rename_files

.circleci/config.yml

@@ -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:

.circleci/deploy.sh

@@ -46,5 +46,4 @@ 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 "b0892fa" #v3.0.2 Latest stable release
+deploy_doc "b42586e" #v2.11.0 Latest stable release

.github/ISSUE_TEMPLATE/question-help.md

@@ -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**:

.github/workflows/self-push.yml

@@ -51,11 +51,4 @@ jobs:
         USE_CUDA: yes
       run: |
         source .env/bin/activate
-        python -m pytest -n 2 --dist=loadfile -s ./tests/ | tee output.txt
-    - name: cat output.txt
-      run: cat output.txt
-    - name: Upload output.txt
-      uses: actions/upload-artifact@v1
-      with:
-        name: pytest_output
-        path: output.txt
+        python -m pytest -n 2 --dist=loadfile -s -v ./tests/

.github/workflows/self-scheduled.yml

@@ -46,11 +46,5 @@ jobs:
         USE_CUDA: yes
       run: |
         source .env/bin/activate
-        python -m pytest -n 1 --dist=loadfile -s ./tests/  | tee output.txt
-    - name: cat output.txt
-      run: cat output.txt
-    - name: Upload output.txt
-      uses: actions/upload-artifact@v1
-      with:
-        name: pytest_output
-        path: output.txt
+        python -m pytest -n 1 --dist=loadfile -s -v ./tests/
+        

README.md

@@ -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
@@ -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()

