your training arguments together with the trained model
-        torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
-
-        # Load a trained model and vocabulary that you have fine-tuned
-        model = model_class.from_pretrained(args.output_dir)
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir)
-        model.to(args.device)
-
-    # Evaluation
-    results = {}
-    if args.do_eval and args.local_rank in [-1, 0]:
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
-        checkpoints = [args.output_dir]
-        if args.eval_all_checkpoints:
-            checkpoints = list(
-                os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
-            )
-            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)
-            if args.model_type == "bert":
-                model.bert.encoder.set_early_exit_entropy(args.early_exit_entropy)
-            elif args.model_type == "roberta":
-                model.roberta.encoder.set_early_exit_entropy(args.early_exit_entropy)
-            else:
-                raise NotImplementedError()
-
-            model.to(args.device)
-            result = evaluate(args, model, tokenizer, prefix=prefix, eval_highway=args.eval_highway)
-            print_result = get_wanted_result(result)
-            logger.info("Result: {}".format(print_result))
-            if args.eval_each_highway:
-                last_layer_results = print_result
-                each_layer_results = []
-                for i in range(model.num_layers):
-                    logger.info("\n")
-                    _result = evaluate(
-                        args, model, tokenizer, prefix=prefix, output_layer=i, eval_highway=args.eval_highway
-                    )
-                    if i + 1 < model.num_layers:
-                        each_layer_results.append(get_wanted_result(_result))
-                each_layer_results.append(last_layer_results)
-                save_fname = args.plot_data_dir + "/" + args.model_name_or_path[2:] + "/each_layer.npy"
-                if not os.path.exists(os.path.dirname(save_fname)):
-                    os.makedirs(os.path.dirname(save_fname))
-                np.save(save_fname, np.array(each_layer_results))
-                info_str = "Score of each layer:"
-                for i in range(model.num_layers):
-                    info_str += " {:.2f}".format(100 * each_layer_results[i])
-                logger.info(info_str)
-            result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
-            results.update(result)
-
-    return results
-
-
-if __name__ == "__main__":
-    main()
@@ -1,396 +0,0 @@
-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,
-    BertEmbeddings,
-    BertLayer,
-    BertPooler,
-    BertPreTrainedModel,
-)
-
-
-def entropy(x):
-    """ Calculate entropy of a pre-softmax logit Tensor
-    """
-    exp_x = torch.exp(x)
-    A = torch.sum(exp_x, dim=1)  # sum of exp(x_i)
-    B = torch.sum(x * exp_x, dim=1)  # sum of x_i * exp(x_i)
-    return torch.log(A) - B / A
-
-
-class DeeBertEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.output_attentions = config.output_attentions
-        self.output_hidden_states = config.output_hidden_states
-        self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])
-        self.highway = nn.ModuleList([BertHighway(config) for _ in range(config.num_hidden_layers)])
-
-        self.early_exit_entropy = [-1 for _ in range(config.num_hidden_layers)]
-
-    def set_early_exit_entropy(self, x):
-        if (type(x) is float) or (type(x) is int):
-            for i in range(len(self.early_exit_entropy)):
-                self.early_exit_entropy[i] = x
-        else:
-            self.early_exit_entropy = x
-
-    def init_highway_pooler(self, pooler):
-        loaded_model = pooler.state_dict()
-        for highway in self.highway:
-            for name, param in highway.pooler.state_dict().items():
-                param.copy_(loaded_model[name])
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-    ):
-        all_hidden_states = ()
-        all_attentions = ()
-        all_highway_exits = ()
-        for i, layer_module in enumerate(self.layer):
-            if self.output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            layer_outputs = layer_module(
-                hidden_states, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask
-            )
-            hidden_states = layer_outputs[0]
-
-            if self.output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)
-
-            current_outputs = (hidden_states,)
-            if self.output_hidden_states:
-                current_outputs = current_outputs + (all_hidden_states,)
-            if self.output_attentions:
-                current_outputs = current_outputs + (all_attentions,)
-
-            highway_exit = self.highway[i](current_outputs)
-            # logits, pooled_output
-
-            if not self.training:
-                highway_logits = highway_exit[0]
-                highway_entropy = entropy(highway_logits)
-                highway_exit = highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
-                all_highway_exits = all_highway_exits + (highway_exit,)
-
-                if highway_entropy < self.early_exit_entropy[i]:
-                    new_output = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
-                    raise HighwayException(new_output, i + 1)
-            else:
-                all_highway_exits = all_highway_exits + (highway_exit,)
-
-        # Add last layer
-        if self.output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        outputs = (hidden_states,)
-        if self.output_hidden_states:
-            outputs = outputs + (all_hidden_states,)
-        if self.output_attentions:
-            outputs = outputs + (all_attentions,)
-
-        outputs = outputs + (all_highway_exits,)
-        return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits
-
-
-@add_start_docstrings(
-    "The Bert Model transformer with early exiting (DeeBERT). ", BERT_START_DOCSTRING,
-)
-class DeeBertModel(BertPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = BertEmbeddings(config)
-        self.encoder = DeeBertEncoder(config)
-        self.pooler = BertPooler(config)
-
-        self.init_weights()
-
-    def init_highway_pooler(self):
-        self.encoder.init_highway_pooler(self.pooler)
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.embeddings.word_embeddings = value
-
-    def _prune_heads(self, heads_to_prune):
-        """ Prunes heads of the model.
-            heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
-            See base class PreTrainedModel
-        """
-        for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].attention.prune_heads(heads)
-
-    @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,
-    ):
-        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 ``output_hidden_states=True`` is passed or 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 ``output_attentions=True`` is passed or 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.
-        highway_exits (:obj:`tuple(tuple(torch.Tensor))`:
-            Tuple of each early exit's results (total length: number of layers)
-            Each tuple is again, a tuple of length 2 - the first entry is logits and the second entry is hidden states.
-        """
-        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 encoder_attention_mask is None:
-            encoder_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 encoder_attention_mask.dim() == 3:
-            encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
-        if encoder_attention_mask.dim() == 2:
-            encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
-
-        encoder_extended_attention_mask = encoder_extended_attention_mask.to(
-            dtype=next(self.parameters()).dtype
-        )  # fp16 compatibility
-        encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
-
-        # 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 = 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,
-        )
-        sequence_output = encoder_outputs[0]
-        pooled_output = self.pooler(sequence_output)
-
-        outputs = (sequence_output, pooled_output,) + encoder_outputs[
-            1:
-        ]  # add hidden_states and attentions if they are here
-        return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
-
-
-class HighwayException(Exception):
-    def __init__(self, message, exit_layer):
-        self.message = message
-        self.exit_layer = exit_layer  # start from 1!
-
-
-class BertHighway(nn.Module):
-    """A module to provide a shortcut
-    from (the output of one non-final BertLayer in BertEncoder) to (cross-entropy computation in BertForSequenceClassification)
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        self.pooler = BertPooler(config)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-    def forward(self, encoder_outputs):
-        # Pooler
-        pooler_input = encoder_outputs[0]
-        pooler_output = self.pooler(pooler_input)
-        # "return" pooler_output
-
-        # BertModel
-        bmodel_output = (pooler_input, pooler_output) + encoder_outputs[1:]
-        # "return" bodel_output
-
-        # Dropout and classification
-        pooled_output = bmodel_output[1]
-
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-
-        return logits, pooled_output
-
-
-@add_start_docstrings(
-    """Bert Model (with early exiting - DeeBERT) with a classifier on top,
-    also takes care of multi-layer training. """,
-    BERT_START_DOCSTRING,
-)
-class DeeBertForSequenceClassification(BertPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.num_layers = config.num_hidden_layers
-
-        self.bert = DeeBertModel(config)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
-
-        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,
-        output_layer=-1,
-        train_highway=False,
-    ):
-        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 ``output_hidden_states=True`` is passed or 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 ``output_attentions=True`` is passed or 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.
-        highway_exits (:obj:`tuple(tuple(torch.Tensor))`:
-            Tuple of each early exit's results (total length: number of layers)
-            Each tuple is again, a tuple of length 2 - the first entry is logits and the second entry is hidden states.
-        """
-
-        exit_layer = self.num_layers
-        try:
-            outputs = self.bert(
-                input_ids,
-                attention_mask=attention_mask,
-                token_type_ids=token_type_ids,
-                position_ids=position_ids,
-                head_mask=head_mask,
-                inputs_embeds=inputs_embeds,
-            )
-            # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
-
-            pooled_output = outputs[1]
-
-            pooled_output = self.dropout(pooled_output)
-            logits = self.classifier(pooled_output)
-            outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here
-        except HighwayException as e:
-            outputs = e.message
-            exit_layer = e.exit_layer
-            logits = outputs[0]
-
-        if not self.training:
-            original_entropy = entropy(logits)
-            highway_entropy = []
-            highway_logits_all = []
-        if labels is not None:
-            if self.num_labels == 1:
-                #  We are doing regression
-                loss_fct = MSELoss()
-                loss = loss_fct(logits.view(-1), labels.view(-1))
-            else:
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
-            # work with highway exits
-            highway_losses = []
-            for highway_exit in outputs[-1]:
-                highway_logits = highway_exit[0]
-                if not self.training:
-                    highway_logits_all.append(highway_logits)
-                    highway_entropy.append(highway_exit[2])
-                if self.num_labels == 1:
-                    #  We are doing regression
-                    loss_fct = MSELoss()
-                    highway_loss = loss_fct(highway_logits.view(-1), labels.view(-1))
-                else:
-                    loss_fct = CrossEntropyLoss()
-                    highway_loss = loss_fct(highway_logits.view(-1, self.num_labels), labels.view(-1))
-                highway_losses.append(highway_loss)
-
-            if train_highway:
-                outputs = (sum(highway_losses[:-1]),) + outputs
-                # exclude the final highway, of course
-            else:
-                outputs = (loss,) + outputs
-        if not self.training:
-            outputs = outputs + ((original_entropy, highway_entropy), exit_layer)
-            if output_layer >= 0:
-                outputs = (
-                    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
-                )  # use the highway of the last layer
-
-        return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)
@@ -1,151 +0,0 @@
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import torch.nn as nn
-from torch.nn import CrossEntropyLoss, MSELoss
-
-from transformers.configuration_roberta import RobertaConfig
-from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_callable
-from transformers.modeling_roberta import ROBERTA_INPUTS_DOCSTRING, ROBERTA_START_DOCSTRING, RobertaEmbeddings
-
-from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
-
-
-@add_start_docstrings(
-    "The RoBERTa Model transformer with early exiting (DeeRoBERTa). ", ROBERTA_START_DOCSTRING,
-)
-class DeeRobertaModel(DeeBertModel):
-
-    config_class = RobertaConfig
-    base_model_prefix = "roberta"
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.embeddings = RobertaEmbeddings(config)
-        self.init_weights()
-
-
-@add_start_docstrings(
-    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
-    also takes care of multi-layer training. """,
-    ROBERTA_START_DOCSTRING,
-)
-class DeeRobertaForSequenceClassification(BertPreTrainedModel):
-
-    config_class = RobertaConfig
-    base_model_prefix = "roberta"
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.num_layers = config.num_hidden_layers
-
-        self.roberta = DeeRobertaModel(config)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
-
-    @add_start_docstrings_to_callable(ROBERTA_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,
-        output_layer=-1,
-        train_highway=False,
-    ):
-        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.RobertaConfig`) 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 ``output_hidden_states=True`` is passed or 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 ``output_attentions=True`` is passed or 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.
-        highway_exits (:obj:`tuple(tuple(torch.Tensor))`:
-            Tuple of each early exit's results (total length: number of layers)
-            Each tuple is again, a tuple of length 2 - the first entry is logits and the second entry is hidden states.
-        """
-
-        exit_layer = self.num_layers
-        try:
-            outputs = self.roberta(
-                input_ids,
-                attention_mask=attention_mask,
-                token_type_ids=token_type_ids,
-                position_ids=position_ids,
-                head_mask=head_mask,
-                inputs_embeds=inputs_embeds,
-            )
-
-            pooled_output = outputs[1]
-
-            pooled_output = self.dropout(pooled_output)
-            logits = self.classifier(pooled_output)
-            outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here
-        except HighwayException as e:
-            outputs = e.message
-            exit_layer = e.exit_layer
-            logits = outputs[0]
-
-        if not self.training:
-            original_entropy = entropy(logits)
-            highway_entropy = []
-            highway_logits_all = []
-        if labels is not None:
-            if self.num_labels == 1:
-                #  We are doing regression
-                loss_fct = MSELoss()
-                loss = loss_fct(logits.view(-1), labels.view(-1))
-            else:
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
-            # work with highway exits
-            highway_losses = []
-            for highway_exit in outputs[-1]:
-                highway_logits = highway_exit[0]
-                if not self.training:
-                    highway_logits_all.append(highway_logits)
-                    highway_entropy.append(highway_exit[2])
-                if self.num_labels == 1:
-                    #  We are doing regression
-                    loss_fct = MSELoss()
-                    highway_loss = loss_fct(highway_logits.view(-1), labels.view(-1))
-                else:
-                    loss_fct = CrossEntropyLoss()
-                    highway_loss = loss_fct(highway_logits.view(-1, self.num_labels), labels.view(-1))
-                highway_losses.append(highway_loss)
-
-            if train_highway:
-                outputs = (sum(highway_losses[:-1]),) + outputs
-                # exclude the final highway, of course
-            else:
-                outputs = (loss,) + outputs
-        if not self.training:
-            outputs = outputs + ((original_entropy, highway_entropy), exit_layer)
-            if output_layer >= 0:
-                outputs = (
-                    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
-                )  # use the highway of the last layer
-
-        return outputs  # (loss), logits, (hidden_states), (attentions), entropy
@@ -1,97 +0,0 @@
-import argparse
-import logging
-import sys
-import unittest
-from unittest.mock import patch
-
-import run_glue_deebert
-
-
-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 DeeBertTests(unittest.TestCase):
-    def test_glue_deebert(self):
-        stream_handler = logging.StreamHandler(sys.stdout)
-        logger.addHandler(stream_handler)
-
-        train_args = """
-            run_glue_deebert.py
-            --model_type roberta
-            --model_name_or_path roberta-base
-            --task_name MRPC
-            --do_train
-            --do_eval
-            --do_lower_case
-            --data_dir ./tests/fixtures/tests_samples/MRPC/
-            --max_seq_length 128
-            --per_gpu_eval_batch_size=1
-            --per_gpu_train_batch_size=8
-            --learning_rate 2e-4
-            --num_train_epochs 3
-            --overwrite_output_dir
-            --seed 42
-            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
-            --plot_data_dir ./examples/deebert/results/
-            --save_steps 0
-            --overwrite_cache
-            --eval_after_first_stage
-            """.split()
-
-        eval_args = """
-            run_glue_deebert.py
-            --model_type roberta
-            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
-            --task_name MRPC
-            --do_eval
-            --do_lower_case
-            --data_dir ./tests/fixtures/tests_samples/MRPC/
-            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
-            --plot_data_dir ./examples/deebert/results/
-            --max_seq_length 128
-            --eval_each_highway
-            --eval_highway
-            --overwrite_cache
-            --per_gpu_eval_batch_size=1
-            """.split()
-
-        entropy_eval_args = """
-            run_glue_deebert.py
-            --model_type roberta
-            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
-            --task_name MRPC
-            --do_eval
-            --do_lower_case
-            --data_dir ./tests/fixtures/tests_samples/MRPC/
-            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
-            --plot_data_dir ./examples/deebert/results/
-            --max_seq_length 128
-            --early_exit_entropy 0.1
-            --eval_highway
-            --overwrite_cache
-            --per_gpu_eval_batch_size=1
-            """.split()
-
-        with patch.object(sys, "argv", train_args):
-            result = run_glue_deebert.main()
-            for value in result.values():
-                self.assertGreaterEqual(value, 0.75)
-
-        with patch.object(sys, "argv", eval_args):
-            result = run_glue_deebert.main()
-            for value in result.values():
-                self.assertGreaterEqual(value, 0.75)
-
-        with patch.object(sys, "argv", entropy_eval_args):
-            result = run_glue_deebert.main()
-            for value in result.values():
-                self.assertGreaterEqual(value, 0.75)
@@ -1,38 +0,0 @@
-#!/bin/bash
-export CUDA_VISIBLE_DEVICES=0
-
-PATH_TO_DATA=/h/xinji/projects/GLUE
-
-MODEL_TYPE=bert  # bert or roberta
-MODEL_SIZE=base  # base or large
-DATASET=MRPC  # SST-2, MRPC, RTE, QNLI, QQP, or MNLI
-
-MODEL_NAME=${MODEL_TYPE}-${MODEL_SIZE}
-EPOCHS=10
-if [ $MODEL_TYPE = 'bert' ]
-then
-  EPOCHS=3
-  MODEL_NAME=${MODEL_NAME}-uncased
-fi
-
-
-python -u run_glue_deebert.py \
-  --model_type $MODEL_TYPE \
-  --model_name_or_path $MODEL_NAME \
-  --task_name $DATASET \
-  --do_train \
-  --do_eval \
-  --do_lower_case \
-  --data_dir $PATH_TO_DATA/$DATASET \
-  --max_seq_length 128 \
-  --per_gpu_eval_batch_size=1 \
-  --per_gpu_train_batch_size=8 \
-  --learning_rate 2e-5 \
-  --num_train_epochs $EPOCHS \
-  --overwrite_output_dir \
-  --seed 42 \
-  --output_dir ./saved_models/${MODEL_TYPE}-${MODEL_SIZE}/$DATASET/two_stage \
-  --plot_data_dir ./results/ \
-  --save_steps 0 \
-  --overwrite_cache \
-  --eval_after_first_stage
@@ -809,6 +809,10 @@ def main():

    # Save the trained model and the tokenizer
    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
+        # Create output directory if needed
+        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
+            os.makedirs(args.output_dir)
+
        logger.info("Saving model checkpoint to %s", args.output_dir)
        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
@@ -14,9 +14,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, CTRL, BERT, RoBERTa, XLNet).
-GPT, GPT-2 and CTRL are fine-tuned using a causal language modeling (CLM) loss. BERT and RoBERTa are fine-tuned
-using a masked language modeling (MLM) loss. XLNet is fine-tuned using a permutation language modeling (PLM) loss.
+Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).
+GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned
+using a masked language modeling (MLM) loss.
 """


@@ -33,7 +33,6 @@ from transformers import (
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
-    DataCollatorForPermutationLanguageModeling,
    HfArgumentParser,
    LineByLineTextDataset,
    PreTrainedTokenizer,
@@ -102,15 +101,6 @@ class DataTrainingArguments:
    mlm_probability: float = field(
        default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
    )
-    plm_probability: float = field(
-        default=1 / 6,
-        metadata={
-            "help": "Ratio of length of a span of masked tokens to surrounding context length for permutation language modeling."
-        },
-    )
-    max_span_length: int = field(
-        default=5, metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."}
-    )

    block_size: int = field(
        default=-1,
@@ -217,8 +207,8 @@ def main():

    if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
        raise ValueError(
-            "BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"
-            "--mlm flag (masked language modeling)."
+            "BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the --mlm "
+            "flag (masked language modeling)."
        )

    if data_args.block_size <= 0:
@@ -231,14 +221,9 @@ def main():

    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
    eval_dataset = get_dataset(data_args, tokenizer=tokenizer, evaluate=True) if training_args.do_eval else None
-    if config.model_type == "xlnet":
-        data_collator = DataCollatorForPermutationLanguageModeling(
-            tokenizer=tokenizer, plm_probability=data_args.plm_probability, max_span_length=data_args.max_span_length,
-        )
-    else:
-        data_collator = DataCollatorForLanguageModeling(
-            tokenizer=tokenizer, mlm=data_args.mlm, mlm_probability=data_args.mlm_probability
-        )
+    data_collator = DataCollatorForLanguageModeling(
+        tokenizer=tokenizer, mlm=data_args.mlm, mlm_probability=data_args.mlm_probability
+    )