docs/README.md

@@ -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>`__
 ```

docs/source/_static/js/custom.js

@@ -1,11 +1,10 @@
 // These two things need to be updated at each release for the version selector.
 // Last stable version
-const stableVersion = "v3.0.2"
+const stableVersion = "v2.11.0"
 // 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",
+    "": "v2.11.0 (stable)",
     "v2.10.0": "v2.10.0",
     "v2.9.1": "v2.9.0/v2.9.1",
     "v2.8.0": "v2.8.0",
@@ -87,7 +86,7 @@ function addVersionControl() {
     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$|^search.html?/)) {
+    if (parts[parts.length - 1] != "" && ! parts[parts.length - 1].match(/\.html$/)) {
         versionIndex = parts.length - 1;
     }
     // Main classes and models are nested so we need to go deeper

docs/source/benchmarks.md

@@ -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).

docs/source/benchmarks.rst

@@ -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>`__.

docs/source/conf.py

@@ -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 ---------------------------------------------------

docs/source/glossary.rst

@@ -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".

docs/source/index.rst

@@ -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::
@@ -142,10 +139,10 @@ conversion utilities for the following models:
 
     task_summary
     model_summary
-    preprocessing
     training
+    preprocessing
+    serialization
     model_sharing
-    tokenizer_summary
     multilingual
 
 .. toctree::
@@ -165,7 +162,6 @@ conversion utilities for the following models:
     :caption: Research
 
     bertology
-    perplexity
     benchmarks
 
 .. toctree::
@@ -178,7 +174,6 @@ conversion utilities for the following models:
     main_classes/pipelines
     main_classes/optimizer_schedules
     main_classes/processors
-    main_classes/trainer
     model_doc/auto
     model_doc/encoderdecoder
     model_doc/bert
@@ -203,4 +198,3 @@ conversion utilities for the following models:
     model_doc/longformer
     model_doc/retribert
     model_doc/mobilebert
-    model_doc/dpr

docs/source/main_classes/optimizer_schedules.rst

@@ -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

docs/source/main_classes/tokenizer.rst

@@ -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``
 ~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/main_classes/trainer.rst

@@ -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

docs/source/model_doc/bart.rst

@@ -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
+
+

docs/source/model_doc/dpr.rst

@@ -1,89 +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:
-
-
-DPRContextEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DPRContextEncoder
-    :members:
-
-DPRQuestionEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DPRQuestionEncoder
-    :members:
-
-
-DPRReader
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DPRReader
-    :members:

docs/source/model_doc/reformer.rst

@@ -112,17 +112,3 @@ ReformerModelWithLMHead
 
 .. autoclass:: transformers.ReformerModelWithLMHead
     :members:
-
-
-ReformerForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ReformerForMaskedLM
-    :members:
-
-
-ReformerForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ReformerForQuestionAnswering
-    :members:

docs/source/model_sharing.md

@@ -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 …
+```

docs/source/model_sharing.rst

@@ -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
-

docs/source/model_summary.rst

@@ -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">
+   <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">
+   <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">
+   <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">
+   <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">
+   <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">
+   <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">
+   <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">
+   <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">
+   <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">
+   <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">
+   <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">
+   <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">
+   <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">
+   <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">
+   <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">
+   <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">
+   <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">
+   <a href="/model_doc/t5">
        <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-t5-blueviolet">
    </a>
 

docs/source/perplexity.rst

@@ -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(1, 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.

docs/source/quicktour.rst

@@ -146,9 +146,8 @@ 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.
+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
@@ -283,7 +282,7 @@ Models are standard `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#to
 `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.
+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:
 

docs/source/serialization.rst

@@ -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)
+

docs/source/task_summary.rst

@@ -74,7 +74,7 @@ of each other. The process is the following:
   with the weights stored in the checkpoint.
 - Build a sequence from the two sentences, with the correct model-specific separators token type ids
   and attention masks (:func:`~transformers.PreTrainedTokenizer.encode` and
-  :func:`~transformers.PreTrainedTokenizer.__call__` take care of this)
+  :func:`~transformers.PreTrainedTokenizer.encode_plus` take care of this)
 - Pass this sequence through the model so that it is classified in one of the two available classes: 0
   (not a paraphrase) and 1 (is a paraphrase)
 - Compute the softmax of the result to get probabilities over the classes
@@ -95,8 +95,8 @@ of each other. The process is the following:
     >>> sequence_1 = "Apples are especially bad for your health"
     >>> sequence_2 = "HuggingFace's headquarters are situated in Manhattan"
 
-    >>> paraphrase = tokenizer(sequence_0, sequence_2, return_tensors="pt")
-    >>> not_paraphrase = tokenizer(sequence_0, sequence_1, return_tensors="pt")
+    >>> paraphrase = tokenizer.encode_plus(sequence_0, sequence_2, return_tensors="pt")
+    >>> not_paraphrase = tokenizer.encode_plus(sequence_0, sequence_1, return_tensors="pt")
 
     >>> paraphrase_classification_logits = model(**paraphrase)[0]
     >>> not_paraphrase_classification_logits = model(**not_paraphrase)[0]
@@ -128,8 +128,8 @@ of each other. The process is the following:
     >>> sequence_1 = "Apples are especially bad for your health"
     >>> sequence_2 = "HuggingFace's headquarters are situated in Manhattan"
 
-    >>> paraphrase = tokenizer(sequence_0, sequence_2, return_tensors="tf")
-    >>> not_paraphrase = tokenizer(sequence_0, sequence_1, return_tensors="tf")
+    >>> paraphrase = tokenizer.encode_plus(sequence_0, sequence_2, return_tensors="tf")
+    >>> not_paraphrase = tokenizer.encode_plus(sequence_0, sequence_1, return_tensors="tf")
 
     >>> paraphrase_classification_logits = model(paraphrase)[0]
     >>> not_paraphrase_classification_logits = model(not_paraphrase)[0]
@@ -221,7 +221,7 @@ Here is an example of question answering using a model and a tokenizer. The proc
     ... ]
 
     >>> for question in questions:
-    ...     inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="pt")
+    ...     inputs = tokenizer.encode_plus(question, text, add_special_tokens=True, return_tensors="pt")
     ...     input_ids = inputs["input_ids"].tolist()[0]
     ...
     ...     text_tokens = tokenizer.convert_ids_to_tokens(input_ids)
@@ -263,7 +263,7 @@ Here is an example of question answering using a model and a tokenizer. The proc
     ... ]
 
     >>> for question in questions:
-    ...     inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="tf")
+    ...     inputs = tokenizer.encode_plus(question, text, add_special_tokens=True, return_tensors="tf")
     ...     input_ids = inputs["input_ids"].numpy()[0]
     ...
     ...     text_tokens = tokenizer.convert_ids_to_tokens(input_ids)

docs/source/tokenizer_summary.rst

@@ -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 model 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 gives the ability to 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 are what are used to 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 from of pretrokenization, 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
-some '▁' characters, that represent spaces. Decoding a tokenized text is then super easy: we just have to concatenate
-all of them together and replace those '▁' by spaces.
-
-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>`.

docs/source/training.rst

@@ -39,7 +39,7 @@ 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)``
+``BertForSequenceClassification.from_pretrained('bert-base-uncased', num_classes=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
@@ -77,7 +77,7 @@ other than bias and layer normalization terms:
     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.PreTrainedTokenizer.batch_encode_plus`. This returns a
 :func:`~transformers.BatchEncoding` instance which
 prepares everything we might need to pass to the model.
 
@@ -272,7 +272,7 @@ 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
+takes in the data in the format provides 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``.

examples/README.md

@@ -1,4 +1,4 @@
-# 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+.
@@ -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
 |---|---|:---:|:---:|:---:|:---:|
@@ -24,8 +24,8 @@ This is still a work-in-progress – in particular documentation is still sparse
 | [**`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              | ✅ | -  | -  | -
 

examples/adversarial/utils_hans.py

@@ -298,13 +298,12 @@ 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,
         )
 

examples/benchmarking/README.md

@@ -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) | 

examples/benchmarking/run_benchmark_tf.py

@@ -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()
 
 

examples/benchmarking/time_xla_1.csv

@@ -0,0 +1,4 @@
+model,batch_size,sequence_length,result
+aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,8,512,0.2032
+aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,64,512,1.5279
+aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,256,512,6.1837

examples/bert-loses-patience/run_glue_with_pabee.py

@@ -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
@@ -647,6 +649,10 @@ 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()`

examples/contrib/mm-imdb/run_mmimdb.py

@@ -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()`

examples/contrib/run_swag.py

@@ -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()`

examples/deebert/README.md

@@ -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",
-}
-```
-

examples/deebert/entropy_eval.sh

@@ -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

examples/deebert/eval_deebert.sh

@@ -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

examples/deebert/run_glue_deebert.py

@@ -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()

examples/deebert/src/modeling_highway_bert.py

@@ -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)

examples/deebert/src/modeling_highway_roberta.py

@@ -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

examples/deebert/test_glue_deebert.py

@@ -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)

examples/deebert/train_deebert.sh

@@ -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

examples/distillation/run_squad_w_distillation.py

@@ -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()`

examples/language-modeling/run_language_modeling.py

@@ -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(

examples/lightning_base.py

@@ -122,9 +122,12 @@ class BaseTransformer(pl.LightningModule):
         else:
             optimizer.step()
         optimizer.zero_grad()
-        self.lr_scheduler.step()  # By default, PL will only step every epoch.
-        lrs = {f"lr_group_{i}": lr for i, lr in enumerate(self.lr_scheduler.get_lr())}
-        self.logger.log_metrics(lrs)
+        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)
@@ -199,7 +202,7 @@ class BaseTransformer(pl.LightningModule):
         parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
         parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
         parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
-        parser.add_argument("--warmup_steps", default=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", default=3, type=int, help="Total number of training epochs to perform."

examples/longform-qa/eli5_app.py

@@ -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,

examples/longform-qa/eli5_utils.py

@@ -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),

examples/movement-pruning/emmental/modeling_bert_masked.py

@@ -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`):

examples/movement-pruning/masked_run_glue.py

@@ -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()`

examples/movement-pruning/masked_run_squad.py

@@ -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()`

examples/multiple-choice/run_tf_multiple_choice.py

@@ -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)
 

examples/multiple-choice/utils_multiple_choice.py

@@ -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:

examples/question-answering/README.md

@@ -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.

examples/question-answering/run_squad.py

@@ -240,6 +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)
@@ -766,6 +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()`

examples/question-answering/run_squad_trainer.py

@@ -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()

examples/question-answering/run_tf_squad.py

@@ -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)

examples/seq2seq/README.md

@@ -1,11 +1,8 @@
-## 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
 
 CNN/DailyMail data
@@ -40,29 +37,14 @@ export ENRO_DIR=${PWD}/wmt_en_ro
 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.
 
+### Evaluation
 
-### Tips and Tricks
+To create summaries for each article in dataset, run:
+```bash
+python run_eval.py <path_to_test.source> test_generations.txt <model-name>  --score_path rouge_scores.txt
+```
+The default batch size, 4, fits in 16GB GPU memory, but may need to be adjusted to fit your system.
 
-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 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). 
 
 ### Summarization Finetuning
 Run/modify `finetune.sh`
@@ -80,20 +62,24 @@ The following command should work on a 16GB GPU:
 
 *Note*: The following tips mostly apply to summarization finetuning.
 
-### Translation Finetuning
+Tips:
+- 1 epoch at batch size 1 for bart-large takes 24 hours and requires 13GB GPU RAM with fp16 on an NVIDIA-V100. 
+- try `bart-base`, `--freeze_encoder` or `--freeze_embeds` for faster training/larger batch size.  (3hr/epoch with bs=8, see the "xsum_shared_task" command below)
+- `fp16_opt_level=O1` (the default works best).
+- 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).
+- In addition to the pytorch-lightning .ckpt checkpoint, a transformers checkpoint will be saved.
+Load it with `BartForConditionalGeneration.from_pretrained(f'{output_dir}/best_tfmr)`.
+- At the moment, `--do_predict` does not work in a multi-gpu setting. You need to use `evaluate_checkpoint` or the `run_eval.py` code.
+- If you want to run experiments on improving the summarization finetuning process, try the XSUM Shared Task (below). It's faster to train than CNNDM because the summaries are shorter.
+- 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 wandb_shared` or `--logger wandb`. It is useful for reproducibility. 
+- 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).
 
-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. 
-```bash
-export ENRO_DIR=${PWD}/wmt_en_ro   # may need to be fixed depending on where you downloaded
-export BS=4
-export GAS=8
-./train_mbart_cc25_enro.sh --output_dir cc25_v1_frozen/
-```
-
-
-### Finetuning Outputs 
+#### 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:
 
@@ -123,71 +109,25 @@ from transformers import AutoModelForSeq2SeqLM
 model = AutoModelForSeq2SeqLM.from_pretrained(f'{output_dir}/best_tfmr')
 ```
 