    # Initialize our Trainer
    trainer = Trainer(
@@ -1,11 +1,14 @@
 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
-from pytorch_lightning.utilities import rank_zero_info
+import torch
+from pytorch_lightning.utilities import rank_zero_info, rank_zero_only

 from transformers import (
    AdamW,
@@ -39,6 +42,14 @@ MODEL_MODES = {
 }


+def set_seed(args: argparse.Namespace):
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    if args.gpus > 0:
+        torch.cuda.manual_seed_all(args.seed)
+
+
 class BaseTransformer(pl.LightningModule):
    def __init__(
        self,
@@ -52,11 +63,7 @@ class BaseTransformer(pl.LightningModule):
    ):
        """Initialize a model, tokenizer and config."""
        super().__init__()
-        # TODO: move to self.save_hyperparameters()
-        # self.save_hyperparameters()
-        # can also expand arguments into trainer signature for easier reading
-
-        self.hparams = hparams
+        self.hparams = hparams  # TODO: move to self.save_hyperparameters()
        self.step_count = 0
        self.tfmr_ckpts = {}
        self.output_dir = Path(self.hparams.output_dir)
@@ -107,12 +114,20 @@ class BaseTransformer(pl.LightningModule):
        ]
        optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)
        self.opt = optimizer
+        return [optimizer]

-        scheduler = get_linear_schedule_with_warmup(
-            self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=self.total_steps
-        )
-        scheduler = {"scheduler": scheduler, "interval": "step", "frequency": 1}
-        return [optimizer], [scheduler]
+    def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None):
+        if self.trainer.use_tpu:
+            xm.optimizer_step(optimizer)
+        else:
+            optimizer.step()
+        optimizer.zero_grad()
+        self.lr_scheduler.step()
+
+    def get_tqdm_dict(self):
+        avg_loss = getattr(self.trainer, "avg_loss", 0.0)
+        tqdm_dict = {"loss": "{:.3f}".format(avg_loss), "lr": self.lr_scheduler.get_last_lr()[-1]}
+        return tqdm_dict

    def test_step(self, batch, batch_nb):
        return self.validation_step(batch, batch_nb)
@@ -120,24 +135,26 @@ class BaseTransformer(pl.LightningModule):
    def test_epoch_end(self, outputs):
        return self.validation_end(outputs)

-    def setup(self, step):
-        train_batch_size = self.hparams.train_batch_size
-        dataloader = self.get_dataloader("train", train_batch_size)
-        self.train_loader = dataloader
-        self.total_steps = (
-            (len(dataloader.dataset) // (train_batch_size * max(1, self.hparams.gpus)))
-            // self.hparams.accumulate_grad_batches
-            * float(self.hparams.max_epochs)
-        )
-
    def train_dataloader(self):
-        return self.train_loader
+        train_batch_size = self.hparams.train_batch_size
+        dataloader = self.load_dataset("train", train_batch_size)
+
+        t_total = (
+            (len(dataloader.dataset) // (train_batch_size * max(1, self.hparams.n_gpu)))
+            // self.hparams.gradient_accumulation_steps
+            * float(self.hparams.num_train_epochs)
+        )
+        scheduler = get_linear_schedule_with_warmup(
+            self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=t_total
+        )
+        self.lr_scheduler = scheduler
+        return dataloader

    def val_dataloader(self):
-        return self.get_dataloader("dev", self.hparams.eval_batch_size)
+        return self.load_dataset("dev", self.hparams.eval_batch_size)

    def test_dataloader(self):
-        return self.get_dataloader("test", self.hparams.eval_batch_size)
+        return self.load_dataset("test", self.hparams.eval_batch_size)

    def _feature_file(self, mode):
        return os.path.join(
@@ -185,18 +202,18 @@ 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=0, type=int, help="Linear warmup over warmup_steps.")
+        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("--num_train_epochs", dest="max_epochs", default=3, type=int)
+        parser.add_argument(
+            "--num_train_epochs", default=3, type=int, help="Total number of training epochs to perform."
+        )
+
        parser.add_argument("--train_batch_size", default=32, type=int)
        parser.add_argument("--eval_batch_size", default=32, type=int)


 class LoggingCallback(pl.Callback):
-    def on_batch_end(self, trainer, pl_module):
-        lrs = {f"lr_group_{i}": lr for i, lr in enumerate(self.lr_scheduler.get_lr())}
-        pl_module.logger.log_metrics(lrs)
-
+    @rank_zero_only
    def on_validation_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
        rank_zero_info("***** Validation results *****")
        metrics = trainer.callback_metrics
@@ -205,15 +222,16 @@ class LoggingCallback(pl.Callback):
            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):
-        rank_zero_info("***** Test results *****")
+        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:
            for key in sorted(metrics):
                if key not in ["log", "progress_bar"]:
-                    rank_zero_info("{} = {}\n".format(key, str(metrics[key])))
+                    logger.info("{} = {}\n".format(key, str(metrics[key])))
                    writer.write("{} = {}\n".format(key, str(metrics[key])))


@@ -236,23 +254,26 @@ def add_generic_args(parser, root_dir) -> None:
    parser.add_argument(
        "--fp16_opt_level",
        type=str,
-        default="O2",
+        default="O1",
        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("--n_tpu_cores", dest="tpu_cores", type=int, default=0)
-    parser.add_argument("--max_grad_norm", dest="gradient_clip_val", default=1.0, type=float, help="Max gradient norm")
+    parser.add_argument("--fast_dev_run", action="store_true")
+    parser.add_argument("--gpus", 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.")
    parser.add_argument("--do_predict", action="store_true", help="Whether to run predictions on the test set.")
    parser.add_argument(
        "--gradient_accumulation_steps",
-        dest="accumulate_grad_batches",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )

    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(
@@ -265,13 +286,10 @@ def generic_train(
    logging_callback=None,
    **extra_train_kwargs
 ):
-    pl.seed_everything(args.seed)
-
    # init model
+    set_seed(args)
    odir = Path(model.hparams.output_dir)
    odir.mkdir(exist_ok=True)
-
-    # add custom checkpoints
    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
@@ -281,25 +299,38 @@ def generic_train(

    train_params = {}

-    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fp16:
-        train_params["precision"] = 16
+        train_params["use_amp"] = args.fp16
        train_params["amp_level"] = args.fp16_opt_level

+    if args.n_tpu_cores > 0:
+        global xm
+        import torch_xla.core.xla_model as xm
+
+        train_params["num_tpu_cores"] = args.n_tpu_cores
+        train_params["gpus"] = 0
+
    if args.gpus > 1:
        train_params["distributed_backend"] = "ddp"

-    trainer = pl.Trainer.from_argparse_args(
-        args,
-        weights_summary=None,
-        callbacks=[logging_callback] + extra_callbacks,
+    trainer = pl.Trainer(
        logger=logger,
-        checkpoint_callback=checkpoint_callback,
+        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,
    )

    if args.do_train:
        trainer.fit(model)
-
+    trainer.logger.log_hyperparams(args)
+    trainer.logger.save()
    return trainer
@@ -1,10 +1,10 @@
 import faiss
 import nlp
 import numpy as np
-import streamlit as st
 import torch
 from elasticsearch import Elasticsearch

+import streamlit as st
 import transformers
 from eli5_utils import (
    embed_questions_for_retrieval,
@@ -193,12 +193,12 @@ def make_qa_retriever_model(model_name="google/bert_uncased_L-8_H-512_A-8", from
 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(q_ls, max_length=max_len, padding="max_length", truncation=True)
+    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(a_ls, max_length=max_len, padding="max_length", truncation=True)
+    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),
@@ -375,12 +375,12 @@ def make_qa_s2s_model(model_name="facebook/bart-large", from_file=None, device="
 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(q_ls, max_length=max_len, padding="max_length", truncation=True)
+    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(a_ls, max_length=min(max_len, max_a_len), padding="max_length", truncation=True)
+    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),
@@ -531,7 +531,7 @@ def qa_s2s_generate(
 # ELI5-trained retrieval model usage
 ###############
 def embed_passages_for_retrieval(passages, tokenizer, qa_embedder, max_length=128, device="cuda:0"):
-    a_toks = tokenizer(passages, max_length=max_length, padding="max_length", truncation=True)
+    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),
@@ -542,7 +542,7 @@ def embed_passages_for_retrieval(passages, tokenizer, qa_embedder, max_length=12


 def embed_questions_for_retrieval(q_ls, tokenizer, qa_embedder, device="cuda:0"):
-    q_toks = tokenizer(q_ls, max_length=128, padding="max_length", truncation=True)
+    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),
@@ -424,7 +424,7 @@ MASKED_BERT_INPUTS_DOCSTRING = r"""

            Indices can be obtained using :class:`transformers.BertTokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
-            :func:`transformers.PreTrainedTokenizer.__call__` for details.
+            :func:`transformers.PreTrainedTokenizer.encode_plus` for details.

            `What are input IDs? <../glossary.html#input-ids>`__
        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
@@ -875,6 +875,10 @@ def main():

    # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
+        # Create output directory if needed
+        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
+            os.makedirs(args.output_dir)
+
        logger.info("Saving model checkpoint to %s", args.output_dir)
        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
@@ -1059,6 +1059,10 @@ def main():

    # Save the trained model and the tokenizer
    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
+        # Create output directory if needed
+        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
+            os.makedirs(args.output_dir)
+
        logger.info("Saving model checkpoint to %s", args.output_dir)
        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
@@ -108,10 +108,7 @@ def main():
        level=logging.INFO,
    )
    logger.warning(
-        "device: %s, n_replicas: %s, 16-bits training: %s",
-        training_args.device,
-        training_args.n_replicas,
-        training_args.fp16,
+        "device: %s, n_gpu: %s, 16-bits training: %s", training_args.device, training_args.n_gpu, training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)

@@ -510,13 +510,12 @@ def convert_examples_to_features(
            else:
                text_b = example.question + " " + ending

-            inputs = tokenizer(
+            inputs = tokenizer.encode_plus(
                text_a,
                text_b,
                add_special_tokens=True,
                max_length=max_length,
-                padding="max_length",
-                truncation=True,
+                pad_to_max_length=True,
                return_overflowing_tokens=True,
            )
            if "num_truncated_tokens" in inputs and inputs["num_truncated_tokens"] > 0:
@@ -77,7 +77,7 @@ exact_match = 86.91
 ```

 This fine-tuned model is available as a checkpoint under the reference
-[`bert-large-uncased-whole-word-masking-finetuned-squad`](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad).
+`bert-large-uncased-whole-word-masking-finetuned-squad`.

 #### Fine-tuning XLNet on SQuAD

@@ -176,5 +176,4 @@ python run_tf_squad.py \
    --doc_stride 128    
 ```

-
-For the moment evaluation is not available in the Tensorflow Trainer only the training.
+For the moment the evaluation is not available in the Tensorflow Trainer only the training.
@@ -199,9 +199,6 @@ def train(args, train_dataset, model, tokenizer):
                        {"langs": (torch.ones(batch[0].shape, dtype=torch.int64) * args.lang_id).to(args.device)}
                    )

-            if isinstance(model, torch.nn.DataParallel):
-                inputs["return_tuple"] = True
-
            outputs = model(**inputs)
            # model outputs are always tuple in transformers (see doc)
            loss = outputs[0]
@@ -243,6 +240,8 @@ def train(args, train_dataset, model, tokenizer):
                # Save model checkpoint
                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                    output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
+                    if not os.path.exists(output_dir):
+                        os.makedirs(output_dir)
                    # Take care of distributed/parallel training
                    model_to_save = model.module if hasattr(model, "module") else model
                    model_to_save.save_pretrained(output_dir)
@@ -316,8 +315,7 @@ def evaluate(args, model, tokenizer, prefix=""):
                    inputs.update(
                        {"langs": (torch.ones(batch[0].shape, dtype=torch.int64) * args.lang_id).to(args.device)}
                    )
-            if isinstance(model, torch.nn.DataParallel):
-                inputs["return_tuple"] = True
+
            outputs = model(**inputs)

        for i, feature_index in enumerate(feature_indices):
@@ -770,6 +768,10 @@ def main():

    # Save the trained model and the tokenizer
    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
+        # Create output directory if needed
+        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
+            os.makedirs(args.output_dir)
+
        logger.info("Saving model checkpoint to %s", args.output_dir)
        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
@@ -1,160 +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.
-""" Fine-tuning the library models for question-answering."""
-
-
-import logging
-import os
-import sys
-from dataclasses import dataclass, field
-from typing import Optional
-
-from transformers import AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer, HfArgumentParser, SquadDataset
-from transformers import SquadDataTrainingArguments as DataTrainingArguments
-from transformers import Trainer, TrainingArguments
-
-
-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"}
-    )
-    use_fast: bool = field(default=False, metadata={"help": "Set this flag to use fast tokenization."})
-    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
-    # or just modify its tokenizer_config.json.
-    cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
-    )
-
-
-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))
-
-    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
-        # If we pass only one argument to the script and it's the path to a json file,
-        # let's parse it to get our arguments.
-        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
-    else:
-        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)
-
-    # Prepare Question-Answering task
-    # 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,
-        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 = AutoModelForQuestionAnswering.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
-    is_language_sensitive = hasattr(model.config, "lang2id")
-    train_dataset = (
-        SquadDataset(
-            data_args, tokenizer=tokenizer, is_language_sensitive=is_language_sensitive, cache_dir=model_args.cache_dir
-        )
-        if training_args.do_train
-        else None
-    )
-    eval_dataset = (
-        SquadDataset(
-            data_args,
-            tokenizer=tokenizer,
-            mode="dev",
-            is_language_sensitive=is_language_sensitive,
-            cache_dir=model_args.cache_dir,
-        )
-        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,)
-
-    # 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)
-
-
-def _mp_fn(index):
-    # For xla_spawn (TPUs)
-    main()
-
-
-if __name__ == "__main__":
-    main()
@@ -137,9 +137,9 @@ def main():
        level=logging.INFO,
    )
    logger.info(
-        "n_replicas: %s, distributed training: %s, 16-bits training: %s",
-        training_args.n_replicas,
-        bool(training_args.n_replicas > 1),
+        "n_gpu: %s, distributed training: %s, 16-bits training: %s",
+        training_args.n_gpu,
+        bool(training_args.n_gpu > 1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)
@@ -5,7 +5,7 @@ psutil
 sacrebleu
 rouge-score
 tensorflow_datasets
-pytorch-lightning==0.8.5
+pytorch-lightning==0.8.1
 matplotlib
 git-python==1.0.3
 faiss
@@ -13,4 +13,3 @@ streamlit
 elasticsearch
 pandas
 nlp
-wget
@@ -1,245 +0,0 @@
-## Sequence to Sequence
-
-This directory contains examples for finetuning and evaluating transformers on summarization and translation tasks.
-Summarization support is more mature than translation support.
-Please tag @sshleifer with any issues/unexpected behaviors, or send a PR!
-For `bertabs` instructions, see `bertabs/README.md`. 
-
-
-### Data
-XSUM Data:
-```bash
-cd examples/seq2seq
-wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz
-tar -xzvf xsum.tar.gz
-export XSUM_DIR=${PWD}/xsum
-```
-this should make a directory called `xsum/` with files like `test.source`.
-To use your own data, copy that files format. Each article to be summarized is on its own line.
-
-CNN/DailyMail data
-```bash
-cd examples/seq2seq
-wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/cnn_dm.tgz
-tar -xzvf cnn_dm.tgz
-export CNN_DIR=${PWD}/cnn_dm
-this should make a directory called `cnn_dm/` with files like `test.source`.
-```
-
-WMT16 English-Romanian Translation Data:
-
-This dataset comes in two formats. The "packed" version merges short training examples into examples of <200 tokens to increase GPU utilization (and also improves validation performance).
-
-```bash
-cd examples/seq2seq
-wget https://s3.amazonaws.com/datasets.huggingface.co/translation/wmt_en_ro_packed_train_200.tgz
-tar -xzvf wmt_en_ro_packed_200.tgz
-export ENRO_DIR=wmt_en_ro_packed_train_200
-```
- 
-The original data can also be downloaded with this command:
-```bash
-wget https://s3.amazonaws.com/datasets.huggingface.co/translation/wmt_en_ro.tar.gz
-tar -xzvf wmt_en_ro.tar.gz
-export ENRO_DIR=${PWD}/wmt_en_ro
-this should make a directory called `wmt_en_ro/` with files like `test.source`.
-```
-
-If you are using your own data, it must be formatted as one directory with 6 files: train.source, train.target, val.source, val.target, test.source, test.target.  
-The `.source` files are the input, the `.target` files are the desired output.
-
- 
-### Tips and Tricks
-
-General Tips:
- since you need to run from `examples/seq2seq`, and likely need to modify code, the easiest workflow is fork transformers, clone your fork, and run `pip install -e .` before you get started.   
- try `--freeze_encoder` or `--freeze_embeds` for faster training/larger batch size.  (3hr per epoch with bs=8, see the "xsum_shared_task" command below)
- `fp16_opt_level=O1` (the default works best).
- 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.
- This warning can be safely ignored: 
-    > "Some weights of BartForConditionalGeneration were not initialized from the model checkpoint at facebook/bart-large-xsum and are newly initialized: ['final_logits_bias']"
- Both finetuning and eval are 30% faster with `--fp16`. For that you need to [install apex](https://github.com/NVIDIA/apex#quick-start).
- Read scripts before you run them! 
-
-Summarization Tips:
- (summ) 1 epoch at batch size 1 for bart-large takes 24 hours and requires 13GB GPU RAM with fp16 on an NVIDIA-V100.
- 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.
- For CNN/DailyMail, the default `val_max_target_length` and `test_max_target_length` will truncate the ground truth labels, resulting in slightly higher rouge scores. To get accurate rouge scores, you should rerun calculate_rouge on the `{output_dir}/test_generations.txt` file saved by `trainer.test()`
- `--max_target_length=60 --val_max_target_length=60 --test_max_target_length=100 ` is a reasonable setting for XSUM.
- `wandb` can be used by specifying `--logger_name wandb`. It is useful for reproducibility. Specify the environment variable `WANDB_PROJECT='hf_xsum'` to do the XSUM shared task.
- If you are finetuning on your own dataset, start from `distilbart-cnn-12-6` if you want long summaries and `distilbart-xsum-12-6` if you want short summaries.
-(It rarely makes sense to start from `bart-large` unless you are a researching finetuning methods). 
-
-**Update 2018-07-18**
-Datasets: Seq2SeqDataset will be used for all models besides MBart, for which MBartDataset will be used.**
-A new dataset is needed to support multilingual tasks.
-
-### Summarization Finetuning
-Run/modify `finetune.sh`
-
-The following command should work on a 16GB GPU:
-```bash
-./finetune.sh \
-    --data_dir $XSUM_DIR \
-    --train_batch_size=1 \
-    --eval_batch_size=1 \
-    --output_dir=xsum_results \
-    --num_train_epochs 1 \
-    --model_name_or_path facebook/bart-large
-```
-
-### Translation Finetuning
-
-First, follow the wmt_en_ro download instructions.
-Then you can finetune mbart_cc25 on english-romanian with the following command.
-**Recommendation:** Read and potentially modify the fairly opinionated defaults in `train_mbart_cc25_enro.sh` script before running it.
-
-Best performing command:
-```bash
-# optionally
-export ENRO_DIR='wmt_en_ro_packed_train_200' # Download instructions above
-# export WANDB_PROJECT="MT" # optional 
-export MAX_LEN=200
-export BS=4
-export GAS=8 # gradient accumulation steps
-./train_mbart_cc25_enro.sh --output_dir enro_finetune_baseline --label_smoothing 0.1 --fp16_opt_level=O1 --logger_name wandb --sortish_sampler
-```
-This should take < 2h/epoch on a 16GB v100 and achieve val_avg_ BLEU score above 25. (you can see in wandb or metrics.json).
-To get results in line with fairseq, you need to do some postprocessing.
-
-MultiGPU command
-(using 8 GPUS as an example)
-```bash
-export ENRO_DIR='wmt_en_ro_packed_train_200' # Download instructions above
- # export WANDB_PROJECT="MT" # optional
-export MAX_LEN=200
-export BS=4
-export GAS=1  # gradient accumulation steps
-./train_mbart_cc25_enro.sh --output_dir enro_finetune_baseline --gpus 8 --logger_name wandb
-```
-### Finetuning Outputs 
-As you train, `output_dir` will be filled with files, that look kind of like this (comments are mine). 
-Some of them are metrics, some of them are checkpoints, some of them are metadata. Here is a quick tour:
-
-```bash
-output_dir
-├── best_tfmr  # this is a huggingface checkpoint generated by save_pretrained. It is the same model as the PL .ckpt file below
-│   ├── config.json
-│   ├── merges.txt
-│   ├── pytorch_model.bin
-│   ├── special_tokens_map.json
-│   ├── tokenizer_config.json
-│   └── vocab.json
-├── git_log.json   # repo, branch, and commit hash
-├── val_avg_rouge2=0.1984-step_count=11.ckpt  # this is a pytorch lightning checkpoint associated with the best val score. (it will be called BLEU for MT)
-├── metrics.json  # new validation metrics will continually be appended to this
-├── student  # this is a huggingface checkpoint generated by SummarizationDistiller. It is the student before it gets finetuned.
-│   ├── config.json
-│   └── pytorch_model.bin
-├── test_generations.txt 
-# ^^ are the summaries or translations produced by your best checkpoint on the test data. Populated when training is done 
-├── test_results.txt  # a convenience file with the test set metrics. This data is also in metrics.json['test']
-├── hparams.pkl  # the command line args passed after some light preprocessing. Should be saved fairly quickly.
-```
-After training, you can recover the best checkpoint by running
-```python
-from transformers import AutoModelForSeq2SeqLM
-model = AutoModelForSeq2SeqLM.from_pretrained(f'{output_dir}/best_tfmr')
-```
-
-### Evaluation Commands
-
-To create summaries for each article in dataset, we use `run_eval.py`, here are a few commands that run eval for different tasks and models.
-If 'translation' is in your task name, the computed metric will be BLEU. Otherwise, ROUGE will be used.
-
-For t5, you need to specify --task translation_{src}_to_{tgt} as follows:
-```bash
-export DATA_DIR=wmt_en_ro
-python run_eval.py t5-base \
-    $DATA_DIR/val.source t5_val_generations.txt \
-    --reference_path $DATA_DIR/val.target \
-    --score_path enro_bleu.json \
-    --task translation_en_to_ro \
-    --n_obs 100 \
-    --device cuda \
-    --fp16 \
-    --bs 32
-```
-
-This command works for MBART, although the BLEU score is suspiciously low.
-```bash
-export DATA_DIR=wmt_en_ro
-python run_eval.py facebook/mbart-large-en-ro $DATA_DIR/val.source mbart_val_generations.txt \
-    --reference_path $DATA_DIR/val.target \
-    --score_path enro_bleu.json \
-    --task translation \
-    --n_obs 100 \
-    --device cuda \
-    --fp16 \
-    --bs 32
-```
-
-Summarization (xsum will be very similar):
-```bash
-export DATA_DIR=cnn_dm
-python run_eval.py sshleifer/distilbart-cnn-12-6 $DATA_DIR/val.source dbart_val_generations.txt \
-    --reference_path $DATA_DIR/val.target \
-    --score_path cnn_rouge.json \
-    --task summarization \
-    --n_obs 100 \
-    --device cuda \
-    --fp16 \
-    --bs 32
-```
-
-
-### DistilBART
-![DBART](https://huggingface.co/front/thumbnails/distilbart_large.png)
-
-For the CNN/DailyMail dataset, (relatively longer, more extractive summaries), we found a simple technique that works:
-you just copy alternating layers from `bart-large-cnn` and finetune more on the same data. 
-
-For the XSUM dataset, that didn’t work as well so we used that same initialization strategy followed by a combination of Distillbert’s ce_loss and the hidden states MSE loss used in the tinybert paper.
-
-You can see the performance tradeoffs of model sizes [here](https://docs.google.com/spreadsheets/d/1EkhDMwVO02m8jCD1cG3RoFPLicpcL1GQHTQjfvDYgIM/edit#gid=0).
-and more granular timing results [here](https://docs.google.com/spreadsheets/d/1EkhDMwVO02m8jCD1cG3RoFPLicpcL1GQHTQjfvDYgIM/edit#gid=1753259047&range=B2:I23).
-
-#### No Teacher Distillation
-To run the simpler distilbart-cnn style distillation all you need is data, a GPU, and a properly initialized student.
-You don't even need `distillation.py`.
-
-Some [un-finetuned students](https://huggingface.co/models?search=sshleifer%2Fstudent) are available for replication purposes.
-They are initialized by copying layers from the associated `bart-large-{cnn|xsum}` teacher using `--init_strategy alternate`. (You can read about that in `initialization_utils.py`)
-The command that produced `sshleifer/distilbart-cnn-12-6` is
-```bash
-./train_distilbart_cnn.sh
-```  
-runtime: 6H on NVIDIA RTX 24GB GPU
-
-*Note*: You can get the same simple distillation logic by using `./run_distiller.sh --no_teacher` followed by identical arguments as the ones in `train_distilbart_cnn.sh`.
-If you are using `wandb` and comparing the two distillation methods, using this entry point will make your logs consistent,
-because you will have the same hyperparameters logged in every run.
-
-#### With a teacher
-*Note* only BART variants are supported
-
-In this method, we use try to enforce that the student and teacher produce similar encoder_outputs, logits, and hidden_states using `BartSummarizationDistiller`.
-This is how `sshleifer/distilbart-xsum*` checkpoints were produced.
-
-The command that produced `sshleifer/distilbart-xsum-12-6` is:
-
-```bash
-./train_distilbart_xsum.sh  
-```
-
-runtime: 13H on V-100 16GB GPU. 
-
-### Contributing
- follow the standard contributing guidelines and code of conduct.
- add tests to `test_seq2seq_examples.py`
- To run only the seq2seq tests, you must be in the root of the repository and run:
-```bash
-pytest examples/seq2seq/  
-```
@@ -1,13 +0,0 @@
-# Add parent directory to python path to access lightning_base.py
-export PYTHONPATH="../":"${PYTHONPATH}"
-
-python finetune.py \
--data_dir=$CNN_DIR \
--learning_rate=3e-5 \
--train_batch_size=$BS \
--eval_batch_size=$BS \
--output_dir=$OUTPUT_DIR \
--max_source_length=512 \
--val_check_interval=0.1 --n_val=200 \
--do_train --do_predict \
- $@
@@ -1,80 +0,0 @@
-"""Fill examples with bitext up to max_tokens without breaking up examples.
-[['I went', 'yo fui'],
-['to the store', 'a la tienda']
-]
-=> ['I went to the store', 'yo fui a la tienda']
-"""
-
-import argparse
-from pathlib import Path
-
-from tqdm import tqdm
-
-from transformers import AutoTokenizer
-
-
-def pack_examples(tok, src_examples, tgt_examples, max_tokens=1024):
-
-    finished_src, finished_tgt = [], []
-
-    sorted_examples = list(sorted(zip(src_examples, tgt_examples), key=lambda x: len(x[0])))
-    new_src, new_tgt = sorted_examples[0]
-
-    def is_too_big(strang):
-        return tok(strang, return_tensors="pt").input_ids.shape[1] > max_tokens
-
-    for src, tgt in tqdm(sorted_examples[1:]):
-        cand_src = new_src + " " + src
-        cand_tgt = new_tgt + " " + tgt
-        if is_too_big(cand_src) or is_too_big(cand_tgt):  # cant fit, finalize example
-            finished_src.append(new_src)
-            finished_tgt.append(new_tgt)
-            new_src, new_tgt = src, tgt
-        else:  # can fit, keep adding
-            new_src, new_tgt = cand_src, cand_tgt
-        # import ipdb; ipdb.set_trace()
-
-    # cleanup
-    if new_src:
-        assert new_tgt
-        finished_src.append(new_src)
-        finished_tgt.append(new_tgt)
-    return finished_src, finished_tgt
-
-
-def minify(src_dir: Path, dest_dir: Path, n: int):
-    """Write first n lines of each file f in src_dir to dest_dir/f"""
-    dest_dir.mkdir(exist_ok=True)
-    for path in src_dir.iterdir():
-        new = [x.rstrip() for x in list(path.open().readlines())][:n]
-        dest_path = dest_dir.joinpath(path.name)
-        print(dest_path)
-        dest_path.open("w").write("\n".join(new))
-
-
-def pack_data_dir(tok, data_dir: Path, max_tokens, save_path):
-    save_path = Path(save_path)
-    save_path.mkdir(exist_ok=True)
-    for split in ["val", "test", "train"]:
-        src_path, tgt_path = data_dir / f"{split}.source", data_dir / f"{split}.target"
-        src_docs = [x.rstrip() for x in Path(src_path).open().readlines()]
-        tgt_docs = [x.rstrip() for x in Path(tgt_path).open().readlines()]
-        packed_src, packed_tgt = pack_examples(tok, src_docs, tgt_docs, max_tokens)
-        print(f"packed {split} split from {len(src_docs)} examples -> {len(packed_src)}.")
-        Path(save_path / f"{split}.source").open("w").write("\n".join(packed_src))
-        Path(save_path / f"{split}.target").open("w").write("\n".join(packed_tgt))
-
-
-def packer_cli():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--tok_name", type=str, help="like facebook/bart-large-cnn,t5-base, etc.")
-    parser.add_argument("--max_seq_len", type=int, default=128)
-    parser.add_argument("--data_dir", type=str)
-    parser.add_argument("--save_path", type=str)
-    args = parser.parse_args()
-    tokenizer = AutoTokenizer.from_pretrained(args.tok_name)
-    return pack_data_dir(tokenizer, Path(args.data_dir), args.max_seq_len, args.save_path)
-
-
-if __name__ == "__main__":
-    packer_cli()
@@ -1,65 +0,0 @@
-### Motivation
-Without processing, english-> romanian mbart-large-en-ro gets BLEU score 26.8 on the WMT data.
-With post processing, it can score 37..
-Here is the postprocessing code, stolen from @mjpost in this [issue](https://github.com/pytorch/fairseq/issues/1758)
-
-
-
-### Instructions
-Note: You need to have your test_generations.txt before you start this process.
-(1) Setup `mosesdecoder` and `wmt16-scripts`
-```bash
-cd $HOME
-git clone git@github.com:moses-smt/mosesdecoder.git
-cd mosesdecoder  
-git@github.com:rsennrich/wmt16-scripts.git
-```
-
-(2) define a function for post processing.
- It removes diacritics and does other things I don't understand 
-```bash
-ro_post_process () {
-  sys=$1
-  ref=$2
-  export MOSES_PATH=$HOME/mosesdecoder
-  REPLACE_UNICODE_PUNCT=$MOSES_PATH/scripts/tokenizer/replace-unicode-punctuation.perl
-  NORM_PUNC=$MOSES_PATH/scripts/tokenizer/normalize-punctuation.perl
-  REM_NON_PRINT_CHAR=$MOSES_PATH/scripts/tokenizer/remove-non-printing-char.perl
-  REMOVE_DIACRITICS=$MOSES_PATH/wmt16-scripts/preprocess/remove-diacritics.py
-  NORMALIZE_ROMANIAN=$MOSES_PATH/wmt16-scripts/preprocess/normalise-romanian.py
-  TOKENIZER=$MOSES_PATH/scripts/tokenizer/tokenizer.perl
-
-
-
-  lang=ro
-  for file in $sys $ref; do
-    cat $file \
-    | $REPLACE_UNICODE_PUNCT \
-    | $NORM_PUNC -l $lang \
-    | $REM_NON_PRINT_CHAR \
-    | $NORMALIZE_ROMANIAN \
-    | $REMOVE_DIACRITICS \
-    | $TOKENIZER -no-escape -l $lang \
-    > $(basename $file).tok
-  done
-  # compute BLEU
-  cat $(basename $sys).tok | sacrebleu -tok none -s none -b $(basename $ref).tok
-}
-```
-
-(3) Call the function on test_generations.txt and test.target
-For example,
-```bash
-ro_post_process enro_finetune/test_generations.txt wmt_en_ro/test.target