-#### XSUM Shared Task
+
+### XSUM Shared Task
 Compare XSUM results with others by using `--logger wandb_shared`. This requires `wandb` registration.
 
 Here is an example command, but you can do whatever you want. Hopefully this will make debugging and collaboration easier!
 ```bash
-WANDB_PROJECT='hf_xsum' ./finetune.sh \
+./finetune.sh \
     --data_dir $XSUM_DIR \
     --output_dir xsum_frozen_embs \
     --model_name_or_path facebook/bart-large \
+    --logger wandb_shared \
     --train_batch_size 16 --eval_batch_size 16 --freeze_embeds --freeze_encoder \
     --num_train_epochs 6 \
-    --max_target_length=60 --val_max_target_length=60 --test_max_target_length=100 \
-    --logger wandb
+    --max_target_length=60 --val_max_target_length=60 --test_max_target_length=100
 ```
 
 You can see your wandb logs [here](https://app.wandb.ai/sshleifer/hf_xsum?workspace=user-)
 
-### 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. 

examples/seq2seq/bertabs/run_summarization.py

@@ -30,7 +30,7 @@ Batch = namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mas
 
 def evaluate(args):
     tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", do_lower_case=True)
-    model = BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization")
+    model = BertAbs.from_pretrained("bertabs-finetuned-cnndm")
     model.to(args.device)
     model.eval()
 

examples/seq2seq/distillation.py

@@ -92,6 +92,8 @@ class BartSummarizationDistiller(SummarizationModule):
         student = BartForConditionalGeneration(student_cfg)
         student, _ = init_student(student, teacher)
         save_dir = self.output_dir.joinpath("student")
+        save_dir.mkdir(exist_ok=True)
+
         self.copy_to_student(d_layers_to_copy, e_layers_to_copy, hparams, student, teacher)
         student.save_pretrained(save_dir)
         hparams.model_name_or_path = str(save_dir)

examples/seq2seq/finetune.py

@@ -3,7 +3,6 @@ import glob
 import logging
 import os
 import time
-import warnings
 from collections import defaultdict
 from pathlib import Path
 from typing import Dict, List, Tuple
@@ -14,12 +13,11 @@ import torch
 from torch.utils.data import DataLoader
 
 from lightning_base import BaseTransformer, add_generic_args, generic_train
-from transformers import MBartTokenizer, get_linear_schedule_with_warmup
+from transformers import get_linear_schedule_with_warmup
 
 
 try:
     from .utils import (
-        assert_all_frozen,
         use_task_specific_params,
         SummarizationDataset,
         lmap,
@@ -48,7 +46,6 @@ except ImportError:
         get_git_info,
         ROUGE_KEYS,
         calculate_bleu_score,
-        assert_all_frozen,
     )
     from callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback
 
@@ -94,12 +91,9 @@ class SummarizationModule(BaseTransformer):
         if self.hparams.freeze_embeds:
             self.freeze_embeds()
         if self.hparams.freeze_encoder:
-            freeze_params(self.model.get_encoder())
-            assert_all_frozen(self.model.get_encoder())
-
+            freeze_params(self.model.model.encoder)  # TODO: this will break for t5
         self.hparams.git_sha = get_git_info()["repo_sha"]
         self.num_workers = hparams.num_workers
-        self.decoder_start_token_id = None
 
     def freeze_embeds(self):
         """Freeze token embeddings and positional embeddings for bart, just token embeddings for t5."""
@@ -165,12 +159,7 @@ class SummarizationModule(BaseTransformer):
         pad_token_id = self.tokenizer.pad_token_id
         source_ids, source_mask, y = SummarizationDataset.trim_seq2seq_batch(batch, pad_token_id)
         t0 = time.time()
-        generated_ids = self.model.generate(
-            input_ids=source_ids,
-            attention_mask=source_mask,
-            use_cache=True,
-            decoder_start_token_id=self.decoder_start_token_id,
-        )
+        generated_ids = self.model.generate(input_ids=source_ids, attention_mask=source_mask, use_cache=True,)
         gen_time = (time.time() - t0) / source_ids.shape[0]
         preds = self.ids_to_clean_text(generated_ids)
         target = self.ids_to_clean_text(y)
@@ -227,8 +216,6 @@ class SummarizationModule(BaseTransformer):
         scheduler = get_linear_schedule_with_warmup(
             self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=t_total
         )
-        if max(scheduler.get_last_lr()) > 0:
-            warnings.warn("All learning rates are 0")
         self.lr_scheduler = scheduler
         return dataloader
 
@@ -286,9 +273,6 @@ class SummarizationModule(BaseTransformer):
         parser.add_argument(
             "--task", type=str, default="summarization", required=False, help="# examples. -1 means use all."
         )
-        parser.add_argument("--src_lang", type=str, default="", required=False)
-        parser.add_argument("--tgt_lang", type=str, default="", required=False)
-
         return parser
 
 
@@ -298,13 +282,6 @@ class TranslationModule(SummarizationModule):
     metric_names = ["bleu"]
     val_metric = "bleu"
 