-```
-This will split out a new blue score and write a new fine called `test_generations.tok` with post-processed outputs.
-
-
-
-
-
-
-
-
-
-```
@@ -1,116 +0,0 @@
-import argparse
-import json
-from pathlib import Path
-
-import torch
-from tqdm import tqdm
-
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-
-
-try:
-    from .utils import calculate_rouge, use_task_specific_params, calculate_bleu_score, trim_batch
-except ImportError:
-    from utils import calculate_rouge, use_task_specific_params, calculate_bleu_score, trim_batch
-
-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_or_translations(
-    examples: list,
-    out_file: str,
-    model_name: str,
-    batch_size: int = 8,
-    device: str = DEFAULT_DEVICE,
-    fp16=False,
-    task="summarization",
-    decoder_start_token_id=None,
-    **gen_kwargs,
-) -> 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()
-    if decoder_start_token_id is None:
-        decoder_start_token_id = gen_kwargs.pop("decoder_start_token_id", None)
-
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-    # update config with summarization specific params
-    use_task_specific_params(model, task)
-
-    for batch in tqdm(list(chunks(examples, batch_size))):
-        if "t5" in model_name:
-            batch = [model.config.prefix + text for text in batch]
-        batch = tokenizer(batch, return_tensors="pt", truncation=True, padding="max_length").to(device)
-        input_ids, attention_mask = trim_batch(**batch, pad_token_id=tokenizer.pad_token_id)
-        summaries = model.generate(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            decoder_start_token_id=decoder_start_token_id,
-            **gen_kwargs,
-        )
-        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("model_name", type=str, help="like facebook/bart-large-cnn,t5-base, etc.")
-    parser.add_argument("input_path", type=str, help="like cnn_dm/test.source")
-    parser.add_argument("save_path", type=str, help="where to save summaries")
-
-    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("--task", type=str, default="summarization", help="typically translation or summarization")
-    parser.add_argument("--bs", type=int, default=8, required=False, help="batch size")
-    parser.add_argument(
-        "--decoder_start_token_id",
-        type=int,
-        default=None,
-        required=False,
-        help="decoder_start_token_id (otherwise will look at config)",
-    )
-    parser.add_argument(
-        "--n_obs", type=int, default=-1, required=False, help="How many observations. Defaults to all."
-    )
-    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()]
-    if args.n_obs > 0:
-        examples = examples[: args.n_obs]
-
-    generate_summaries_or_translations(
-        examples,
-        args.save_path,
-        args.model_name,
-        batch_size=args.bs,
-        device=args.device,
-        fp16=args.fp16,
-        task=args.task,
-        decoder_start_token_id=args.decoder_start_token_id,
-    )
-    if args.reference_path is None:
-        return
-    # Compute scores
-    score_fn = calculate_bleu_score if "translation" in args.task else calculate_rouge
-    output_lns = [x.rstrip() for x in open(args.save_path).readlines()]
-    reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()][: len(output_lns)]
-    scores: dict = score_fn(output_lns, reference_lns)
-    if args.score_path is not None:
-        json.dump(scores, open(args.score_path, "w+"))
-    return scores
-
-
-if __name__ == "__main__":
-    run_generate()
@@ -1,92 +0,0 @@
-import argparse
-import os
-import sys
-import tarfile
-import tempfile
-from pathlib import Path
-from unittest.mock import patch
-
-import pytest
-import pytorch_lightning as pl
-import wget
-
-from transformers.testing_utils import slow
-
-from .finetune import SummarizationModule, main
-from .test_seq2seq_examples import CUDA_AVAILABLE, MBART_TINY
-from .utils import load_json
-
-
-def fetch_and_save_wmt_100():
-    # TODO(SS): DELETEME
-    DATA_URL = "https://s3.amazonaws.com/datasets.huggingface.co/translation/wmt_en_ro_test_data.tgz"
-
-    dest_dir = "wmt_100_dir"
-    if os.path.isdir(dest_dir):
-        return dest_dir
-    filename = wget.download(DATA_URL)
-    tarball = tarfile.TarFile(filename)
-    tarball.extractall(path=dest_dir)
-    os.remove(filename)
-    return dest_dir
-
-
-@slow
-@pytest.mark.skipif(not CUDA_AVAILABLE, reason="too slow to run on CPU")
-def test_train_mbart_cc25_enro_script():
-    data_dir = "examples/seq2seq/test_data/wmt_en_ro"
-    env_vars_to_replace = {
-        "$MAX_LEN": 200,
-        "$BS": 4,
-        "$GAS": 1,
-        "$ENRO_DIR": data_dir,
-        "facebook/mbart-large-cc25": MBART_TINY,
-    }
-
-    # Clean up bash script
-    bash_script = Path("examples/seq2seq/train_mbart_cc25_enro.sh").open().read().split("finetune.py")[1].strip()
-    bash_script = bash_script.replace("\\\n", "").strip().replace("$@", "")
-    for k, v in env_vars_to_replace.items():
-        bash_script = bash_script.replace(k, str(v))
-    output_dir = tempfile.mkdtemp(prefix="output")
-
-    if CUDA_AVAILABLE:
-        gpus = 1  # torch.cuda.device_count()
-    else:
-        bash_script = bash_script.replace("--fp16", "")
-        gpus = 0
-
-    testargs = (
-        ["finetune.py"]
-        + bash_script.split()
-        + [f"--output_dir={output_dir}", f"--gpus={gpus}", "--learning_rate=3e-1"]
-    )
-    with patch.object(sys, "argv", testargs):
-        parser = argparse.ArgumentParser()
-        parser = pl.Trainer.add_argparse_args(parser)
-        parser = SummarizationModule.add_model_specific_args(parser, os.getcwd())
-        args = parser.parse_args()
-        # assert args.gpus == gpus THIS BREAKS
-        # args.gpus = gpus
-        model = main(args)
-    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)
-
-    assert "test_generations.txt" in contents
-    assert "test_results.txt" in contents
-
-    metrics = load_json(model.metrics_save_path)
-    first_step_stats = metrics["val"][0]
-    last_step_stats = metrics["val"][-1]
-    assert last_step_stats["val_avg_gen_time"] >= 0.01
-    assert 1.0 >= last_step_stats["val_avg_gen_time"]
-    assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]
-
-    assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"], float)
-    # desired_n_evals = int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1)
-    # assert len(metrics["val"]) ==  desired_n_evals
-    assert len(metrics["test"]) == 1
@@ -1,100 +0,0 @@
-UN Chief Says There Is No Military Solution in Syria
-Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.
-The U.N. chief again urged all parties, including the divided U.N. Security Council, to unite and support inclusive negotiations to find a political solution.
-Ban told a news conference Wednesday that he plans to meet with foreign ministers of the five permanent council nations - the U.S., Russia, China, Britain and France - on the sidelines of the General Assembly's ministerial session later this month to discuss Syria.
-He expressed regret that divisions in the council and among the Syrian people and regional powers "made this situation unsolvable."
-Ban urged the five permanent members to show the solidarity and unity they did in achieving an Iran nuclear deal in addressing the Syria crisis.
-8 Poll Numbers That Show Donald Trump Is For Real
-Some have tried to label him a flip-flopper.
-Others have dismissed him as a joke.
-And some are holding out for an implosion.
-But no matter how some Republicans are trying to drag Donald Trump down from atop the polls, it hasn't worked (yet).
-Ten of the last 11 national polls have shown Donald Trump's lead at double digits, and some are starting to ask seriously what it means for the real estate mogul's nomination chances.
-Of course, it's still early in the election cycle.
-None of this is to say that Trump is likely to win the Republican nomination.
-Pundits point out that at this time in 2011, Rick Perry's lead was giving way to a rising Herman Cain, neither of whom won even one state in the nomination process.
-And there are many reasons he would struggle in a general election.
-But outside groups like Jeb Bush's Super PAC and the economic conservative group Club for Growth are recognizing Trump's staying power and beginning to unload their dollars to topple him.
-Here are some recent poll numbers that suggest that the real estate mogul isn't just a passing phase:
-Trump's favorability ratings have turned 180 degrees.
-Right before Donald Trump announced his candidacy in mid-June, a Monmouth University poll showed only two in 10 Republicans had a positive view of the real estate mogul.
-By mid-July, it was 40 percent.
-In early August, it was 52 percent.
-Now, six in 10 Republicans have a favorable view of Donald Trump.
-Roughly three in 10 say they have a negative view.
-And these numbers hold up in early states.
-A Quinnipiac poll in Iowa last week found that 60 percent of Republicans there had a favorable view of Trump.
-Two-thirds of GOP voters would be happy with Trump as the nominee.
-In a CNN/ORC poll last week, 67 percent of Republicans said they would be either "enthusiastic" or "satisfied" if Trump were the nominee.
-Only two in 10 say they would be "upset" if he were the nominee.
-Only Ben Carson generates roughly the same level of enthusiasm as Trump (43 percent say they would be "enthusiastic" vs. 40 percent who say the same of Trump).
-The next closest in enthusiasm?
-Marco Rubio with only 21 percent.
-On the flip side, 47 percent of Republican voters say they would be "dissatisfied" or "upset" if establishment favorite Jeb Bush becomes the nominee.
-A majority of Republicans don't see Trump's temperament as a problem.
-While Donald Trump has been widely criticized for his bombast and insults, 52 percent of leaned Republican voters nationwide think that the real estate mogul has the right temperament to be president, according to Monday's ABC News/Washington Post poll.
-The same number holds in the first-in-the-nation caucus state of Iowa, where the same 52 percent of Republicans think he has the personality to be commander in chief, according to Quinnipiac last week.
-Still, 44 percent think he doesn't have the personality to serve effectively, and almost six in 10 independents say his temperament does not belong in the White House, according to ABC/Post.
-Republican voters are getting used to the idea.
-When they put on their pundit hats, Republican voters think Trump is for real.
-When asked who is most likely to win the GOP nomination, four in 10 said Trump was the best bet, according to a CNN/ORC poll out last week.
-That's a change from when four in 10 placed their money on Jeb Bush in late July.
-Full disclosure: GOP voters haven't had the clearest crystal ball in the past.
-At this time last cycle, four in 10 Republicans picked Rick Perry to win the nomination, vs. only 28 percent for eventual nominee Mitt Romney.
-Still, it shows that a plurality of GOP voters see Trump's campaign as plausible.
-Even if Republicans rallied around another candidate, Trump still beats almost everyone.
-Some pundits point out that the splintered field is likely contributing to Trump's lead, while anti-Trump support is be spread diffusely among more than a dozen other candidates.
-But a Monmouth University poll in early September shows that, in a hypothetical head-to-head matchup between Trump and most other Republican candidates, Trump almost always garners majority support.
-He leads Carly Fiorina by 13 points, Marco Rubio by 14 points, Walker by 15 points, Jeb Bush by 19 points, and, finally, Rand Paul, John Kasich and Chris Christie by 33 points each.
-He's in a dead heat with Ted Cruz.
-The only candidate who beats him?
-Ben Carson would lead the businessman by a wide 19 points in a hypothetical head-to-head.
-A bare majority of Donald Trump's supporters say they've made up their minds.
-A new CBS/NYT poll out on Tuesday shows that just more than half of voters who support Trump say they have locked in their votes.
-Obviously, a lot can happen to change that, and no one can really say they would never change their mind.
-46 percent said they are leaving the door open to switching candidates.
-Still, Trump's strongest competition at the moment is from fellow outsider neurosurgeon Ben Carson, but voters who say they have made up their minds are twice as likely to go for Trump.
-Six in 10 Republicans say they agree with Trump on immigration.
-Even since Donald Trump called immigrants from Mexico "rapists" in his campaign announcement speech two months ago, immigration has been front and center in the 2016 conversation.
-Some are worried that Trump's bombast will drive crucial Hispanic voters away from the Republican Party and damage rebranding efforts.
-But according to Monday's new ABC/Post poll, six in 10 Republicans say they agree with Trump on immigration issues.
-So as long as immigration remains in the spotlight, it seems Donald Trump will remain too.
-Frustration with government is climbing to new highs.
-Donald Trump and Ben Carson now account for roughly half of the support from Republican voters, largely due to their outsider status.
-Six in 10 Republicans in Monday's new ABC/Post poll say they want a political outsider over someone with government experience.
-And they are angry at Washington, too.
-A Des Moines Register/Bloomberg poll in Iowa from two weeks ago shows that three in four Iowa Republicans are frustrated with Republicans in Congress, with 54 percent "unsatisfied" and 21 percent "mad as hell."
-Jeremy Corbyn to make debut at Prime Minister's Questions
-Since his election, Mr Corbyn's debut at PMQs has been keenly awaited
-New Labour leader Jeremy Corbyn is to make his debut at Prime Minister's Questions later, taking on David Cameron for the first time.
-Mr Corbyn will rise to ask the first of his six allotted questions shortly after midday, with his performance likely to be closely scrutinised by the media and Labour MPs.
-He has called for "less theatre and more facts" at the weekly showpiece.
-He has also said he could skip some sessions, leaving them to colleagues.
-The encounter will be the first parliamentary test of Mr Corbyn's leadership, coming after his appointment of a shadow cabinet and his speech to the TUC annual congress on Tuesday.
-Meanwhile, the Labour leader's decision to stand in silence during the singing of the national anthem at a service on Tuesday to mark the 75th anniversary of the Battle of Britain has attracted criticism from a number of Tory MPs and is the focus of several front page stories in the newspapers.
-Mr Corbyn's decision not to sing the national anthem has attracted attention
-A spokesman for Mr Corbyn said he had "stood in respectful silence" and did recognise the "heroism of the Royal Air Force in the Battle of Britain."
-But a member of Mr Corbyn's shadow cabinet, Owen Smith, told BBC Two's Newsnight programme he would have advised the Labour leader to sing the national anthem "irrespective" of his belief that the monarchy should be abolished.
-Nearly a dozen shadow ministers have refused to serve in Mr Corbyn's top team, citing differences over the economy, defence and foreign affairs, while less than a sixth of the parliamentary party originally backed him as leader.
-BBC political correspondent Robin Brant says policy differences are also "stacking up" within Labour following Mr Corbyn's appointment over its position on the European Union and the government's cap on benefits.
-Mr Corbyn told the TUC conference Labour was putting forward amendments to remove the whole idea of a cap altogether.
-Hours later Mr Smith, the shadow work and pensions secretary, said the party was "very clear" that it was only opposing government plans to reduce the level of cap from £26,000 to £23,000.
-Mr Corbyn will be the fifth Labour leader that David Cameron has faced across the despatch box over the past decade since he became Tory leader.
-The Labour leader, who has promised a different approach to politics, says he has "crowd sourced" ideas for questions to ask Mr Cameron and has been given more than 30,000 suggestions.
-The Islington North MP has said PMQs is too confrontational and that he will refrain from both "repartee" and trading barbs, instead vowing to focus on serious issues such as poverty, inequality and the challenges facing young people.
-Mr Corbyn has said that Angela Eagle, the shadow business secretary, will deputise for him at PMQs when he does not attend - for instance when Mr Cameron is travelling abroad.
-He has also floated the idea of allowing other colleagues to take the floor on occasion, saying he had approached the Commons Speaker John Bercow to discuss the issue.
-When he became leader in 2005, Mr Cameron said he wanted to move away from the "Punch and Judy" style of politics often associated with PMQs but admitted some years later that he had failed.
-Since it was first televised in 1990, PMQs has been seen as a key barometer of a leader's judgement, their command of the Commons and their standing among their fellow MPs although critics have argued it has become a caricature and is in need of far-reaching reforms.
-'Shot in Joburg': Homeless youth trained as photographers
-Downtown Johannesburg is a tough place to be homeless.
-But one group of former street children have found a way to learn a skill and make a living.
-"I was shot in Joburg" is a non-profit studio that teaches homeless youngsters how to take photographs of their neighbourhood and make a profit from it.
-BBC News went to meet one of the project's first graduates.
-JD Sports boss says higher wages could hurt expansion
-JD Sports Executive Chairman Peter Cowgill says a higher minimum wage for UK workers could mean "more spending power in the pockets of potential consumers."
-But that spending power is unlikely to outweigh the higher labour costs at his firm, he says.
-The costs could hit JD Sports' expansion plans, he added, which could mean fewer extra jobs.
-Thanasi Kokkinakis backed by Tennis Australia president Steve Healy
-Thanasi Kokkinakis deserves kudos rather than criticism for his behaviour.
-Thanasi Kokkinakis has been the collateral damage in the recent storm around his friend Nick Kyrgios and deserves kudos rather than criticism for his own behaviour, according to Tennis Australia president Steve Healy.
@@ -1,100 +0,0 @@
-Șeful ONU declară că nu există soluții militare în Siria
-Secretarul General Ban Ki-moon afirmă că răspunsul său la suportul militar al Rusiei pentru Siria este că „nu există o soluție militară” la conflictul care durează de aproape cinci ani iar mai multe arme nu ar face decât să agraveze violența și suferința a milioane de oameni.
-Șeful ONU a solicitat din nou tuturor părților, inclusiv Consiliului de securitate ONU divizat să se unifice și să susțină negocierile pentru a găsi o soluție politică.
-Ban a declarat miercuri în cadrul unei conferințe că intenționează să se întâlnească luna aceasta cu miniștrii de externe din cinci țări permanent prezente în consiliu - SUA, Rusia, China, Anglia și Franța - pe marginea sesiunii ministeriale a Adunării Generale pentru a discuta despre Siria.
-Ban și-a exprimat regretul că divizările în consiliu și între poporul sirian și puterile regionale „au făcut această situație de nerezolvat”.
-Ban le-a cerut celor cinci membri permanenți să dea dovadă de solidaritatea și unitatea arătate atunci când au reușit să încheie un acord referitor la armele nucleare ale Iranului, abordând astfel criza din Siria.
-8 cifre din sondaje care arată că Donald Trump are șanse reale
-Unii au încercat să îl eticheteze ca politician „flip-flop”.
-Alții l-au numit o glumă.
-Iar alții așteaptă implozia.
-Însă indiferent de modul în care unii republicani încearcă să îl dărâme pe Donald Trump din vârful sondajelor, nu a funcționat (încă).
-Zece din ultimele 11 sondaje naționale au arătat că Donald Trump conduce cu un procent din două cifre iar unele voci încep să se întrebe serios ce înseamnă acest lucru pentru șansele de numire ale mogulului imobiliar.
-Desigur, este încă prematur.
-Nimic din toate acestea nu spune că Trump va câștiga cursa pentru nominalizarea republicanilor.
-Pundits arată că, în aceeași perioadă a anului 2011, avansul lui Rick Perry îi făcea loc lui Herman Cain în sondaje, dar niciunul dintre ei nu a câștigat în vreun stat în cursa de nominalizare.
-Iar motivele pentru care s-ar lupta din greu la alegerile generale sunt numeroase.
-Însă grupurile din exterior precum Super PAC al lui Jeb Bush și grupul conservator economic Club for Growth admit puterea lui Trump și încep să îl susțină cu bani.
-În continuare vă prezentăm câteva cifre din sondaje recente care sugerează că mogulul imobiliar nu este doar ceva trecător:
-Cifrele care indică susținerea față de Trump s-au întors la 180 grade.
-Chiar înainte ca Donald Trump să își anunțe candidatura, la mijlocul lui iunie, un sondaj realizat de Universitatea din Monmouth arăta că doar doi din 10 republicani aveau o părere pozitivă despre mogulul imobiliar.
-Până la mijlocul lui iulie, procentul a urcat la 40%.
-La începutul lui august, era 52%.
-În prezent, șase din 10 republicani au o părere favorabilă despre Donald Trump.
-Aproximativ trei din 10 declară că au o părere negativă.
-Aceste cifre se mențin.
-Un sondaj realizat săptămâna trecută de Quinnipiac în Iowa a concluzionat că 60% dintre republicanii din regiune au o părere favorabilă despre Trump.
-Două treimi dintre alegătorii GOP ar fi fericiți dacă Trump ar câștiga cursa pentru nominalizare.
-Într-un sondaj realizat săptămâna trecută de CNN/ORC, 67% dintre republicani au declarat că ar fi „entuziasmați” sau „mulțumiți” dacă Trump ar câștiga cursa pentru nominalizare.
-Doar doi din 10 declară că ar fi „supărați” dacă Trump ar câștiga cursa pentru nominalizare.
-Doar Ben Carson generează aproximativ același nivel de entuziasm ca Trump (43% declară că ar fi „entuziasmați” față de 40% care declară același lucru despre Trump).
-Cel mai aproape în ceea ce privește entuziasmul?
-Marco Rubio, cu doar 21%.
-De partea cealaltă, 47% dintre alegătorii republicani afirmă că ar fi „nemulțumiți” sau „supărați” dacă favoritul Jeb Bush câștigă cursa pentru nominalizare.
-Majoritatea republicanilor nu consideră temperamentul lui Trump o problemă.
-Deși Donald Trump a fost puternic criticat pentru insultele aduse și stilul său bombastic, 52% dintre alegătorii republicani la nivel național consideră că mogulul imobiliar are temperamentul potrivit pentru a fi președinte, conform sondajului realizat luni de ABC News/Washington Post.
-Regăsim aceleași cifre în statul Iowa, unde tot 52% dintre republicani cred că Trump are personalitatea potrivită pentru a fi conducător, conform sondajului realizat săptămâna trecută de Quinnipiac.
-Totuși, 44% sunt de părere că nu are personalitatea necesară pentru a acționa eficient și aproape șase din 10 independenți afirmă că temperamentul său nu are ce căuta la Casa Albă, conform ABC/Post.
-Alegătorii republicani se obișnuiesc cu ideea.
-Atunci când iau atitudinea de intelectuali, alegătorii republicani consideră că Trump este autentic.
-Conform unui sondaj realizat săptămâna trecută de CNN/ORC, la întrebarea cine are cele mai multe șanse să câștige cursa pentru nominalizare GOP, patru din 10 au declarat că Trump.
-Situația s-a schimbat față de finalul lui iulie, când patru din 10 ar fi pariat pe Jeb Bush.
-Informare completă: în trecut, alegătorii GOP nu au citit foarte bine viitorul.
-În aceeași perioadă a ultimelor alegeri, patru din 10 republicani l-au ales pe Rick Perry în cursa pentru nominalizare, față de doar 28% pentru Mitt Romney.
-Însă, aceste cifre arată că majoritatea alegătorilor GOP consideră plauzibilă campania lui Trump.
-Chiar dacă republicanii sau repliat spre un alt candidat. Trump încă se află în fruntea tuturor.
-Unele voci spun că situația divizată va contribui probabil la victoria lui Trump, în timp ce susținerea contra lui Trump se va împărți la mai mult de doisprezece candidați.
-Însă un sondaj derulat la începutul lui septembrie de Universitatea din Monmouth arată că, în situația ipotetică a unei colaborări între Trump și majoritatea celorlalți candidați republicani, aproape întotdeauna Trump va beneficia de susținerea majoritară.
-Trump se află la distanță de 13 puncte de Carly Fiorina, la 14 puncte de Marco Rubio, la 15 puncte de Walker, la 19 puncte de Jeb Bush și, în cele din urmă, la câte 33 de puncte față de Rand Paul, John Kasich și Chris Christie.
-Este aproape la egalitate cu Ted Cruz.
-Singurul candidat care îl învinge?
-Ben Carson l-ar învinge pe omul de afaceri cu 19 puncte într-o confruntare ipotetică de unu la unu.
-Majoritatea susținătorilor lui Donald Trump declară că s-au decis.
-Un nou sondaj realizat marți de CBS/NYT arată că peste jumătate dintre alegătorii care îl susțin pe Trump declară că nu își schimbă opțiunea de vot.
-Evident, se pot întâmpla multe în acest sens și nimeni nu poate spune că aceștia nu se vor răzgândi niciodată.
-46% afirmă că lasă portița deschisă posibilității de a-și schimba opțiunea.
-Cu toate acestea, cel mai important adversar al lui Trump este în prezent neurochirurgul Ben Carson, însă este de două ori mai probabil ca alegătorii care declară că s-au decis să voteze cu Trump.
-Șase din 10 republicani afirmă că sunt de acord cu Trump în problema imigrării.
-De când Donald Trump i-a numit pe imigranții din Mexic „violatori” în discursul de deschidere a campaniei sale, în urmă cu două luni, imigrarea a fost subiectul central în campania pentru 2016.
-Unii sunt îngrijorați că stilul bombastic al lui Trump va duce la o scindare între alegătorii hispanici importanți și Partidul Republican și va prejudicia eforturile de rebranding.
-Însă, conform sondajului realizat luni de ABC/Post, șase din 10 republicani afirmă că sunt de acord cu Trump în problema imigrării.
-Așa că, se pare că atâta timp cât problema imigrării rămâne în lumina reflectoarelor, la fel va rămâne și Doland Trump.
-Frustrarea față de autorități atinge noi culmi.
-Donald Trump și Ben Carson sunt acum susținuți de aproape jumătate dintre alegătorii republicani, în mare parte datorită statutului lor de outsideri.
-Conform sondajului realizat luni de ABC/Post, șase din 10 republicani afirmă că preferă un outsider politic în detrimentul cuiva cu experiență în guvernare.
-Oamenii sunt de asemenea supărați pe autoritățile de la Washington.
-Un sondaj derulat în urmă cu două săptămâni în Iowa de către Des Moines Register/Bloomberg arată că trei din patru republicani din Iowa sunt frustrați de prestația republicanilor din COngres, 54% declarându-se „nemulțumiți” iar 21% „nervoși la culme”.
-Jeremy Corbyn își face debutul la Prime Minister's Questions
-Încă de la alegerea sa, debutul domnului Corbyn la PMQs a fost îndelung așteptat
-Noul lider al Partidului Laburist, Jeremy Corbyn, își va face mai târziu debutul la Prime Minister's Questions, confruntându-se pentru prima dată cu David Cameron.
-Dl Corbyn va adresa primele dintre cele șase întrebări la care are dreptul la scurt timp după prânz; prestația sa va fi probabil analizată îndeaproape de mass-media și parlamentarii laburiști.
-În cadrul aparițiilor săptămânale, el a cerut „mai puțin teatru și mai multe fapte”.
-A declarat de asemenea că poate renunța la câteva participări și că le cedează colegilor săi.
-Confruntarea va fi primul test parlamentar al Dl Corbyn în poziție de lider, venind după ce a numit un „cabinet fantomă” și după discursul pe care l-a ținut marți la congresul anual TUC.
-Între timp, decizia liderului Partidului laburist de a păstra tăcerea la rostirea imnului național în cadrul unei slujbe ținute marți cu ocazia aniversării a 75 de ani de la Bătălia Angliei a atras critici din partea unor parlamentari conservatori și a ținut prima pagină a ziarelor.
-Decizia domnului Corbyn de a nu cânta imnul național a atras atenția
-Un purtător de cuvânt al Dl Corbyn a declarat că acesta „a păstrat tăcerea în mod respectuos” și a recunoscut „eroismul Forțelor aeriene britanice în Bătălia Angliei.”
-Însă un membru al cabinetului fantomă al Dl Corbyn, Owen Smith, a declarat pentru emisiunea Two's Newsnight transmisă de BBC că i-ar fi recomandat liderului laburist să cânte imnul național „indiferent” de credința sa că monarhia ar trebui abolită.
-În jur de doisprezece miniștri din cabinetul fantomă au refuzat să facă parte din echipa de frunte a Dl Corbyn, argumentând prin diferențe de opinie legate de economie, apărare și externe, în timp ce mai puțin de o șesime din partidul parlamentar l-a susținut ca lider.
-Corespondentul politic al BBC, Robin Brant, declară că diferențele de politică „se cumulează” în Partidul Laburist după numirea domnului Corbyn referitor la poziția sa față de Uniunea Europeană și limita de beneficii.
-Dl Corbyn a declarat la conferința TUC că Partidul Laburist va aduce modificări prin care se va elimina integral ideea limitării.
-Câteva ore mai târziu, Dl Smith, Ministrul Muncii și Pensiilor, a declarat că partidul „este foarte clar” în opoziția exclusivă față de planurile guvernului de a reduce nivelul „cap” de la 26.000 lire la 23.000 lire.
-Dl Corbyn va fi al cincilea lider laburist cu care se confruntă David Cameron la tribună în ultimul deceniu, de când a preluat conducerea Partidului Conservator.
-Liderul laburist, care a promis o abordare diferită a politicii, spune că are idei „din surse externe” pentru întrebări pe care să i le adreseze Domnului Cameron și că a primit peste 30.000 de sugestii.
-Parlamentarul Islington North a afirmat că PMQs implică un nivel de confruntare prea înalt și că se va abține de la replici și atacuri, angajându-se să se concentreze în schimb pe probleme serioase precum sărăcia, inegalitatea și provocările cu care se confruntă tinerii.
-Dl Corbyn a declarat că Angela Eagle, Ministrul de finanțe, îi va ține locul la PMQs atunci când el nu poate participa - de exemplu atunci când Dl Cameron se deplasează în străinătate.
-A exprimat de asemenea ideea că va permite altor colegi să ia cuvântul ocazional, spunând că l-a abordat pe Președintele Camerei Deputaților, John Bercow, pentru a discuta acest aspect.
-În 2005, când a preluat conducerea, Dl Cameron a declarat că dorește să renunțe la stilul politic „Punch and Judy” asociat adesea cu PMQs însă a recunoscut câțiva ani mai târziu că nu a reușit în demersul său.
-De la prima transmisie, în 1990, PMQs a fost considerată un barometru cheie al raționamentului unui lider, al modului în care acesta conduce Camera Deputaților și a poziției sale în rândul colegilor parlamentari, deși criticii afirmă a ca devenit o caricatură și că are nevoie de o reformare profundă.
-„Cadru în Joburg”: Tineri fără adăpost beneficiază de cursuri de fotografie
-Este dificil să fii un om fără adăpost în Johannesburg.
-Însă un grup de oameni care au trăit pe străzi în copilărie au găsit un mod de a învăța o meserie și de a-și câștiga traiul.
-„I was shot în Joburg” este un studio non-profit care îi învață pe tinerii fără adăpost să facă fotografii ale zonelor în care trăiesc și să câștige bani din asta.
-BBC News s-a întâlnit cu unul dintre primii absolvenți ai proiectului.
-Șeful JD Sports spune că salariile mai mari ar putea dăuna extinderii
-Președintele JD Sports, Peter Cowgill, declară că o creștere a salariului minim în Marea Britanie ar putea însemna „o putere de cumpărare mai mare în buzunarele potențialilor consumatori.”
-Este însă puțin probabil ca respectiva putere de cumpărare să depășească costurile mai mari pentru forța de muncă în cadrul firmei, afirmă el.
-Costurile ar putea avea impact asupra planurilor de extindere ale JD Sports, a adăugat el, ceea ce ar putea însemna mai puține locuri de muncă noi.
-Thanasi Kokkinakis susținut de președintele Tennis Australia, Steve Healy
-Thanasi Kokkinakis ar merita să fie lăudat și nu criticat pentru comportamentul său.
-Thanasi Kokkinakis a fost victimă colaterală în „furtuna” creată în jurul prietenului său, Nick Kyrgios, iar comportamentul său merită mai degrabă cuvinte de laudă și nu critică, în opinia președintelui Tennis Australia, Steve Healy.
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-Membership of Parliament: see Minutes
-Approval of Minutes of previous sitting: see Minutes
-Membership of Parliament: see Minutes
-Verification of credentials: see Minutes
-Documents received: see Minutes
-Written statements and oral questions (tabling): see Minutes
-Petitions: see Minutes
-Texts of agreements forwarded by the Council: see Minutes
-Action taken on Parliament's resolutions: see Minutes
-Agenda for next sitting: see Minutes
-Closure of sitting
-(The sitting was closed at 7.45 p.m.)
-Election of Vice-Presidents of the European Parliament (deadline for submitting nominations): see Minutes
-(The sitting was suspended at 12.40 p.m. and resumed at 3.00 p.m.)
-Election of Quaestors of the European Parliament (deadline for submitting nominations): see Minutes
-(The sitting was suspended at 3.25 p.m. and resumed at 6.00 p.m.)
-Agenda for next sitting: see Minutes
-Closure of sitting
-(The sitting was closed at 6.15 p.m.)
-Opening of the sitting
-(The sitting was opened at 9.35 a.m.)
-Documents received: see Minutes
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-(The sitting was suspended at 7 p.m. and resumed at 9 p.m.)
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-Closure of sitting
-(The sitting was suspended at 23.25 p.m.)
-Documents received: see Minutes
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-(The sitting was suspended at 11.35 a.m. and resumed for voting time at noon)
-Approval of Minutes of previous sitting: see Minutes
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-1.
-Membership of committees (vote)
-2.
-Amendment of the ACP-EC Partnership Agreement (vote)
-4.
-Certification of train drivers operating locomotives and trains on the railway system in the Community (vote)
-6.
-Law applicable to non-contractual obligations ("ROME II") (vote)
-8.
-Seventh and eighth annual reports on arms exports (vote)
-Corrections to votes and voting intentions: see Minutes
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-Adjournment of the session
-I declare the session of the European Parliament adjourned.
-(The sitting was closed at 1 p.m.)
-Approval of Minutes of previous sitting: see Minutes
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-1.
-Appointments to parliamentary committees (vote): see Minutes
-Voting time
-Agenda for next sitting: see Minutes
-Closure of sitting
-(The sitting was closed at 12 midnight)
-Opening of the sitting
-(The sitting was opened at 09.05)
-Documents received: see Minutes
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-1.
-Protection of passengers against displaced luggage (vote)
-2.
-Approval of motor vehicles with regard to the forward field of vision of the driver (vote)
-3.
-EC-Korea Agreement on scientific and technological cooperation (vote)
-4.
-Mainstreaming sustainability in development cooperation policies (vote)
-5.
-Draft Amending Budget No 1/2007 (vote)
-7.
-EC-Gabon Fisheries Partnership (vote)
-10.
-Limitation periods in cross-border disputes involving personal injuries and fatal accidents (vote)
-12.
-Strategy for a strengthened partnership with the Pacific Islands (vote)
-13.
-The European private company statute (vote)
-That concludes the vote.
-Corrections to votes and voting intentions: see Minutes
-Assignment conferred on a Member: see Minutes
-Membership of committees and delegations: see Minutes
-Decisions concerning certain documents: see Minutes
-Forwarding of texts adopted during the sitting: see Minutes
-Dates for next sittings: see Minutes
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-Componenţa Parlamentului: a se vedea procesul-verbal
-Aprobarea procesului-verbal al şedinţei precedente: a se vedea procesul-verbal
-Componenţa Parlamentului: a se vedea procesul-verbal
-Verificarea prerogativelor: a se vedea procesul-verbal
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-Petiţii: a se vedea procesul-verbal
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-Ridicarea şedinţei
-(Se levanta la sesión a las 19.45 horas)
-Alegerea vicepreşedinţilor Parlamentului European (termenul de depunere a candidaturilor): consultaţi procesul-verbal
-(Die Sitzung wird um 12.40 Uhr unterbrochen und um 15.00 Uhr wiederaufgenommen).
-Alegerea chestorilor Parlamentului European (termenul de depunere a candidaturilor): consultaţi procesul-verbal
-(Die Sitzung wird um 15.25 Uhr unterbrochen und um 18.00 Uhr wiederaufgenommen).
-Ordinea de zi a următoarei şedinţe: a se vedea procesul-verbal
-Ridicarea şedinţei
-(Die Sitzung wird um 18.15 Uhr geschlossen.)
-Deschiderea şedinţei
-(Die Sitzung wird um 9.35 Uhr eröffnet.)
-Depunerea documentelor: a se vedea procesul-verbal
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-(La seduta, sospesa alle 19.00, è ripresa alle 21.00)
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-(Die Sitzung wird um 23.25 Uhr geschlossen.)
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-(La séance, suspendue à 11h35 dans l'attente de l'Heure des votes, est reprise à midi)
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-1.
-Componenţa comisiilor (vot)
-2.
-Modificarea Acordului de parteneriat ACP-CE ("Acordul de la Cotonou”) (vot)
-4.
-Certificarea mecanicilor de locomotivă care conduc locomotive şi trenuri în sistemul feroviar comunitar (vot)
-6.
-Legea aplicabilă obligaţiilor necontractuale ("Roma II”) (vot)
-8.
-Al şaptelea şi al optulea raport anual privind exportul de armament (vot)
-Corectările voturilor şi intenţiile de vot: a se vedea procesul-verbal
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-Întreruperea sesiunii
-Dichiaro interrotta la sessione del Parlamento europeo.
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-1.
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-Timpul afectat votului
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-1.
-Protecţia pasagerilor împotriva deplasării bagajelor (vot)
-2.
-Omologarea vehiculelor cu motor cu privire la câmpul de vizibilitate înainte al conducătorului auto (vot)
-3.
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-4.
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-5.
-Proiect de buget rectificativ nr.1/2007 (vot)
-7.
-Acordul de parteneriat în domeniul pescuitului între Comunitatea Europeană şi Republica Gaboneză (vot)
-10.
-Termenele de prescripţie aplicabile în cadrul litigiilor transfrontaliere cu privire la vătămările corporale şi accidentele mortale (vot)
-12.
-Relaţiile UE cu insulele din Pacific: Strategie pentru un parteneriat consolidat (vot)
-13.
-Statutul societăţii private europene (vot)
-Damit ist die Abstimmungsstunde beendet.
-Corectările voturilor şi intenţiile de vot: a se vedea procesul-verbal
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-Calendarul următoarelor şedinţe: a se vedea procesul-verbal
@@ -1,100 +0,0 @@
-Brazil's Former Presidential Chief-of-Staff to Stand Trial
-A federal judge on Tuesday accepted the charges filed against Brazil's former presidential chief of staff for his alleged involvement in a massive corruption scheme at state-owned oil company Petrobras.