-    def __init__(self, hparams, **kwargs):
-        super().__init__(hparams, **kwargs)
-        self.dataset_kwargs["src_lang"] = hparams.src_lang
-        self.dataset_kwargs["tgt_lang"] = hparams.tgt_lang
-        if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer, MBartTokenizer):
-            self.decoder_start_token_id = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
-
     def calc_generative_metrics(self, preds, target) -> dict:
         return calculate_bleu_score(preds, target)
 
@@ -318,6 +295,8 @@ def main(args, model=None) -> SummarizationModule:
             model: SummarizationModule = SummarizationModule(args)
         else:
             model: SummarizationModule = TranslationModule(args)
+
+    dataset = Path(args.data_dir).name
     if (
         args.logger == "default"
         or args.fast_dev_run
@@ -328,12 +307,12 @@ def main(args, model=None) -> SummarizationModule:
     elif args.logger == "wandb":
         from pytorch_lightning.loggers import WandbLogger
 
-        logger = WandbLogger(name=model.output_dir.name)
+        logger = WandbLogger(name=model.output_dir.name, project=dataset)
 
     elif args.logger == "wandb_shared":
         from pytorch_lightning.loggers import WandbLogger
 
-        logger = WandbLogger(name=model.output_dir.name)
+        logger = WandbLogger(name=model.output_dir.name, project=f"hf_{dataset}")
     trainer: pl.Trainer = generic_train(
         model,
         args,

examples/seq2seq/finetune_bart_tiny.sh

@@ -12,7 +12,7 @@ export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
 # Make output directory if it doesn't exist
 mkdir -p $OUTPUT_DIR
 
-# Add parent directory to python path to access lightning_base.py and testing_utils.py
+# Add parent directory to python path to access lightning_base.py and utils.py
 export PYTHONPATH="../":"${PYTHONPATH}"
 python finetune.py \
 --data_dir=cnn_tiny/ \

examples/seq2seq/finetune_t5.sh

@@ -1,13 +1,18 @@
+export OUTPUT_DIR_NAME=t5
+export CURRENT_DIR=${PWD}
+export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
+
+# Make output directory if it doesn't exist
+mkdir -p $OUTPUT_DIR
+
 # Add parent directory to python path to access lightning_base.py
 export PYTHONPATH="../":"${PYTHONPATH}"
 
 python finetune.py \
---data_dir=$CNN_DIR \
+--data_dir=./cnn-dailymail/cnn_dm \
+--model_name_or_path=t5-large \
 --learning_rate=3e-5 \
---train_batch_size=$BS \
---eval_batch_size=$BS \
+--train_batch_size=4 \
+--eval_batch_size=4 \
 --output_dir=$OUTPUT_DIR \
---max_source_length=512 \
---val_check_interval=0.1 --n_val=200 \
---do_train --do_predict \
- $@
+--do_train  $@

examples/seq2seq/run_eval.py

@@ -9,9 +9,9 @@ from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 
 try:
-    from .utils import calculate_rouge, use_task_specific_params, calculate_bleu_score, trim_batch
+    from .utils import calculate_rouge, use_task_specific_params, calculate_bleu_score
 except ImportError:
-    from utils import calculate_rouge, use_task_specific_params, calculate_bleu_score, trim_batch
+    from utils import calculate_rouge, use_task_specific_params, calculate_bleu_score
 
 DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -29,7 +29,6 @@ def generate_summaries_or_translations(
     batch_size: int = 8,
     device: str = DEFAULT_DEVICE,
     fp16=False,
-    task="summarization",
     **gen_kwargs,
 ) -> None:
     fout = Path(out_file).open("w", encoding="utf-8")
@@ -41,16 +40,15 @@ def generate_summaries_or_translations(
     tokenizer = AutoTokenizer.from_pretrained(model_name)
 
     # update config with summarization specific params
-    use_task_specific_params(model, task)
+    use_task_specific_params(model, "summarization")
 
     for batch in tqdm(list(chunks(examples, batch_size))):
         if "t5" in model_name:
             batch = [model.config.prefix + text for text in batch]
-        batch = tokenizer(batch, max_length=1024, 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, **gen_kwargs)
+        batch = tokenizer.batch_encode_plus(
+            batch, max_length=1024, return_tensors="pt", truncation=True, pad_to_max_length=True
+        ).to(device)
+        summaries = model.generate(**batch, **gen_kwargs)
         dec = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False)
         for hypothesis in dec:
             fout.write(hypothesis + "\n")
@@ -59,42 +57,30 @@ def generate_summaries_or_translations(
 
 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("output_path", type=str, help="where to save summaries")
+    parser.add_argument("model_name", type=str, help="like facebook/bart-large-cnn,t5-base, etc.")
     parser.add_argument("--reference_path", type=str, required=False, help="like cnn_dm/test_reference_summaries.txt")
     parser.add_argument("--score_path", type=str, required=False, help="where to save the rouge score in json format")
+    parser.add_argument("--metric", type=str, choices=["bleu", "rouge"], default="rouge")
     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(
-        "--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,
+        examples, args.output_path, args.model_name, batch_size=args.bs, device=args.device, fp16=args.fp16
     )
-    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+"))
+
+    output_lns = [x.rstrip() for x in open(args.output_path).readlines()]
+    scores = {}
+    if args.reference_path is not None:
+        score_fn = {"bleu": calculate_bleu_score, "rouge": calculate_rouge}[args.metric]
+        reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()]
+        scores: dict = score_fn(output_lns, reference_lns)
+        if args.score_path is not None:
+            json.dump(scores, open("score_path", "w+"))
     return scores
 
 

examples/seq2seq/test_seq2seq_examples.py

@@ -12,7 +12,6 @@ import torch
 from torch.utils.data import DataLoader
 
 from transformers import AutoTokenizer
-from transformers.testing_utils import require_multigpu
 
 from .distillation import distill_main, evaluate_checkpoint
 from .finetune import main
@@ -108,7 +107,7 @@ class TestSummarizationDistiller(unittest.TestCase):
         logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
         return cls
 
-    @require_multigpu
+    @unittest.skipUnless(torch.cuda.device_count() > 1, "skipping multiGPU test")
     def test_multigpu(self):
         updates = dict(no_teacher=True, freeze_encoder=True, gpus=2, sortish_sampler=False,)
         self._test_distiller_cli(updates)
@@ -194,12 +193,13 @@ class TestSummarizationDistiller(unittest.TestCase):
 
 @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"
+    tmp = Path(tempfile.gettempdir()) / "utest_generations_bart_sum.hypo"
+
+    output_file_name = Path(tempfile.gettempdir()) / "utest_output_bart_sum.hypo"
     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
+    _dump_articles(tmp, articles)
+    testargs = ["run_eval.py", str(tmp), str(output_file_name), model]  # TODO: test score_path
     with patch.object(sys, "argv", testargs):
         run_generate()
         assert Path(output_file_name).exists()
@@ -223,30 +223,10 @@ def test_finetune(model):
         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
+    main(args)
 
 
 @pytest.mark.parametrize(
@@ -259,12 +239,7 @@ def test_dataset(tok):
     max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
     trunc_target = 4
     train_dataset = SummarizationDataset(
-        tokenizer,
-        data_dir=tmp_dir,
-        type_path="train",
-        max_source_length=20,
-        max_target_length=trunc_target,
-        tgt_lang="ro_RO",
+        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:

examples/seq2seq/train_distilbart_cnn.sh

@@ -16,9 +16,9 @@ python finetune.py \
     --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 \
+    --data_dir $CNN_DIR \
     --model_name_or_path sshleifer/student_cnn_12_6 \
     --tokenizer_name facebook/bart-large \
-    --warmup_steps 500 \
     --output_dir distilbart-cnn-12-6 \
     $@
 

examples/seq2seq/train_distilbart_xsum.sh

@@ -16,6 +16,5 @@ python distillation.py \
   --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 \
   $@

examples/seq2seq/train_mbart_cc25_enro.sh

@@ -1,21 +0,0 @@
-#!/usr/bin/env bash
-export PYTHONPATH="../":"${PYTHONPATH}"
-
-python finetune.py \
-    --learning_rate=3e-5 \
-    --fp16 \
-    --gpus 1 \
-    --do_train \
-    --do_predict \
-    --val_check_interval 0.1 \
-    --n_val 500 \
-    --adam_eps 1e-06 \
-    --num_train_epochs 3 --src_lang en_XX --tgt_lang ro_RO \
-    --freeze_encoder --freeze_embeds --data_dir $ENRO_DIR \
-    --max_source_length=300 --max_target_length 300 --val_max_target_length=300 --test_max_target_length 300 \
-    --train_batch_size=$BS --eval_batch_size=$BS --gradient_accumulation_steps=$GAS \
-    --model_name_or_path facebook/mbart-large-cc25 \
-    --task translation \
-    --warmup_steps 500 \
-    --logger wandb --sortish_sampler \
-    $@

examples/seq2seq/utils.py

@@ -14,8 +14,6 @@ from torch import nn
 from torch.utils.data import Dataset, Sampler
 from tqdm import tqdm
 
-from transformers import BartTokenizer
-
 
 def encode_file(
     tokenizer,
@@ -27,7 +25,6 @@ def encode_file(
     prefix="",
     tok_name="",
 ):
-    extra_kw = {"add_prefix_space": True} if isinstance(tokenizer, BartTokenizer) else {}
     cache_path = Path(f"{data_path}_{tok_name}{max_length}.pt")
     if not overwrite_cache and cache_path.exists():
         try:
@@ -44,13 +41,13 @@ def encode_file(
     assert lns, f"found empty file at {data_path}"
     examples = []
     for text in tqdm(lns, desc=f"Tokenizing {data_path.name}"):
-        tokenized = tokenizer(
+        tokenized = tokenizer.batch_encode_plus(
             [text],
             max_length=max_length,
-            padding="max_length" if pad_to_max_length else None,
+            pad_to_max_length=pad_to_max_length,
+            add_prefix_space=True,
             truncation=True,
             return_tensors=return_tensors,
-            **extra_kw,
         )
         assert tokenized.input_ids.shape[1] == max_length
         examples.append(tokenized)
@@ -63,9 +60,8 @@ def lmap(f: Callable, x: Iterable) -> List:
     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 calculate_bleu_score(output_lns, refs_lns) -> dict:
+    return {"bleu": corpus_bleu(output_lns, [refs_lns]).score}
 
 
 def trim_batch(
@@ -90,14 +86,9 @@ class SummarizationDataset(Dataset):
         n_obs=None,
         overwrite_cache=False,
         prefix="",
-        src_lang=None,
-        tgt_lang=None,
     ):
         super().__init__()
-        # FIXME: the rstrip logic strips all the chars, it seems.
         tok_name = tokenizer.__class__.__name__.lower().rstrip("tokenizer")
-        if hasattr(tokenizer, "set_lang") and src_lang is not None:
-            tokenizer.set_lang(src_lang)  # HACK: only applies to mbart
         self.source = encode_file(
             tokenizer,
             os.path.join(data_dir, type_path + ".source"),
@@ -108,8 +99,7 @@ class SummarizationDataset(Dataset):
         )
         tgt_path = os.path.join(data_dir, type_path + ".target")
         if hasattr(tokenizer, "set_lang"):
-            assert tgt_lang is not None, "--tgt_lang must be passed to build a translation"
-            tokenizer.set_lang(tgt_lang)  # HACK: only applies to mbart
+            tokenizer.set_lang("ro_RO")  # HACK: only applies to mbart
         self.target = encode_file(
             tokenizer, tgt_path, max_target_length, overwrite_cache=overwrite_cache, tok_name=tok_name
         )
@@ -233,8 +223,8 @@ def get_git_info():
 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)
+def calculate_rouge(output_lns: List[str], reference_lns: List[str]) -> Dict:
+    scorer = rouge_scorer.RougeScorer(ROUGE_KEYS, use_stemmer=True)
     aggregator = scoring.BootstrapAggregator()
 
     for reference_ln, output_ln in zip(reference_lns, output_lns):

examples/text-classification/run_tf_glue.py

@@ -131,9 +131,9 @@ 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)

examples/text-classification/run_xnli.py

@@ -573,6 +573,10 @@ 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()`

examples/text-generation/run_generation.py

@@ -214,14 +214,8 @@ def main():
     if requires_preprocessing:
         prepare_input = PREPROCESSING_FUNCTIONS.get(args.model_type)
         preprocessed_prompt_text = prepare_input(args, model, tokenizer, prompt_text)
-
-        if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
-            tokenizer_kwargs = {"add_space_before_punct_symbol": True}
-        else:
-            tokenizer_kwargs = {}
-
         encoded_prompt = tokenizer.encode(
-            preprocessed_prompt_text, add_special_tokens=False, return_tensors="pt", **tokenizer_kwargs
+            preprocessed_prompt_text, add_special_tokens=False, return_tensors="pt", add_space_before_punct_symbol=True
         )
     else:
         encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors="pt")

examples/token-classification/run_ner.py

@@ -75,8 +75,7 @@ class DataTrainingArguments:
         metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}
     )
     labels: Optional[str] = field(
-        default=None,
-        metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."},
+        metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}
     )
     max_seq_length: int = field(
         default=128,

examples/token-classification/run_tf_ner.py

@@ -17,7 +17,6 @@
 
 import logging
 import os
-import warnings
 from dataclasses import dataclass, field
 from typing import Dict, List, Optional, Tuple
 