-The federal prosecutor's office said Jose Dirceu will face trial on the corruption, racketeering and money laundering charges filed earlier this month.
-Fourteen other people will also be tried, including Joao Vaccari Neto, the former treasurer of Brazil's governing Workers' Party and Renato de Souza Duque, Petrobras' former head of corporate services.
-Dirceu is the most senior member of the ruling Workers' Party to be taken into custody in connection with the scheme.
-Dirceu served as former President Luiz Inacio Lula da Silva's chief of staff between 2003 and 2005.
-He was arrested early August in his home, where he already was under house arrest serving an 11-year sentence for his involvement in a cash-for-votes scheme in Congress more than 10 years ago.
-Prosecutors have said that Dirceu masterminded the kickback scheme at Petrobras, accepted bribes while in office and continued to receive payments from contractors after he was jailed in late 2013 for the vote-buying scandal.
-According to prosecutors, the scheme at Petrobras involved roughly $2 billion in bribes and other illegal funds.
-Some of that money was allegedly funneled back to campaign coffers of the ruling party and its allies.
-It also allegedly included the payment of bribes to Petrobras executives in return for inflated contracts.
-'Miraculous' recovery for Peshawar massacre schoolboy
-A teenager paralysed after being shot four times in Pakistan's deadliest terror attack has made a "miraculous" recovery following treatment in the UK.
-Muhammad Ibrahim Khan, 13, had been told by doctors in Pakistan that he would never walk again.
-At least 140 people, mostly children, were killed when gunmen stormed Peshawar's Army Public School last December.
-Muhammad, who arrived in London last month for surgery, is being discharged from hospital later.
-Exactly nine months ago, on an ordinary Tuesday morning, Muhammad sat in his first aid class listening to his teachers intently.
-At the same time seven gunmen disguised in security uniforms were entering the Army Public School.
-They were strapped with explosives and had one simple mission in mind: Kill every man, woman and child they came across.
-"I can't forget what happened that day," Muhammad says with a severe stare.
-We were sitting in the auditorium, we were asking questions... and then we heard heavy gunfire outside.
-The terrorists moved inside and they started killing - our teacher was burned alive.
-Muhammad described pulling four other pupils out of the auditorium as the carnage unfolded.
-He said he then heard his friend, Hamza calling to him.
-He said, 'oh brother save me'.
-I held his hand.
-That's when I was shot in the back, and he was shot in the head.
-Most of the people killed in the attack were pupils
-Hamza died in Muhammad's arms.
-Muhammad recalled blacking out after that, and the next thing he knew he was in a hospital bed, paralysed from the waist down.
-Doctors in Peshawar in northern Pakistan, and then Rawalpindi, close to the capital, told his family there was no treatment, and he would never walk again.
-"Seeing him I felt like my soul had left my body," says Muhammad's father, Sher Khan
-Those nine months were the hardest in my life.
-But Mr Khan and his wife, Sherbano, refused to believe that their cricket-mad son would never be able to use his legs again.
-They campaigned, and appealed for help on Pakistani TV, gaining the support of high profile people such as cricketer turned politician Imran Khan.
-Finally, they were able to raise the funds to bring Muhammad to the UK and provide him with treatment at London's private Harley Street Clinic.
-Consultant neurosurgeon Irfan Malik described Muhammad as "terrified" when he first arrived at the hospital.
-"He'd spent the last [few] months lying on a bed, unable to move side to side," says Mr Malik.
-He was weak, he had a pressure sore on his back.
-He wasn't in great shape.
-A vertebra at the base of Muhammad's spine was destroyed
-Muhammad was shot in his shoulder, his hip, and his back during the attack, damaging his lower spine - leading to paralysis.
-But during six hours of surgery, Mr Malik and his team were able to reattach nerve endings and reconstruct the damaged part of the spine.
-Even Mr Malik was surprised at what happened next.
-Exactly one week after the surgery Muhammad stood up and started taking steps and walking.
-We were not expecting to get that sort of excellent result.
-That was miraculous," he says.
-Less than two weeks after his operation, Muhammad is ready to leave hospital and start the long road to recovery.
-Muhammad has defied the odds and started to walk again
-He says he wants to build his strength and continue his education in the UK.
-But he says he is determined to return to Pakistan, join the army and help fight terrorism.
-"I feel like I have a second chance at life," he says as he shows off pictures he's drawn of guns scribbled out next to school books and pens
-Muhammad grows physically stronger every day but the psychological trauma he continues to endure is unimaginable.
-"My anger is not diminishing" he says.
-In my school little kids were killed.
-What was their crime?
-His mother, wiping a tear from her eye, caressed his head and said: "I can see my son walking again."
-He'll be able to get on with his normal life.
-'Super Voice' 4G service from Three offers better signal
-Three is making use of a lower frequency 4G spectrum that can travel more widely
-Mobile phone provider Three has launched a UK service it says will improve reception inside buildings and in rural black spots.
-Its 4G Super Voice enables customers to make calls and send texts using a lower frequency spectrum.
-Other networks are looking into introducing the technology, known as Voice Over Long-Term Evolution (VoLTE).
-It currently works on only the Samsung Galaxy S5, but recent iPhone handsets will be added in the coming months.
-Three said up to 5.5 million customers would have access to the service by 2017.
-Chief technology officer Bryn Jones said: "By the end of the year, one million of our customers will have access to better indoor coverage and be able to use their phones in more places than ever before."
-Stars prepare for panto season
-Pantomime season is big business for theatres up and down the UK, with many getting ready for this year's season now.
-Some of the biggest names in showbusiness now take part in the yuletide theatre.
-Matthew Kelly and Hayley Mills will be appearing in Cinderella - one as an ugly sister, the other as fairy godmother.
-They reveal their panto secrets to BBC Breakfast.
-Steven Wilson: 'If I don't do anything, I feel this creeping guilt'
-Steven Wilson was recently the big winner at the Progressive Music Awards
-Steven Wilson is often dubbed the hardest working musician in the world of progressive rock.
-The multi-talented musician won three prizes at this month's Progressive Music Awards in London, including album of the year for Hand.
-The Guardian's five-star review called it "a smart, soulful and immersive work of art."
-Since the 1980s, Wilson has been the driving force in a number of musical projects, the best known of which is the rock band Porcupine Tree.
-Now, ahead of two sell-out shows at the Royal Albert Hall, Wilson is releasing a vinyl-only double LP, Transience, to showcase the "more accessible" side of his solo output.
-He tells the BBC about his love of vinyl, his busy schedule and explains how comic actor Matt Berry came to be his support act.
-What does vinyl mean to you?
-I grew up at the very tail end of the vinyl era, and at the time, I remember, we couldn't wait for CD to come along because vinyl was so frustrating.
-You would buy the record, take it home, and it would have a scratch, and you would have to take it back again.
-I love CDs, and for some kinds of music - classical for example - it is better than vinyl.
-But the problem with the CD and digital downloads is that there's nothing you can really cherish or treasure.
-Owning vinyl is like having a beautiful painting hanging in your living room.
-It's something you can hold, pore over the lyrics and immerse yourself in the art work.
-I thought it was just a nostalgic thing, but it can't be if kids too young to remember vinyl are enjoying that kind of experience.
-Do you have a piece of vinyl that you treasure?
-The truth is I got rid of 100% of my vinyl in the 90s.
-All the vinyl I have is re-bought.
-I started off from the perspective that I wanted to recreate the collection I had when I was 15, but it's gone beyond that.
-The first record which I persuaded my parents to buy for me was Electric Light Orchestra's Out of the Blue.
-If I still had my original copy, it would have sentimental value, but, alas, it's in a charity shop somewhere.
-Steven Wilson hopes the album will be a doorway for potential new fans
-Why release your new compilation Transience on vinyl?
-It was originally conceived as an idea for Record Store Day, but we missed the boat on that.
-My record company had suggested I put together some of my shorter, more accessible songs.
-I got a bit obsessed by the idea to make something like "an introduction to Steven Wilson," and I was committed to it being a vinyl-only release.
-Anyone who buys the vinyl does also get a high-resolution download.
-Do you have a concern that the album won't show your work in a true light?
@@ -1,100 +0,0 @@
-Fostul șef al cabinetului prezidențial brazilian este adus în fața instanței
-Marți, un judecător federal a acceptat acuzațiile aduse împotriva fostului șef al cabinetului prezidențial brazilian pentru presupusa implicare a acestuia într-o schemă masivă de corupție privind compania petrolieră de stat Petrobras.
-Biroul procurorului federal a declarat că Jose Dirceu va fi trimis în judecată pentru acuzațiile de corupție, înșelătorie și spălare de bani aduse în această lună.
-Alte paisprezece persoane vor fi judecate, printre acestea numărându-se Joao Vaccari Neto, fostul trezorier al Partidului Muncitorilor, aflat la putere în Brazilia, și Renato de Souza Duque, fostul președinte al serviciilor pentru întreprinderi ale Petrobras.
-Dirceu este cel mai vechi membru al Partidului Muncitorilor aflat la guvernare luat în custodie pentru legăturile cu această schemă.
-Dirceu a servit ca șef de cabinet al fostului președinte Luiz Inacio Lula da Silva între 2003 și 2005.
-A fost arestat la începutul lui august de acasă, unde deja se afla sub arest la domiciliu, cu o pedeapsă de 11 ani pentru implicarea într-o schemă de cumpărare a voturilor în Congres cu peste 10 ani în urmă.
-Procurorii au declarat că Dirceu a dezvoltat schema de luare de mită de la Petrobras, a acceptat mită în timp ce se afla în funcție și a continuat să primească plăți de la antreprenori după ce a fost închis la sfârșitul lui 2013 pentru scandalul voturilor cumpărate.
-Conform procurorilor, schema de la Petrobras a implicat aproximativ 2 miliarde de dolari sub formă de mită și alte fonduri ilegale.
-O parte din acei bani s-ar fi întors în fondul de campanie al partidului aflat la guvernare și al aliaților acestora.
-De asemenea, ar fi inclus mită către directorii Petrobras în schimbul unor contracte umflate.
-Recuperarea „miraculoasă” a unui elev supraviețuitor al masacrului de la Peshawar
-Un adolescent paralizat după ce fusese împușcat de patru ori în cel mai cumplit atac terorist din Pakistan a reușit o recuperare „miraculoasă” după ce a urmat un tratament în Regatul Unit.
-Lui Mohamed Ibrahim Khan, în vârstă de 13 ani, doctorii din Pakistan îi spuseseră că nu va mai putea să meargă niciodată.
-Cel puțin 140 de persoane, majoritatea copii, au fost ucise când bărbați înarmați au atacat școala publică a armatei din Peshawar în luna decembrie a anului trecut.
-Mohamed, care a sosit la Londra luna trecută pentru operație, va fi externat mai târziu din spital.
-Exact cu nouă luni în urmă, într-o dimineață obișnuită de marți, Mohamed stătea la ora de primul ajutor și își asculta atent profesorii.
-Chiar atunci, șapte bărbați înarmați deghizați în uniformele agenților de pază intrau în școala publică a armatei.
-Purtau centuri cu explozivi și aveau de îndeplinit o misiune simplă: să îi ucidă pe toți bărbații, femeile și copiii care le ieșeau în cale.
-„Nu pot uita ce s-a întâmplat în acea zi”, spune Mohamed cu o privire aspră.
-Stăteam în amfiteatru, puneam întrebări... apoi am auzit focuri de armă afară.
-Teroriștii au intrat înăuntru și au început să ucidă. Profesorul nostru a fost ars de viu.
-Mohamed descrie cum a scos patru elevi din amfiteatru în timp ce se desfășura carnagiul.
-Apoi spune că și-a auzit prietenul, pe Hamza, strigându-l.
-Spunea „oh, frate, salvează-mă”.
-L-am ținut de mână.
-Atunci eu am fost împușcat în spate, iar el în cap.
-Cei mai mulți dintre cei uciși în atac erau elevi
-Hamza a murit în brațele lui Mohamed.
-Mohamed își amintește că imediat după asta a leșinat și că următorul lucru pe care l-a știut a fost că se afla pe un pat de spital, paralizat de la brâu în jos.
-Doctorii din Peshawar din nordul Pakistanului, apoi cei din Rawalpindi, aproape de capitală, i-au spus familiei sale că nu exista tratament și că nu va mai putea merge niciodată.
-„Când l-am văzut, am simțit cum îmi iese sufletul”, spune Sher Khan, tatăl lui Mohamed.
-Acele nouă luni au fost cele mai grele din viața mea.
-Însă Khan și soția lui, Sherbano, au refuzat să creadă că fiul lor atât de pasionat de crichet nu-și va mai putea folosi vreodată picioarele.
-Au făcut o campanie și au cerut ajutor de la televiziunea pakistaneză, atrăgând sprijinul unor oameni faimoși precum Imran Khan, jucător de crichet devenit politician.
-Într-un final, au reușit să strângă fonduri pentru a-l duce pe Mohamed în Regatul Unit și a-i oferi tratament la clinica privată Harley Street din Londra.
-Neurochirurgul consultant Irfan Malik l-a descris pe Mohamed drept „înspăimântat” când acesta a ajuns la spital.
-„Își petrecuse ultimele [câteva] luni zăcând în pat, fără să se poată mișca de pe o parte pe alta, spune Malik.
-Era slăbit, se pusese multă presiune pe spatele lui.
-Nu era într-o formă prea bună.
-O vertebră de la baza coloanei vertebrale a lui Mohamed fusese distrusă
-Mohamed fusese împușcat în umăr, în șold și în spate în timpul atacului, iar coloana vertebrală inferioară îi fusese distrusă, ducând la paralizie.
-Însă, în timpul unei operații care a durat șase ore, Malik și echipa lui au reușit să lege din nou terminațiile nervoase și să reconstruiască partea distrusă a coloanei.
-Chiar și Malik a fost surprins de ceea ce s-a întâmplat în continuare.
-Exact la o săptămână după operație, Mohamed s-a ridicat și a început să facă pași și să meargă.
-Nu ne așteptam la un rezultat atât de bun.
-A fost un miracol”, spune acesta.
-În mai puțin de două săptămâni de la operație, Mohamed este gata să părăsească spitalul și să înceapă procesul lung de recuperare.
-Mohamed a sfidat soarta și a început să meargă din nou
-Vrea să devină puternic și să își continue studiile în Regatul Unit.
-Însă este hotărât să revină în Pakistan, să se înroleze în armată și să lupte împotriva terorismului.
-„Simt că am încă o șansă la viață” spune el, arătând imaginile cu arme desenate de el lângă manuale școlare și stilouri
-Fizic, Mohamed devine tot mai puternic în fiecare zi, însă trauma psihologică prin care trece și acum este de neimaginat.
-„Furia mea nu a scăzut”, mărturisește el.
-În școala mea au fost uciși copii mici.
-Ce crimă au comis ei?
-Mama lui își șterge o lacrimă, îl mângâie pe creștet și spune: „Îmi văd fiul mergând din nou”.
-Va putea să-și continue firesc viața.
-Serviciul 4G „Super Voice” de la Three oferă semnal mai bun
-Three folosește un spectru 4G cu o frecvență mai joasă, care poate acoperi o zonă mai extinsă
-Furnizorul de telefonie mobilă Three a lansat în Regatul Unit un serviciu despre care spune că va îmbunătăți recepția în interiorul clădirilor și în zonele rurale fără semnal.
-Serviciul 4G Super Voice le permite clienților să efectueze apeluri și să trimită mesaje text folosind un spectru cu o frecvență mai joasă.
-Și alte rețele intenționează să introducă aceeași tehnologie, cunoscută ca „Voice Over Long-Term Evolution (VoLTE)”.
-Aceasta funcționează momentan doar cu Samsung Galaxy S5, însă telefoanele iPhone recente vor beneficia de ea în lunile următoare.
-Three menționează că până la 5,5 milioane de clienți vor avea acces la serviciu până în 2017.
-Responsabilul șef pentru tehnologie, Bryn Jones a declarat: „Până la sfârșitul anului, un milion dintre clienții noștri vor avea acces la o acoperire mai bună în interior și își vor putea folosi telefoanele în mai multe locuri ca până acum”.
-Vedetele se pregătesc pentru stagiunea de pantomimă
-Stagiunea de pantomimă este foarte importantă pentru teatrele din tot Regatul Unit, multe dintre ele pregătindu-se acum pentru stagiunea din acest an.
-Acum, la teatrul de Crăciun participă unele dintre numele cele mai mari din showbusiness.
-Matthew Kelly și Hayley Mills vor apărea în Cenușăreasa - primul în rolul uneia dintre surorile rele, iar a doua în rolul zânei.
-Aceștia dezvăluie secretele pantomimei lor la BBC Breakfast.
-Steven Wilson: „Dacă nu fac nimic, mă simt vinovat”
-Steven Wilson a fost desemnat recent drept marele câștigător al Progressive Music Awards
-Steven Wilson a fost numit de multe ori drept cel mai muncitor muzician din lumea rockului progresiv.
-Talentatul muzician a câștigat trei premii la Progressive Music Awards, care a avut loc luna aceasta la Londra, printre care și premiul pentru cel mai bun album al anului pentru Hand.
-În recenzia sa de cinci stele, The Guardian a numit albumul „o operă de artă inteligentă, expresivă și captivantă”.
-Încă din anii 1980, Wilson este motorul mai multor proiecte muzicale, cel mai cunoscut dintre acestea fiind trupa de rock Porcupine Tree.
-Acum, înainte de două spectacole cu casa închisă la Royal Albert Hall, Wilson lansează un dublu LP doar în format vinil, Transience, pentru a arăta latura „mai accesibilă” a activității sale solo.
-A povestit pentru BBC despre dragostea lui pentru viniluri și despre programul său încărcat și a explicat cum a ajuns actorul de comedie Matt Berry să îi deschidă spectacolele.
-Ce înseamnă vinil pentru tine?
-Am crescut chiar în perioada de sfârșit a erei vinilurilor și îmi amintesc că atunci abia așteptam apariția CD-ului, căci vinilul era atât de enervant.
-Cumpărai un disc, mergeai cu el acasă, avea o zgârietură și trebuia să îl aduci înapoi.
-Iubesc CD-urile, iar pentru anumite tipuri de muzică, de exemplu cea clasică, sunt mai bune decât vinilurile.
-Însă problema cu CD-urile și cu descărcările digitale este aceea că nu mai există nimic pe care să îl prețuiești cu adevărat.
-Să ai un vinil e ca și cum ai avea un tablou frumos agățat în sufragerie.
-E ceva ce poți ține în mână, în timp ce te lași absorbit de versuri și copleșit de actul artistic.
-Am crezut că e doar o chestie nostalgică, însă nu are cum să fie așa dacă unor puști prea tineri să-și amintească de viniluri le place acest gen de experiență.
-Ai vreun vinil la care ții în mod special?
-Recunosc că am scăpat de toate vinilurile în anii '90.
-Toate vinilurile pe care le am sunt cumpărate din nou.
-Am pornit de la ideea de a reface colecția pe care o aveam la 15 ani, însă am trecut de limita aceea.
-Primul disc pe care mi-am convins părinții să mi-l cumpere a fost Out of the Blue de la Electric Light Orchestra.
-Dacă aș mai fi avut încă exemplarul inițial, acesta ar fi avut valoare sentimentală, însă, din păcate, se află pe undeva printr-un magazin de caritate.
-Steven Wilson speră că albumul va fi o poartă către posibili fani noi