@@ -110,9 +109,9 @@ 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)
@@ -185,12 +184,7 @@ def main():
 
         for i in range(batch_size):
             for j in range(seq_len):
-                if label_ids[i, j] == -1:
-                    label_ids[i, j] = -100
-                    warnings.warn(
-                        "Using `-1` to mask the loss for the token is depreciated. Please use `-100` instead."
-                    )
-                if label_ids[i, j] != -100:
+                if label_ids[i, j] != -1:
                     out_label_list[i].append(label_map[label_ids[i][j]])
                     preds_list[i].append(label_map[preds[i][j]])
 

model_cards/MoseliMotsoehli/TswanaBert/README.md

@@ -1,74 +0,0 @@
----
-language: setswana
----
-
-# TswanaBert
-Pretrained model on the Tswana language using a masked language modeling (MLM) objective.
-
-## Model Description.
-TswanaBERT is a transformer model pre-trained on a corpus of Setswana in a self-supervised fashion by masking part of the input words and training to predict the masks by using byte-level tokens.
-
-## Intended uses & limitations
-The model can  be used for either masked language modeling or next word prediction. It can also be fine-tuned on a specific down-stream NLP application. 
-
-#### How to use
-
-```python
->>> from transformers import pipeline
->>> from transformers import AutoTokenizer, AutoModelWithLMHead
-
->>> tokenizer = AutoTokenizer.from_pretrained("MoseliMotsoehli/TswanaBert")
->>> model = AutoModelWithLMHead.from_pretrained("MoseliMotsoehli/TswanaBert")
->>> unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)
->>> unmasker("Ntshopotse <mask> e godile.")
-
-[{'score': 0.32749542593955994,
-  'sequence': '<s>Ntshopotse setse e godile.</s>',
-  'token': 538,
-  'token_str': 'Ġsetse'},
- {'score': 0.060260992497205734,
-  'sequence': '<s>Ntshopotse le e godile.</s>',
-  'token': 270,
-  'token_str': 'Ġle'},
- {'score': 0.058460816740989685,
-  'sequence': '<s>Ntshopotse bone e godile.</s>',
-  'token': 364,
-  'token_str': 'Ġbone'},
- {'score': 0.05694682151079178,
-  'sequence': '<s>Ntshopotse ga e godile.</s>',
-  'token': 298,
-  'token_str': 'Ġga'},
- {'score': 0.0565204992890358,
-  'sequence': '<s>Ntshopotse, e godile.</s>',
-  'token': 16,
-  'token_str': ','}]
-```
-
-#### Limitations and bias
-The model is trained on a relatively small collection of setwana, mostly from news articles and creative writtings, and so is not representative enough of the language as yet.
-
-## Training data
-
-1. The largest portion of this dataset (10k)  sentences of text, comes from the [Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download)
-
-2. I Then added SABC news headlines collected by Marivate Vukosi, & Sefara Tshephisho, (2020)  that is generously made available on [zenoodo](http://doi.org/10.5281/zenodo.3668495 ). This added 185 tswana sentences to my corpus. 
-
-3. I went on to add 300 more sentences by scrapping following news sites and blogs that mosty originate in Botswana. I actively continue to expand the dataset.
-
-* http://setswana.blogspot.com/
-* https://omniglot.com/writing/tswana.php
-* http://www.dailynews.gov.bw/
-* http://www.mmegi.bw/index.php
-* https://tsena.co.bw
-* http://www.botswana.co.za/Cultural_Issues-travel/botswana-country-guide-en-route.html
-* https://www.poemhunter.com/poem/2013-setswana/
-https://www.poemhunter.com/poem/ngwana-wa-mosetsana/
- 
-
-### BibTeX entry and citation info
-
-```bibtex
-@inproceedings{author = {Moseli Motsoehli},
-  year={2020}
-}
-```

model_cards/MoseliMotsoehli/zuBERTa/README.md

@@ -1,56 +0,0 @@
----
-language: zulu
----
-
-# zuBERTa
-zuBERTa is a RoBERTa style transformer language model trained on zulu text.
-
-## Intended uses & limitations
-The model can be used for getting embeddings to use on a down-stream task such as question answering.
-
-#### How to use
-
-```python
->>> from transformers import pipeline
->>> from transformers import AutoTokenizer, AutoModelWithLMHead
-
->>> tokenizer = AutoTokenizer.from_pretrained("MoseliMotsoehli/zuBERTa")
->>> model = AutoModelWithLMHead.from_pretrained("MoseliMotsoehli/zuBERTa")
->>> unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)
->>> unmasker("Abafika eNkandla bafika sebeholwa <mask> uMpongo kaZingelwayo.")
-
-[
-  {
-    "sequence": "<s>Abafika eNkandla bafika sebeholwa khona uMpongo kaZingelwayo.</s>",
-    "score": 0.050459690392017365,
-    "token": 555,
-    "token_str": "Ġkhona"
-  },
-  {
-    "sequence": "<s>Abafika eNkandla bafika sebeholwa inkosi uMpongo kaZingelwayo.</s>",
-    "score": 0.03668094798922539,
-    "token": 2321,
-    "token_str": "Ġinkosi"
-  },
-  {
-    "sequence": "<s>Abafika eNkandla bafika sebeholwa ubukhosi uMpongo kaZingelwayo.</s>",
-    "score": 0.028774697333574295,
-    "token": 5101,
-    "token_str": "Ġubukhosi"
-  }
-]
-```
-
-## Training data
-
-1. 30k sentences of text, came from the [Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download) of zulu 2018. These were collected from news articles and creative writtings. 
-2. ~7500 articles of human generated translations were scraped from the zulu [wikipedia](https://zu.wikipedia.org/wiki/Special:AllPages).
-
-### BibTeX entry and citation info
-
-```bibtex
-@inproceedings{author = {Moseli Motsoehli},
-  title = {Towards transformation of Southern African language models through transformers.},
-  year={2020}
-}
-```

model_cards/SparkBeyond/roberta-large-sts-b/README.md

@@ -40,7 +40,7 @@ def roberta_similarity_batches(to_predict):
   return similarity_scores
 
 def similarity_roberta(model, tokenizer, sent_pairs):
-  batch_token = tokenizer(sent_pairs, padding='max_length', truncation=True, max_length=500)
+  batch_token = tokenizer.batch_encode_plus(sent_pairs, pad_to_max_length=True, max_length=500)
   res = model(torch.tensor(batch_token['input_ids']).cuda(), attention_mask=torch.tensor(batch_token["attention_mask"]).cuda())  
   return res
 

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

@@ -60,7 +60,7 @@ tokenizer = BartTokenizer.from_pretrained('a-ware/bart-squadv2')
 model = BartForQuestionAnswering.from_pretrained('a-ware/bart-squadv2')
 
 question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
-encoding = tokenizer(question, text, return_tensors='pt')
+encoding = tokenizer.encode_plus(question, text, return_tensors='pt')
 input_ids = encoding['input_ids']
 attention_mask = encoding['attention_mask']
 

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

@@ -43,7 +43,7 @@ tokenizer = XLMRobertaTokenizer.from_pretrained('a-ware/xlmroberta-squadv2')
 model = XLMRobertaForQuestionAnswering.from_pretrained('a-ware/xlmroberta-squadv2')
 
 question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
-encoding = tokenizer(question, text, return_tensors='pt')
+encoding = tokenizer.encode_plus(question, text, return_tensors='pt')
 input_ids = encoding['input_ids']
 attention_mask = encoding['attention_mask']
 

model_cards/asafaya/bert-large-arabic/README.md

@@ -1,59 +0,0 @@
----
-language: arabic
----
-
-# Arabic BERT Large Model
-
-Pretrained BERT Large language model for Arabic
-
-_If you use this model in your work, please cite this paper (to appear in 2020):_
-
-```
-@inproceedings{
-  title={KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media},
-  author={Safaya, Ali and Abdullatif, Moutasem and Yuret, Deniz},
-  booktitle={Proceedings of the International Workshop on Semantic Evaluation (SemEval)},
-  year={2020}
-}
-```
-
-## Pretraining Corpus
-
-`arabic-bert-large` model was pretrained on ~8.2 Billion words:
-
-- Arabic version of [OSCAR](https://traces1.inria.fr/oscar/) - filtered from [Common Crawl](http://commoncrawl.org/)
-- Recent dump of Arabic [Wikipedia](https://dumps.wikimedia.org/backup-index.html)
-
-and other Arabic resources which sum up to ~95GB of text.
-
-__Notes on training data:__
-
-- Our final version of corpus contains some non-Arabic words inlines, which we did not remove from sentences since that would affect some tasks like NER.
-- Although non-Arabic characters were lowered as a preprocessing step, since Arabic characters does not have upper or lower case, there is no cased and uncased version of the model.
-- The corpus and vocabulary set are not restricted to Modern Standard Arabic, they contain some dialectical Arabic too.
-
-## Pretraining details
-
-- This model was trained using Google BERT's github [repository](https://github.com/google-research/bert) on a single TPU v3-8 provided for free from [TFRC](https://www.tensorflow.org/tfrc).
-- Our pretraining procedure follows training settings of bert with some changes: trained for 3M training steps with batchsize of 128, instead of 1M with batchsize of 256.
-
-## Load Pretrained Model
-
-You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:  
-
-```python
-from transformers import AutoTokenizer, AutoModel
-
-tokenizer = AutoTokenizer.from_pretrained("asafaya/bert-large-arabic")
-model = AutoModel.from_pretrained("asafaya/bert-large-arabic")
-```
-
-## Results
-
-For further details on the models performance or any other queries, please refer to [Arabic-BERT](https://github.com/alisafaya/Arabic-BERT)
-
-## Acknowledgement
-
-Thanks to Google for providing free TPU for the training process and for Huggingface for hosting this model on their servers 😊
-
-

model_cards/asafaya/bert-medium-arabic/README.md

@@ -1,59 +0,0 @@
----
-language: arabic
----
-
-# Arabic BERT Medium Model
-
-Pretrained BERT Medium language model for Arabic
-
-_If you use this model in your work, please cite this paper (to appear in 2020):_
-
-```
-@inproceedings{
-  title={KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media},
-  author={Safaya, Ali and Abdullatif, Moutasem and Yuret, Deniz},
-  booktitle={Proceedings of the International Workshop on Semantic Evaluation (SemEval)},
-  year={2020}
-}
-```
-
-## Pretraining Corpus
-
-`arabic-bert-medium` model was pretrained on ~8.2 Billion words:
-
-- Arabic version of [OSCAR](https://traces1.inria.fr/oscar/) - filtered from [Common Crawl](http://commoncrawl.org/)
-- Recent dump of Arabic [Wikipedia](https://dumps.wikimedia.org/backup-index.html)
-
-and other Arabic resources which sum up to ~95GB of text.
-
-__Notes on training data:__
-
-- Our final version of corpus contains some non-Arabic words inlines, which we did not remove from sentences since that would affect some tasks like NER.
-- Although non-Arabic characters were lowered as a preprocessing step, since Arabic characters does not have upper or lower case, there is no cased and uncased version of the model.
-- The corpus and vocabulary set are not restricted to Modern Standard Arabic, they contain some dialectical Arabic too.
-
-## Pretraining details
-
-- This model was trained using Google BERT's github [repository](https://github.com/google-research/bert) on a single TPU v3-8 provided for free from [TFRC](https://www.tensorflow.org/tfrc).
-- Our pretraining procedure follows training settings of bert with some changes: trained for 3M training steps with batchsize of 128, instead of 1M with batchsize of 256.
-
-## Load Pretrained Model
-
-You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:  
-
-```python
-from transformers import AutoTokenizer, AutoModel
-
-tokenizer = AutoTokenizer.from_pretrained("asafaya/bert-medium-arabic")
-model = AutoModel.from_pretrained("asafaya/bert-medium-arabic")
-```
-
-## Results
-
-For further details on the models performance or any other queries, please refer to [Arabic-BERT](https://github.com/alisafaya/Arabic-BERT)
-
-## Acknowledgement
-
-Thanks to Google for providing free TPU for the training process and for Huggingface for hosting this model on their servers 😊
-
-