-De ce ți-ai lansat noua compilație Transience pe vinil?
-Aceasta a fost concepută inițial ca idee pentru Ziua magazinelor de discuri, însă am ratat ocazia.
-Casa mea de discuri sugerase să adun câteva dintre melodiile mele mai scurte și mai accesibile.
-Am ajuns să fiu ușor obsedat de ideea de a face ceva gen „introducere în muzica lui Steven Wilson” și am ținut neapărat ca proiectul să fie lansat doar pe vinil.
-Cine cumpără vinilul primește, de asemenea, și o variantă descărcată la rezoluție înaltă.
-Ești îngrijorat că albumul nu va arăta muzica ta în adevărata ei lumină?
@@ -1,352 +0,0 @@
-import argparse
-import logging
-import os
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-from unittest.mock import patch
-
-import pytest
-import torch
-from pytest import param
-from torch.utils.data import DataLoader
-
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, MBartTokenizer
-from transformers.testing_utils import require_multigpu
-
-from .distillation import distill_main, evaluate_checkpoint
-from .finetune import main
-from .pack_dataset import pack_data_dir
-from .run_eval import generate_summaries_or_translations, run_generate
-from .utils import MBartDataset, Seq2SeqDataset, label_smoothed_nll_loss, lmap, load_json
-
-
-logging.basicConfig(level=logging.DEBUG)
-
-logger = logging.getLogger()
-CUDA_AVAILABLE = torch.cuda.is_available()
-CHEAP_ARGS = {
-    "label_smoothing": 0.2,
-    "early_stopping_patience": 2,
-    "logger_name": "default",
-    "length_penalty": 0.5,
-    "cache_dir": "",
-    "task": "summarization",
-    "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,
-    "output_dir": "",
-    "fp16": CUDA_AVAILABLE,
-    "no_teacher": False,
-    "fp16_opt_level": "O1",
-    "gpus": 1 if CUDA_AVAILABLE else 0,
-    "n_tpu_cores": 0,
-    "max_grad_norm": 1.0,
-    "do_train": True,
-    "do_predict": True,
-    "accumulate_grad_batches": 1,
-    "server_ip": "",
-    "server_port": "",
-    "seed": 42,
-    "model_name_or_path": "sshleifer/bart-tiny-random",
-    "config_name": "",
-    "tokenizer_name": "facebook/bart-large",
-    "do_lower_case": False,
-    "learning_rate": 0.3,
-    "weight_decay": 0.0,
-    "adam_epsilon": 1e-08,
-    "warmup_steps": 0,
-    "max_epochs": 1,
-    "train_batch_size": 2,
-    "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,
-}
-
-
-def _dump_articles(path: Path, articles: list):
-    content = "\n".join(articles)
-    Path(path).open("w").writelines(content)
-
-
-ARTICLES = [" Sam ate lunch today.", "Sams lunch ingredients."]
-SUMMARIES = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
-T5_TINY = "patrickvonplaten/t5-tiny-random"
-BART_TINY = "sshleifer/bart-tiny-random"
-MBART_TINY = "sshleifer/tiny-mbart"
-MARIAN_TINY = "sshleifer/tiny-marian-en-de"
-stream_handler = logging.StreamHandler(sys.stdout)
-logger.addHandler(stream_handler)
-logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
-
-
-def make_test_data_dir(**kwargs):
-    tmp_dir = Path(tempfile.mkdtemp(**kwargs))
-    for split in ["train", "val", "test"]:
-        _dump_articles((tmp_dir / f"{split}.source"), ARTICLES)
-        _dump_articles((tmp_dir / f"{split}.target"), SUMMARIES)
-    return tmp_dir
-
-
-class TestSummarizationDistiller(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
-        return cls
-
-    @require_multigpu
-    def test_multigpu(self):
-        updates = dict(no_teacher=True, freeze_encoder=True, gpus=2, sortish_sampler=False,)
-        self._test_distiller_cli(updates)
-
-    def test_distill_no_teacher(self):
-        updates = dict(student_encoder_layers=2, student_decoder_layers=1, no_teacher=True)
-        self._test_distiller_cli(updates)
-
-    def test_distill_checkpointing_with_teacher(self):
-        updates = dict(
-            student_encoder_layers=2,
-            student_decoder_layers=1,
-            max_epochs=4,
-            val_check_interval=0.25,
-            alpha_hid=2.0,
-            model_name_or_path="IGNORE_THIS_IT_DOESNT_GET_USED",
-        )
-        model = self._test_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), 2)
-        examples = lmap(str.strip, model.hparams.data_dir.joinpath("test.source").open().readlines())
-        out_path = tempfile.mktemp()
-        generate_summaries_or_translations(examples, out_path, str(model.output_dir / "best_tfmr"))
-        self.assertTrue(Path(out_path).exists())
-
-        evaluate_checkpoint(ckpts[0], dest_dir=Path(tempfile.mkdtemp()))
-
-    def test_loss_fn(self):
-        model = AutoModelForSeq2SeqLM.from_pretrained(BART_TINY)
-        input_ids, mask = model.dummy_inputs["input_ids"], model.dummy_inputs["attention_mask"]
-        target_ids = torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]], dtype=torch.long, device=model.device)
-        decoder_input_ids = target_ids[:, :-1].contiguous()  # Why this line?
-        lm_labels = target_ids[:, 1:].clone()  # why clone?
-        model_computed_loss = model(
-            input_ids, attention_mask=mask, decoder_input_ids=decoder_input_ids, labels=lm_labels, use_cache=False
-        ).loss
-
-        logits = model(input_ids, attention_mask=mask, decoder_input_ids=decoder_input_ids, use_cache=False).logits
-
-        lprobs = torch.nn.functional.log_softmax(logits, dim=-1)
-        smoothed_loss, nll_loss = label_smoothed_nll_loss(
-            lprobs, lm_labels, 0.1, ignore_index=model.config.pad_token_id
-        )
-        with self.assertRaises(AssertionError):
-            # TODO: understand why this breaks
-            self.assertEqual(nll_loss, model_computed_loss)
-
-    @unittest.skip("T5 distillation is broken at the moment")
-    def test_distill_t5(self):
-        updates = dict(
-            student_encoder_layers=1,
-            student_decoder_layers=1,
-            alpha_hid=2.0,
-            teacher=T5_TINY,
-            model_name_or_path=T5_TINY,
-            tokenizer_name=T5_TINY,
-        )
-        self._test_distiller_cli(updates)
-
-    def _test_distiller_cli(self, updates, check_contents=True):
-        default_updates = dict(
-            label_smoothing_eps=0.0,
-            early_stopping_patience=-1,
-            train_batch_size=1,
-            eval_batch_size=2,
-            max_epochs=2,
-            alpha_mlm=0.2,
-            alpha_ce=0.8,
-            do_predict=True,
-            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("test_generations.txt", contents)
-        self.assertIn("test_results.txt", contents)
-
-        metrics = load_json(model.metrics_save_path)
-        last_step_stats = metrics["val"][-1]
-        self.assertGreaterEqual(last_step_stats["val_avg_gen_time"], 0.01)
-        self.assertGreaterEqual(1.0, last_step_stats["val_avg_gen_time"])
-        self.assertIsInstance(last_step_stats[f"val_avg_{model.val_metric}"], float)
-        desired_n_evals = int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1)
-        self.assertEqual(len(metrics["val"]), desired_n_evals)
-        self.assertEqual(len(metrics["test"]), 1)
-        return model
-
-
-@pytest.mark.parametrize(["model"], [pytest.param(T5_TINY), pytest.param(BART_TINY), pytest.param(MBART_TINY)])
-def test_run_eval_bart(model):
-    input_file_name = Path(tempfile.mkdtemp()) / "utest_input.source"
-    output_file_name = input_file_name.parent / "utest_output.txt"
-    assert not output_file_name.exists()
-    articles = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
-    _dump_articles(input_file_name, articles)
-    testargs = ["run_eval.py", model, str(input_file_name), str(output_file_name)]  # TODO: test score_path
-    with patch.object(sys, "argv", testargs):
-        run_generate()
-        assert Path(output_file_name).exists()
-        os.remove(Path(output_file_name))
-
-
-@pytest.mark.parametrize(
-    ["model"], [pytest.param(T5_TINY), pytest.param(BART_TINY), pytest.param(MBART_TINY), pytest.param(MARIAN_TINY)],
-)
-def test_finetune(model):
-    args_d: dict = CHEAP_ARGS.copy()
-    task = "translation" if model in [MBART_TINY, MARIAN_TINY] else "summarization"
-    args_d["label_smoothing"] = 0.1 if task == "translation" else 0
-
-    tmp_dir = make_test_data_dir()
-    output_dir = tempfile.mkdtemp(prefix="output_")
-    args_d.update(
-        data_dir=tmp_dir,
-        model_name_or_path=model,
-        tokenizer_name=None,
-        train_batch_size=2,
-        eval_batch_size=2,
-        output_dir=output_dir,
-        do_predict=True,
-        task=task,
-        src_lang="en_XX",
-        tgt_lang="ro_RO",
-        freeze_encoder=True,
-        freeze_embeds=True,
-    )
-    assert "n_train" in args_d
-    args = argparse.Namespace(**args_d)
-    module = main(args)
-
-    input_embeds = module.model.get_input_embeddings()
-    assert not input_embeds.weight.requires_grad
-    if model == T5_TINY:
-        lm_head = module.model.lm_head
-        assert not lm_head.weight.requires_grad
-        assert (lm_head.weight == input_embeds.weight).all().item()
-
-    else:
-        bart = module.model.model
-        embed_pos = bart.decoder.embed_positions
-        assert not embed_pos.weight.requires_grad
-        assert not bart.shared.weight.requires_grad
-        # check that embeds are the same
-        assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
-        assert bart.decoder.embed_tokens == bart.shared
-
-
-def test_pack_dataset():
-    tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
-
-    tmp_dir = Path(make_test_data_dir())
-    orig_examples = tmp_dir.joinpath("train.source").open().readlines()
-    save_dir = Path(tempfile.mkdtemp(prefix="packed_"))
-    pack_data_dir(tokenizer, tmp_dir, 128, save_dir)
-    orig_paths = {x.name for x in tmp_dir.iterdir()}
-    new_paths = {x.name for x in save_dir.iterdir()}
-    packed_examples = save_dir.joinpath("train.source").open().readlines()
-    # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
-    # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
-    assert len(packed_examples) < len(orig_examples)
-    assert len(packed_examples) == 1
-    assert len(packed_examples[0]) == sum(len(x) for x in orig_examples)
-    assert orig_paths == new_paths
-
-
-def test_mbart_dataset_truncation():
-    tokenizer = MBartTokenizer.from_pretrained(MBART_TINY)
-    tmp_dir = make_test_data_dir()
-    max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
-    max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
-    trunc = 4
-    src_lang, tgt_lang = "ro_RO", "de_DE"  # NOT WHAT IT WAS TRAINED ON
-    train_dataset = MBartDataset(
-        tokenizer,
-        data_dir=tmp_dir,
-        type_path="train",
-        max_source_length=trunc,
-        max_target_length=1000,  # ignored
-        src_lang=src_lang,
-        tgt_lang=tgt_lang,
-    )
-    dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
-    for batch in dataloader:
-        assert isinstance(batch, dict)
-        assert batch["attention_mask"].shape == batch["input_ids"].shape
-        # show that articles were trimmed.
-        assert batch["input_ids"].shape[1] == trunc
-        # show that targets are the same len
-        assert batch["decoder_input_ids"].shape[1] == trunc
-        # check language codes in correct place
-        assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
-        assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
-        assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
-        assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
-
-        assert max_len_target > trunc  # Truncated
-        assert max_len_source > trunc
-        break  # No need to test every batch
-
-
-@pytest.mark.parametrize(["tok"], [pytest.param(T5_TINY), pytest.param(BART_TINY), param(MARIAN_TINY)])
-def test_summarization_dataset_truncation(tok):
-    tokenizer = AutoTokenizer.from_pretrained(tok)
-    tmp_dir = make_test_data_dir()
-    max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
-    max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
-    trunc_target = 4
-    train_dataset = Seq2SeqDataset(
-        tokenizer, data_dir=tmp_dir, type_path="train", max_source_length=20, max_target_length=trunc_target,
-    )
-    dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
-    for batch in dataloader:
-        assert batch["attention_mask"].shape == batch["input_ids"].shape
-        # show that articles were trimmed.
-        assert batch["input_ids"].shape[1] == max_len_source
-        assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
-        # show that targets were truncated
-        assert batch["decoder_input_ids"].shape[1] == trunc_target  # Truncated
-        assert max_len_target > trunc_target  # Truncated
-        break  # No need to test every batch
@@ -1,24 +0,0 @@
-#!/usr/bin/env bash
-export PYTHONPATH="../":"${PYTHONPATH}"
-
-export BS=32
-export GAS=1
-
-python finetune.py \
-    --learning_rate=3e-5 \
-    --fp16 \
-    --gpus 1 \
-    --do_train \
-    --do_predict \
-    --val_check_interval 0.25 \
-    --n_val 500 \
-    --num_train_epochs 2 \
-    --freeze_encoder --freeze_embeds --data_dir $CNN_DIR \
-    --max_target_length 142 --val_max_target_length=142 \
-    --train_batch_size=$BS --eval_batch_size=$BS --gradient_accumulation_steps=$GAS \
-    --model_name_or_path sshleifer/student_cnn_12_6 \
-    --tokenizer_name facebook/bart-large \
-    --warmup_steps 500 \
-    --output_dir distilbart-cnn-12-6 \
-    $@
-
@@ -1,21 +0,0 @@
-#!/usr/bin/env bash
-export PYTHONPATH="../":"${PYTHONPATH}"
-export BS=16
-export GAS=2
-python distillation.py \
-  --learning_rate=3e-4 \
-  --do_train \
-  --do_predict \
-  --fp16 \
-  --val_check_interval 0.1 --n_val 1000 \
-  --teacher facebook/bart-large-xsum --data_dir $XSUM_DIR \
-  --max_target_length=60 --val_max_target_length=60 --test_max_target_length=100 \
-  --student_decoder_layers 6 --student_encoder_layers 12 \
-  --freeze_encoder --freeze_embeds \
-  --model_name_or_path IGNORED \
-  --alpha_hid=3. --length_penalty=0.5 \
-  --train_batch_size=$BS --eval_batch_size=$BS --gradient_accumulation_steps=$GAS --num_train_epochs=6 \
-  --tokenizer_name facebook/bart-large \
-  --warmup_steps 500 \
-  --output_dir distilbart_xsum_12_6 \
-  $@
@@ -1,20 +0,0 @@
-#!/usr/bin/env bash
-export PYTHONPATH="../":"${PYTHONPATH}"
-
-python finetune.py \
-    --learning_rate=3e-5 \
-    --fp16 \
-    --do_train \
-    --do_predict \
-    --val_check_interval 0.25 \
-    --adam_eps 1e-06 \
-    --num_train_epochs 6 --src_lang en_XX --tgt_lang ro_RO \
-    --data_dir $ENRO_DIR \
-    --max_source_length $MAX_LEN --max_target_length $MAX_LEN --val_max_target_length $MAX_LEN --test_max_target_length $MAX_LEN \
-    --train_batch_size=$BS --eval_batch_size=$BS --gradient_accumulation_steps=$GAS \
-    --task translation \
-    --warmup_steps 500 \
-    --freeze_embeds \
-    --early_stopping_patience 4 \
-    --model_name_or_path facebook/mbart-large-cc25 \
-    $@
@@ -1,304 +0,0 @@
-import itertools
-import json
-import linecache
-import os
-import pickle
-import warnings
-from logging import getLogger
-from pathlib import Path
-from typing import Callable, Dict, Iterable, List
-
-import git
-import numpy as np
-import torch
-from rouge_score import rouge_scorer, scoring
-from sacrebleu import corpus_bleu
-from torch import nn
-from torch.utils.data import Dataset, Sampler
-
-from transformers import BartTokenizer
-
-
-def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=-100):
-    """From fairseq"""
-    if target.dim() == lprobs.dim() - 1:
-        target = target.unsqueeze(-1)
-    nll_loss = -lprobs.gather(dim=-1, index=target)
-    smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
-    if ignore_index is not None:
-        pad_mask = target.eq(ignore_index)
-        nll_loss.masked_fill_(pad_mask, 0.0)
-        smooth_loss.masked_fill_(pad_mask, 0.0)
-        bs = pad_mask.long().sum()
-    else:
-        nll_loss = nll_loss.squeeze(-1)
-        smooth_loss = smooth_loss.squeeze(-1)
-        bs = lprobs.shape[0]
-
-    nll_loss = nll_loss.sum()  # mean()? Scared to break other math.
-    smooth_loss = smooth_loss.sum()
-    eps_i = epsilon / lprobs.size(-1)
-    loss = (1.0 - epsilon) * nll_loss + eps_i * smooth_loss
-    return loss / bs, nll_loss / bs
-
-
-def encode_line(tokenizer, line, max_length, pad_to_max_length=True, return_tensors="pt"):
-    extra_kw = {"add_prefix_space": True} if isinstance(tokenizer, BartTokenizer) else {}
-    return tokenizer(
-        [line],
-        max_length=max_length,
-        padding="max_length" if pad_to_max_length else None,
-        truncation=True,
-        return_tensors=return_tensors,
-        **extra_kw,
-    )
-
-
-def lmap(f: Callable, x: Iterable) -> List:
-    """list(map(f, x))"""
-    return list(map(f, x))
-
-
-def calculate_bleu_score(output_lns, refs_lns, **kwargs) -> dict:
-    """Uses sacrebleu's corpus_bleu implementation."""
-    return {"bleu": corpus_bleu(output_lns, [refs_lns], **kwargs).score}
-
-
-def trim_batch(
-    input_ids, pad_token_id, attention_mask=None,
-):
-    """Remove columns that are populated exclusively by pad_token_id"""
-    keep_column_mask = input_ids.ne(pad_token_id).any(dim=0)
-    if attention_mask is None:
-        return input_ids[:, keep_column_mask]
-    else:
-        return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
-
-
-class Seq2SeqDataset(Dataset):
-    def __init__(
-        self,
-        tokenizer,
-        data_dir,
-        max_source_length,
-        max_target_length,
-        type_path="train",
-        n_obs=None,
-        src_lang=None,
-        tgt_lang=None,
-        prefix="",
-    ):
-        super().__init__()
-        self.src_file = Path(data_dir).joinpath(type_path + ".source")
-        self.tgt_file = Path(data_dir).joinpath(type_path + ".target")
-        self.src_lens = self.get_char_lens(self.src_file)
-        self.max_source_length = max_source_length
-        self.max_target_length = max_target_length
-        assert min(self.src_lens) > 0, f"found empty line in {self.src_file}"
-        self.tokenizer = tokenizer
-        self.prefix = prefix
-        if n_obs is not None:
-            self.src_lens = self.src_lens[:n_obs]
-        self.pad_token_id = self.tokenizer.pad_token_id
-        self.src_lang = src_lang
-        self.tgt_lang = tgt_lang
-
-    def __len__(self):
-        return len(self.src_lens)
-
-    def __getitem__(self, index) -> Dict[str, torch.Tensor]:
-        index = index + 1  # linecache starts at 1
-        source_line = self.prefix + linecache.getline(str(self.src_file), index).rstrip("\n")
-        tgt_line = linecache.getline(str(self.tgt_file), index).rstrip("\n")
-        assert source_line, f"empty source line for index {index}"
-        assert tgt_line, f"empty tgt line for index {index}"
-        source_inputs = encode_line(self.tokenizer, source_line, self.max_source_length)
-        target_inputs = encode_line(self.tokenizer, tgt_line, self.max_target_length)
-
-        source_ids = source_inputs["input_ids"].squeeze()
-        target_ids = target_inputs["input_ids"].squeeze()
-        src_mask = source_inputs["attention_mask"].squeeze()
-        return {
-            "input_ids": source_ids,
-            "attention_mask": src_mask,
-            "decoder_input_ids": target_ids,
-        }
-
-    @staticmethod
-    def get_char_lens(data_file):
-        return [len(x) for x in Path(data_file).open().readlines()]
-
-    @staticmethod
-    def trim_seq2seq_batch(batch, pad_token_id) -> tuple:
-        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"])
-        return source_ids, source_mask, y
-
-    def collate_fn(self, batch) -> Dict[str, torch.Tensor]:
-        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
-        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
-
-    def make_sortish_sampler(self, batch_size):
-        return SortishSampler(self.src_lens, batch_size)
-
-
-class MBartDataset(Seq2SeqDataset):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        if self.max_source_length != self.max_target_length:
-            warnings.warn(
-                f"Mbart will ignore max_target_length = {self.max_target_length} and use {self.max_source_length} for both sides."
-            )
-
-    def __getitem__(self, index) -> Dict[str, str]:
-        index = index + 1  # linecache starts at 1
-        source_line = self.prefix + linecache.getline(str(self.src_file), index).rstrip("\n")
-        tgt_line = linecache.getline(str(self.tgt_file), index).rstrip("\n")
-        assert source_line, f"empty source line for index {index}"
-        assert tgt_line, f"empty tgt line for index {index}"
-        return {
-            "tgt_texts": tgt_line,
-            "src_texts": source_line,
-        }
-
-    def collate_fn(self, batch) -> Dict[str, torch.Tensor]:
-        batch_encoding = self.tokenizer.prepare_translation_batch(
-            [x["src_texts"] for x in batch],
-            src_lang=self.src_lang,
-            tgt_texts=[x["tgt_texts"] for x in batch],
-            tgt_lang=self.tgt_lang,
-            max_length=self.max_source_length,
-        )
-        return batch_encoding.data
-
-
-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 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)
-
-
-logger = getLogger(__name__)
-
-
-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:
-        pars = task_specific_params.get(task, {})
-        logger.info(f"using task specific params for {task}: {pars}")
-        model.config.update(pars)
-
-
-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) -> None:
-    """Save git information to output_dir/git_log.json"""
-    repo_infos = get_git_info()
-    save_json(repo_infos, os.path.join(folder_path, "git_log.json"))
-
-
-def save_json(content, path):
-    with open(path, "w") as f:
-        json.dump(content, f, indent=4)
-
-
-def load_json(path):
-    with open(path) as f:
-        return json.load(f)
-
-
-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], use_stemmer=True) -> Dict:
-    scorer = rouge_scorer.RougeScorer(ROUGE_KEYS, use_stemmer=use_stemmer)
-    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"
Author	SHA1	Message	Date
sshleifer	a07fcff087	boom boom	2020-06-25 02:10:13 +00:00
sshleifer	243f2d98f4	boom boom	2020-06-24 20:44:27 +00:00
sshleifer	7c6802d216	boom boom	2020-06-23 16:10:00 -04:00
sshleifer	6dd3122ad4	tests pass	2020-06-23 14:35:54 -04:00
sshleifer	3a8f32d919	pl 0.8.1 compat	2020-06-19 14:46:51 +00:00
sshleifer	29d1eb57ac	Support top, bottom init	2020-06-19 09:43:20 -04:00
sshleifer	02418a591b	boom boom	2020-06-18 21:38:13 -04:00
sshleifer	be95182ee1	half-merge	2020-06-18 21:37:14 -04:00
sshleifer	970fe3d833	smaller distilbart-clean delta	2020-06-17 16:03:49 -04:00
sshleifer	97e29a9cf3	allstats-option	2020-06-15 21:38:08 -04:00
sshleifer	785a1b6076	RougeTracker	2020-06-15 21:13:36 -04:00
sshleifer	9ef5581d5e	boom boom	2020-06-15 18:05:27 -04:00
sshleifer	657b7fdada	boom boom	2020-06-15 17:57:13 -04:00
sshleifer	3a5999f5d4	boom boom	2020-06-15 17:50:28 -04:00
sshleifer	0efb2830f4	turn off ES callback	2020-06-15 17:43:01 -04:00
sshleifer	b5099747f9	boom boom	2020-06-15 17:36:56 -04:00
sshleifer	34fd62a5ea	init with copy	2020-06-15 17:36:12 -04:00
sshleifer	e167e9fc74	boom boom	2020-06-15 17:19:30 -04:00
sshleifer	1df5e7af4a	odir fix	2020-06-15 17:10:53 -04:00
sshleifer	544f543861	rouge_tracker_csvs	2020-06-15 16:56:58 -04:00
sshleifer	10970ec650	fixed dec_mask, maybe	2020-06-15 12:42:54 -04:00
sshleifer	78b1c850f2	passing, unsolved	2020-06-15 12:24:39 -04:00
sshleifer	ac8d301786	boom boom	2020-06-14 21:56:11 -04:00
sshleifer	9f32be4fbc	boom boom	2020-06-14 21:54:18 -04:00
sshleifer	74c8ac0f84	fixes	2020-06-14 21:51:09 -04:00
sshleifer	1d5e77d87b	test_passing	2020-06-14 21:41:49 -04:00
sshleifer	47b3638593	boom boom	2020-06-14 21:18:15 -04:00
sshleifer	f7c95805cf	boom boom	2020-06-14 21:01:52 -04:00
sshleifer	0169cbbe49	all but t5 passing	2020-06-14 21:01:41 -04:00
sshleifer	8e4a74b1cb	borked	2020-06-14 20:46:20 -04:00
sshleifer	86034ae640	Merge branch 'distilbart-clean' into theseus	2020-06-14 19:51:25 -04:00
sshleifer	5283878c9f	Move stuff to utils	2020-06-14 19:17:31 -04:00
sshleifer	6bdfb14664	boom boom	2020-06-14 19:07:15 -04:00
sshleifer	99de2c3c3c	pass through logger	2020-06-14 19:01:15 -04:00
sshleifer	9e95429843	docs	2020-06-14 18:20:02 -04:00
sshleifer	c9597fef42	docs	2020-06-14 18:15:28 -04:00
sshleifer	c34b886cd1	Wandb logger	2020-06-14 18:11:22 -04:00
sshleifer	f179e7b37c	Allow wandb logger	2020-06-14 17:58:41 -04:00
sshleifer	969c271bd7	more honest docs	2020-06-14 20:18:43 +00:00
sshleifer	9f98a6ac15	docs	2020-06-14 16:07:25 -04:00
sshleifer	4606bda735	Bash cleanup	2020-06-14 19:47:04 +00:00
sshleifer	eb84b9d5ee	boom boom	2020-06-14 15:36:29 -04:00
sshleifer	5526be3499	boom boom	2020-06-14 15:35:47 -04:00
sshleifer	6bf996f58d	boom boom	2020-06-14 15:31:46 -04:00
sshleifer	35a82ee87f	boom boom	2020-06-14 15:01:50 -04:00
sshleifer	379e8c7dba	Cleanup	2020-06-14 15:00:35 -04:00
sshleifer	3e62d96b14	add git-python requirement	2020-06-14 14:28:20 -04:00
sshleifer	350eeb7cd2	style	2020-06-14 13:17:09 -04:00
sshleifer	c06e38add3	boom boom	2020-06-14 13:15:17 -04:00
sshleifer	d94b09d285	started	2020-06-13 20:27:10 -04:00
sshleifer	eab9779732	better mask logic	2020-06-13 20:12:11 -04:00
sshleifer	4a6b23ee40	boom boom	2020-06-13 20:11:41 -04:00
sshleifer	e2d45445c3	style	2020-06-13 19:55:14 -04:00
sshleifer	de7035d9f6	style	2020-06-13 19:49:35 -04:00
sshleifer	abee3e2ecd	Merge branch 'master' into distilbart-clean	2020-06-13 16:40:30 -04:00
sshleifer	0274338aff	original tests pass	2020-06-13 16:40:04 -04:00
sshleifer	e67ad41ae7	boom boom	2020-06-13 15:33:00 -04:00
sshleifer	c5a1de5b75	boom boom	2020-06-13 15:30:03 -04:00
sshleifer	af3f86fd6a	remove some cruft	2020-06-11 23:58:34 -04:00
sshleifer	fb6eb096ec	Failing but minimal	2020-06-11 23:51:55 -04:00
sshleifer	bdf0b1ab88	boom boom	2020-06-11 23:45:32 -04:00
sshleifer	9d99761b1b	passing	2020-06-11 22:55:51 -04:00
sshleifer	3f3040ae12	boom boom	2020-06-11 10:51:05 -04:00
sshleifer	4224ac999d	boom boom	2020-06-11 09:29:05 -04:00
sshleifer	c7d05f70db	boom boom	2020-06-11 09:26:30 -04:00
sshleifer	ac47dbdfdf	boom boom	2020-06-11 09:24:46 -04:00
sshleifer	10b092ea27	boom boom	2020-06-11 09:22:15 -04:00
sshleifer	e5728e4ddb	boom boom	2020-06-11 02:31:05 +00:00
sshleifer	5a281449df	fp16_ever=False	2020-06-11 02:29:48 +00:00
sshleifer	585bb576ba	passing cpu	2020-06-10 19:47:02 -04:00
sshleifer	6aa28bd1f5	ignore student	2020-06-10 23:27:33 +00:00
sshleifer	a2f2d5da31	undo some chg	2020-06-10 23:23:20 +00:00
sshleifer	b2b222d0db	boom boom	2020-06-10 17:20:26 -04:00
sshleifer	574ffbb1eb	boom boom	2020-06-10 17:17:59 -04:00
sshleifer	a549aaac3e	boom boom	2020-06-10 17:17:03 -04:00
sshleifer	bad1b75a91	boom boom	2020-06-10 17:04:13 -04:00
sshleifer	6624b63c24	boom boom	2020-06-10 16:56:42 -04:00
sshleifer	7f3f9fae22	boom boom	2020-06-10 16:36:06 -04:00
sshleifer	c553e1214c	t5 works	2020-06-09 23:54:39 -04:00
sshleifer	d26f5dac45	stuck on t5 mask	2020-06-09 23:35:59 -04:00
sshleifer	913bb8f770	boom boom	2020-06-09 22:53:11 -04:00
sshleifer	af05c0e004	boom boom	2020-06-09 22:52:35 -04:00
sshleifer	dc785f0d10	boom boom	2020-06-09 22:51:59 -04:00
sshleifer	d3b8694cd8	boom boom	2020-06-09 22:50:44 -04:00
sshleifer	b5adf48404	boom boom	2020-06-09 22:49:23 -04:00
sshleifer	315ab6be60	boom boom	2020-06-09 22:48:38 -04:00
sshleifer	76377c9854	boom boom	2020-06-09 22:45:35 -04:00
sshleifer	a387c057db	boom boom	2020-06-09 22:45:03 -04:00
sshleifer	0098872c04	Deleted SummarizationDistiller	2020-06-09 22:41:36 -04:00
sshleifer	a79f5fb77e	boom boom	2020-06-09 22:09:57 -04:00
sshleifer	0dc32e6f27	boom boom	2020-06-09 21:11:07 -04:00
sshleifer	fa7f8185e8	t5 failing	2020-06-09 17:37:38 -04:00
sshleifer	31b4c8cc08	boom boom	2020-06-09 15:31:31 -04:00
sshleifer	318a585f2b	boom boom	2020-06-09 17:44:45 +00:00
sshleifer	630af0c3fa	boom boom	2020-06-09 13:27:31 -04:00
sshleifer	3c384445a7	boom boom	2020-06-08 21:58:24 +00:00
sshleifer	9f6596ebc9	boom boom	2020-06-08 14:30:54 -04:00
sshleifer	582efc0319	boom boom	2020-06-08 14:00:07 -04:00
sshleifer	3467f1c7f6	boom boom	2020-06-08 17:58:33 +00:00
sshleifer	bcb4624d42	boom boom	2020-06-08 13:57:57 -04:00
sshleifer	e46e10150d	boom boom	2020-06-08 13:29:51 -04:00
sshleifer	ecff6f92fa	V8 and generate_summaries fixes	2020-06-08 13:14:46 -04:00
sshleifer	c59be9b2e0	boom boom	2020-06-08 13:07:01 -04:00
sshleifer	b32d283d26	boom boom	2020-06-08 12:17:51 -04:00
sshleifer	7ccd2366de	Evaluate_checkpoint	2020-06-08 11:32:29 -04:00
sshleifer	c294e5e331	boom boom	2020-06-07 14:23:46 -04:00
sshleifer	3b304afdf7	boom boom	2020-06-07 14:21:49 -04:00
sshleifer	09f199b790	Merge branch 'distilbart' of github.com:sshleifer/transformers_fork into distilbart	2020-06-07 14:15:28 -04:00
sshleifer	bb1bffc89e	Save top1	2020-06-07 14:15:23 -04:00
sshleifer	47c226ea3f	Merge branch 'distilbart' of github.com:sshleifer/transformers_fork into distilbart	2020-06-07 17:48:10 +00:00
sshleifer	3abbf78a48	boom boom	2020-06-07 17:48:06 +00:00
sshleifer	9fc692d9f3	boom boom	2020-06-06 13:40:07 -04:00
sshleifer	e146713686	passing	2020-06-06 13:19:46 -04:00
sshleifer	014c061f3d	boom boom	2020-06-06 11:19:45 -04:00
sshleifer	e446fd24ef	boom boom	2020-06-06 13:13:55 +00:00
sshleifer	055167ba12	boom boom	2020-06-06 01:00:50 -04:00
sshleifer	8f68b91e81	boom boom	2020-06-05 20:10:55 -04:00
sshleifer	8b98c4cfa8	boom boom	2020-06-05 20:02:23 -04:00
sshleifer	e3a00a402b	boom boom	2020-06-05 19:33:35 -04:00
sshleifer	24f483cf06	boom boom	2020-06-05 19:33:12 -04:00
sshleifer	8d4f075245	boom boom	2020-06-05 19:26:14 -04:00
sshleifer	0b98750ecd	boom boom	2020-06-05 17:26:11 -04:00
sshleifer	cc83d34a7d	boom boom	2020-06-05 15:13:24 -04:00
sshleifer	a65ab52d74	boom boom	2020-06-05 19:10:02 +00:00
sshleifer	3e47e61a1a	Disable cos loss	2020-06-05 12:51:13 -04:00
sshleifer	20aedfbcf7	boom boom	2020-06-04 09:45:47 -04:00
sshleifer	440744a111	Distiller brewer does not break	2020-06-04 09:38:02 -04:00
sshleifer	a2ba031b92	boom boom	2020-06-04 07:52:34 -04:00
sshleifer	8f614af5b7	broken	2020-06-04 07:15:34 -04:00
sshleifer	df0b28a581	boom boom	2020-06-04 02:09:27 -04:00
sshleifer	8c6769ee41	boom boom	2020-06-03 13:04:02 +00:00
sshleifer	b11fc6977e	boom boom	2020-06-03 13:02:37 +00:00
sshleifer	fb6adb2a47	backup rouge dfs	2020-06-03 12:53:05 +00:00
sshleifer	789f06e9d8	less annoying requirements	2020-06-02 18:59:11 -04:00
sshleifer	e17df7499c	merged master	2020-06-02 13:45:10 -04:00
sshleifer	82a6b8345f	Merge branch 'master' into distilbart	2020-06-02 13:44:40 -04:00
sshleifer	60a90143ee	rouge tracker fix	2020-06-02 11:17:09 -04:00
sshleifer	7b9557fbf5	boom boom	2020-05-31 09:16:20 -04:00
sshleifer	105d83d595	undo rename	2020-05-31 08:52:04 -04:00
sshleifer	bd361eae11	rename generic_train -> build_trainer	2020-05-31 08:38:36 -04:00
sshleifer	8fb24ce3ce	boom boom	2020-05-27 15:49:36 -04:00
sshleifer	6689b594fc	boom boom	2020-05-27 15:46:50 -04:00
sshleifer	25edee17ed	boom boom	2020-05-27 14:22:36 -04:00
sshleifer	7c52e73dfe	boom boom	2020-05-27 14:19:08 -04:00
sshleifer	d53236c840	boom boom	2020-05-27 14:17:34 -04:00
sshleifer	eb4b3cce7a	boom boom	2020-05-27 13:50:07 -04:00
sshleifer	324d4dcfce	No wandb if ddp	2020-05-27 12:49:11 -04:00
sshleifer	3f6463a895	attempt logger.name fix	2020-05-27 11:51:49 -04:00
sshleifer	3c34cae08a	distrib hacks	2020-05-27 15:47:45 +00:00
sshleifer	175efd864a	getstate	2020-05-27 14:33:15 +00:00
sshleifer	516bbcabd0	boom boom	2020-05-27 10:28:34 -04:00
sshleifer	0c3d6d8cbf	boom boom	2020-05-27 10:19:14 -04:00
sshleifer	84ea9fac23	Fixed tests	2020-05-27 09:37:52 -04:00
sshleifer	44743e8dff	fix ckpt bug maybe	2020-05-27 08:49:18 -04:00
sshleifer	7f1af24a05	freeze decoder	2020-05-27 01:10:20 -04:00
sshleifer	99e7161eb2	boom boom	2020-05-26 21:23:47 -04:00
sshleifer	d92a69f223	boom boom	2020-05-26 21:22:08 -04:00
sshleifer	4f19747960	boom boom	2020-05-26 21:20:53 -04:00
sshleifer	9b54356b0b	Logging adjustments	2020-05-26 20:08:41 -04:00
sshleifer	f750d801ff	boom boom	2020-05-26 15:25:28 -04:00
sshleifer	298ec5361c	--freeze_encoder	2020-05-26 15:21:00 -04:00
sshleifer	ee18f81852	Merge branch 'distilbart' of github.com:sshleifer/transformers_fork into distilbart	2020-05-26 15:05:36 -04:00
sshleifer	e7d9dc3d22	dont default teacher	2020-05-26 19:05:30 +00:00
sshleifer	d2cd7b4d9d	Somehow passing with better freezing logic	2020-05-26 15:01:56 -04:00
sshleifer	d3611c9844	Run distiller: no data defaults	2020-05-26 12:00:12 -04:00
sshleifer	c10bb9fb23	boom boom	2020-05-25 23:15:20 -04:00
sshleifer	81e939fb54	boom boom	2020-05-25 21:23:39 -04:00
sshleifer	0c3d22ea86	boom boom	2020-05-25 21:20:08 -04:00
sshleifer	63521f19a9	boom boom	2020-05-25 21:18:18 -04:00
sshleifer	56aedacc27	boom boom	2020-05-25 21:15:00 -04:00
sshleifer	0933e118e0	boom boom	2020-05-25 21:07:02 -04:00
sshleifer	12f2d9e6bc	early quitting for encoder only loss	2020-05-25 21:01:47 -04:00
sshleifer	20a59685a9	passing with enc mse	2020-05-25 20:01:04 -04:00
sshleifer	f5e697bbe6	Dont require model_name_or_path	2020-05-25 19:31:17 -04:00
sshleifer	db52f3708f	save fewer checkpoints	2020-05-25 17:18:57 -04:00
sshleifer	800dcdcce2	boom boom	2020-05-25 16:51:21 -04:00
sshleifer	88f9f917a6	Sortish Sampler	2020-05-25 16:44:48 -04:00
sshleifer	a42f09eec6	warmup_steps=500	2020-05-25 16:24:49 -04:00
sshleifer	c154fb3f8b	style	2020-05-25 10:12:23 -04:00
sshleifer	c7f6e62c3e	boom boom	2020-05-25 09:26:59 -04:00
sshleifer	af8b962edf	boom boom	2020-05-25 09:26:11 -04:00
sshleifer	6f1757d2b8	boom boom	2020-05-25 09:23:48 -04:00
sshleifer	4d3617acc6	boom boom	2020-05-24 22:45:23 -04:00
sshleifer	2443a7eefc	boom boom	2020-05-24 22:42:50 -04:00
sshleifer	6f03e39b30	boom boom	2020-05-24 22:39:17 -04:00
sshleifer	9f214624f4	boom boom	2020-05-24 22:37:28 -04:00
sshleifer	e48a8299b9	boom boom	2020-05-24 22:36:48 -04:00
sshleifer	20abeec1f5	passing	2020-05-24 14:22:45 -04:00
sshleifer	584c329ffe	encoder different	2020-05-24 14:20:29 -04:00
sshleifer	d32887160a	add RougeTracker	2020-05-22 13:02:59 -04:00
sshleifer	ef45ee9a7e	boom boom	2020-05-22 15:58:06 +00:00
sshleifer	8fbdd7d205	boom boom	2020-05-22 11:54:34 -04:00
sshleifer	1ca011ff75	test_mtl=140 default	2020-05-22 11:49:34 -04:00
sshleifer	a78603cee7	boom boom	2020-05-21 23:14:56 -04:00
sshleifer	d4830f9d0a	rename	2020-05-21 23:13:57 -04:00
sshleifer	c779c38297	boom boom	2020-05-21 17:04:19 -04:00
sshleifer	021d2b9f80	only train	2020-05-21 15:04:20 -04:00
sshleifer	fa0eda43b6	boom boom	2020-05-21 14:19:55 -04:00
sshleifer	c6f7e14d0b	progbar	2020-05-21 14:07:16 -04:00
sshleifer	db3d85ad2c	boom boom	2020-05-21 13:56:35 -04:00
sshleifer	705ed2ffb4	Better desc	2020-05-21 13:43:47 -04:00
sshleifer	29499c9ca5	boom boom	2020-05-21 13:19:07 -04:00
sshleifer	bcb9996ae5	overcautious freezing	2020-05-21 13:17:17 -04:00
sshleifer	aa995f98a3	freeze after init	2020-05-21 13:02:00 -04:00
sshleifer	5d751729ec	Freeze encoder fix	2020-05-21 12:55:59 -04:00
sshleifer	f5606df47b	boom boom	2020-05-21 12:26:07 -04:00
sshleifer	ca82946559	boom boom	2020-05-21 12:24:06 -04:00
sshleifer	3461e24b8d	boom boom	2020-05-21 12:21:42 -04:00
sshleifer	15a2d4ee79	boom boom	2020-05-21 11:45:07 -04:00
sshleifer	3f73cb76a7	boom boom	2020-05-21 11:41:13 -04:00
sshleifer	88b4b970d3	switch copy logic	2020-05-21 11:39:29 -04:00
sshleifer	ac72c7aadc	convenience method	2020-05-21 09:55:38 -04:00
sshleifer	dd418e8d1d	boom boom	2020-05-21 09:41:17 -04:00
sshleifer	07da0623ad	avoid losing bart grouped batch sampler work	2020-05-21 08:49:24 -04:00
sshleifer	21a2cc0eb1	delete run_distiller -> main	2020-05-21 01:20:11 -04:00
sshleifer	2c2a10e5d7	orig tests pass	2020-05-21 01:08:43 -04:00
sshleifer	910da0fb96	boom boom	2020-05-21 01:00:57 -04:00
sshleifer	7a7dcd397f	l2copy[1] = 0	2020-05-20 22:25:18 -04:00
sshleifer	b39c39fe87	boom boom	2020-05-20 22:09:49 -04:00
sshleifer	ce0de9073a	boom boom	2020-05-20 22:07:35 -04:00
sshleifer	cf47a6ff9d	boom boom	2020-05-20 22:05:05 -04:00
sshleifer	a6420767e9	boom boom	2020-05-20 22:04:48 -04:00
sshleifer	fee71b4fef	npars	2020-05-20 22:01:26 -04:00
sshleifer	a2671a6eee	boom boom	2020-05-20 21:30:49 -04:00
sshleifer	18524d8396	boom boom	2020-05-20 21:30:36 -04:00
sshleifer	a250d1c6aa	boom boom	2020-05-20 21:30:09 -04:00
sshleifer	b62bfe666d	spelling	2020-05-20 18:36:14 -04:00
sshleifer	1fc653f626	log_metrics	2020-05-20 18:18:11 -04:00
sshleifer	f281724e92	wandb	2020-05-20 18:14:27 -04:00
sshleifer	67f9553d4b	Run encoder once	2020-05-20 17:58:43 -04:00
sshleifer	cd461e13ff	fix	2020-05-20 16:40:27 -04:00
sshleifer	05bfb1eb1d	Support fewer layers	2020-05-20 16:38:20 -04:00
sshleifer	c79351a64a	fix flatten	2020-05-20 15:59:22 -04:00
sshleifer	c774160bd2	imports	2020-05-20 15:30:32 -04:00
sshleifer	beda65dd17	bash	2020-05-20 19:29:21 +00:00
sshleifer	59c658de2f	fixes	2020-05-20 15:28:05 -04:00
sshleifer	5947812374	add files	2020-05-20 15:08:41 -04:00
sshleifer	09cc1e64cf	save preds	2020-05-20 15:04:38 -04:00
sshleifer	5fbf42258c	summaries for file	2020-05-20 12:56:26 -04:00
sshleifer	af315c157e	boom boom	2020-05-20 09:55:53 -04:00
sshleifer	2c7294830e	val check test passing with smaller batch size	2020-05-20 09:31:11 -04:00
sshleifer	2b7132c25e	metrics saving, but no val_check_interval honored	2020-05-20 09:20:55 -04:00
sshleifer	bef77fc211	boom boom	2020-05-19 15:59:22 -04:00
sshleifer	abb81df15f	boom boom	2020-05-19 15:58:04 -04:00
sshleifer	51221fbbf8	boom boom	2020-05-19 15:57:04 -04:00
sshleifer	2ee9388492	boom boom	2020-05-19 15:54:12 -04:00
sshleifer	c4530f91ab	batch	2020-05-19 15:22:13 -04:00
sshleifer	bbc4e52d3b	assert student small	2020-05-19 15:18:50 -04:00
sshleifer	74704cedab	boom boom	2020-05-19 14:46:35 -04:00
sshleifer	bf5782e080	boom boom	2020-05-19 14:38:41 -04:00
sshleifer	3cebd56848	boom boom	2020-05-19 14:35:05 -04:00
sshleifer	696c8c28f2	boom boom	2020-05-19 14:34:20 -04:00
sshleifer	04a8ace0df	boom boom	2020-05-19 14:32:41 -04:00
sshleifer	0ed41562eb	boom boom	2020-05-19 14:30:36 -04:00
sshleifer	7081d0f3fd	add rouge	2020-05-19 14:29:14 -04:00
sshleifer	ca9c685453	rouge	2020-05-19 14:05:46 -04:00
sshleifer	70cf536ae2	Merge branch 'distilbart' of github.com:sshleifer/transformers_fork into distilbart	2020-05-19 11:35:22 -04:00
sshleifer	5a3ed998f0	Cache tokenized	2020-05-19 11:35:17 -04:00
sshleifer	6302fb02ef	bs=8	2020-05-19 14:44:21 +00:00
sshleifer	5a35811635	boom boom	2020-05-19 09:55:26 -04:00
sshleifer	174aaf374d	Fast dev run	2020-05-19 09:54:43 -04:00
sshleifer	1edc50f670	bash	2020-05-19 13:50:01 +00:00
sshleifer	d2cc12b182	boom boom	2020-05-19 09:16:02 -04:00
sshleifer	8afb88ce24	relatif importschlossen	2020-05-19 09:09:15 -04:00
sshleifer	4be1287671	real data test passes	2020-05-19 09:00:33 -04:00
sshleifer	4f5790f652	passing	2020-05-18 15:16:41 -04:00
sshleifer	fcc49a0c5c	can import	2020-05-18 14:31:36 -04:00
sshleifer	937d3d6ae8	Merge branch 'master' into distilbart	2020-05-18 14:30:34 -04:00
sshleifer	b01f1d594c	Failing test	2020-05-18 14:19:46 -04:00
sshleifer	71850ad26b	copy decoder layers	2020-05-18 13:33:30 -04:00
sshleifer	1368969930	passing	2020-05-18 11:55:30 -04:00
sshleifer	f2de528d04	passing	2020-05-18 11:49:12 -04:00
sshleifer	eabb3c5f9a	test passing, loss doesnt change	2020-05-18 11:13:54 -04:00
sshleifer	762dd832f1	boom boom	2020-05-17 15:43:35 -04:00
sshleifer	8280dad80e	roberta extract test works	2020-05-16 11:09:26 -04:00