model_cards/asafaya/bert-mini-arabic/README.md

@@ -1,61 +0,0 @@
----
-language: arabic
-datasets:
-- oscar
-- wikipedia
----
-
-# Arabic BERT Mini Model
-
-Pretrained BERT Mini language model for Arabic
-
-_If you use this model in your work, please cite this paper (to appear in 2020):_
-
-```
-@inproceedings{
-  title={KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media},
-  author={Safaya, Ali and Abdullatif, Moutasem and Yuret, Deniz},
-  booktitle={Proceedings of the International Workshop on Semantic Evaluation (SemEval)},
-  year={2020}
-}
-```
-
-## Pretraining Corpus
-
-`arabic-bert-mini` model was pretrained on ~8.2 Billion words:
-
-- Arabic version of [OSCAR](https://traces1.inria.fr/oscar/) - filtered from [Common Crawl](http://commoncrawl.org/)
-- Recent dump of Arabic [Wikipedia](https://dumps.wikimedia.org/backup-index.html)
-
-and other Arabic resources which sum up to ~95GB of text.
-
-__Notes on training data:__
-
-- Our final version of corpus contains some non-Arabic words inlines, which we did not remove from sentences since that would affect some tasks like NER.
-- Although non-Arabic characters were lowered as a preprocessing step, since Arabic characters does not have upper or lower case, there is no cased and uncased version of the model.
-- The corpus and vocabulary set are not restricted to Modern Standard Arabic, they contain some dialectical Arabic too.
-
-## Pretraining details
-
-- This model was trained using Google BERT's github [repository](https://github.com/google-research/bert) on a single TPU v3-8 provided for free from [TFRC](https://www.tensorflow.org/tfrc).
-- Our pretraining procedure follows training settings of bert with some changes: trained for 3M training steps with batchsize of 128, instead of 1M with batchsize of 256.
-
-## Load Pretrained Model
-
-You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:  
-
-```python
-from transformers import AutoTokenizer, AutoModel
-
-tokenizer = AutoTokenizer.from_pretrained("asafaya/bert-mini-arabic")
-model = AutoModel.from_pretrained("asafaya/bert-mini-arabic")
-```
-
-## Results
-
-For further details on the models performance or any other queries, please refer to [Arabic-BERT](https://github.com/alisafaya/Arabic-BERT)
-
-## Acknowledgement
-
-Thanks to Google for providing free TPU for the training process and for Huggingface for hosting this model on their servers 😊
-

model_cards/bert-base-cased-README.md

@@ -1,230 +1,10 @@
 ---
-language: english
 tags:
 - exbert
+
 license: apache-2.0
-datasets:
-- bookcorpus
-- wikipedia
 ---
 
-# BERT base model (cased)
-
-Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
-[this paper](https://arxiv.org/abs/1810.04805) and first released in
-[this repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a difference between
-english and English.
-
-Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
-the Hugging Face team.
-
-## Model description
-
-BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
-was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
-publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
-was pretrained with two objectives:
-
-- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
-  the entire masked sentence through the model and has to predict the masked words. This is different from traditional
-  recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
-  GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
-  sentence.
-- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
-  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
-  predict if the two sentences were following each other or not.
-
-This way, the model learns an inner representation of the English language that can then be used to extract features
-useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
-classifier using the features produced by the BERT model as inputs.
-
-## Intended uses & limitations
-
-You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
-be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
-fine-tuned versions on a task that interests you.
-
-Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
-to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
-generation you should look at model like GPT2.
-
-### How to use
-
-You can use this model directly with a pipeline for masked language modeling:
-
-```python
->>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='bert-base-cased')
->>> unmasker("Hello I'm a [MASK] model.")
-
-[{'sequence': "[CLS] Hello I'm a fashion model. [SEP]",
-  'score': 0.09019174426794052,
-  'token': 4633,
-  'token_str': 'fashion'},
- {'sequence': "[CLS] Hello I'm a new model. [SEP]",
-  'score': 0.06349995732307434,
-  'token': 1207,
-  'token_str': 'new'},
- {'sequence': "[CLS] Hello I'm a male model. [SEP]",
-  'score': 0.06228214129805565,
-  'token': 2581,
-  'token_str': 'male'},
- {'sequence': "[CLS] Hello I'm a professional model. [SEP]",
-  'score': 0.0441727414727211,
-  'token': 1848,
-  'token_str': 'professional'},
- {'sequence': "[CLS] Hello I'm a super model. [SEP]",
-  'score': 0.03326151892542839,
-  'token': 7688,
-  'token_str': 'super'}]
-```
-
-Here is how to use this model to get the features of a given text in PyTorch:
-
-```python
-from transformers import BertTokenizer, TFBertModel
-tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
-model = TFBertModel.from_pretrained("bert-base-cased")
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='pt')
-output = model(**encoded_input)
-```
-
-and in TensorFlow:
-
-```python
-from transformers import BertTokenizer, BertModel
-tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
-model = BertModel.from_pretrained("bert-base-cased")
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='tf')
-output = model(encoded_input)
-```
-
-### Limitations and bias
-
-Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
-predictions:
-
-```python
->>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='bert-base-cased')
->>> unmasker("The man worked as a [MASK].")
-
-[{'sequence': '[CLS] The man worked as a lawyer. [SEP]',
-  'score': 0.04804691672325134,
-  'token': 4545,
-  'token_str': 'lawyer'},
- {'sequence': '[CLS] The man worked as a waiter. [SEP]',
-  'score': 0.037494491785764694,
-  'token': 17989,
-  'token_str': 'waiter'},
- {'sequence': '[CLS] The man worked as a cop. [SEP]',
-  'score': 0.035512614995241165,
-  'token': 9947,
-  'token_str': 'cop'},
- {'sequence': '[CLS] The man worked as a detective. [SEP]',
-  'score': 0.031271643936634064,
-  'token': 9140,
-  'token_str': 'detective'},
- {'sequence': '[CLS] The man worked as a doctor. [SEP]',
-  'score': 0.027423162013292313,
-  'token': 3995,
-  'token_str': 'doctor'}]
-
->>> unmasker("The woman worked as a [MASK].")
-
-[{'sequence': '[CLS] The woman worked as a nurse. [SEP]',
-  'score': 0.16927455365657806,
-  'token': 7439,
-  'token_str': 'nurse'},
- {'sequence': '[CLS] The woman worked as a waitress. [SEP]',
-  'score': 0.1501094549894333,
-  'token': 15098,
-  'token_str': 'waitress'},
- {'sequence': '[CLS] The woman worked as a maid. [SEP]',
-  'score': 0.05600163713097572,
-  'token': 13487,
-  'token_str': 'maid'},
- {'sequence': '[CLS] The woman worked as a housekeeper. [SEP]',
-  'score': 0.04838843643665314,
-  'token': 26458,
-  'token_str': 'housekeeper'},
- {'sequence': '[CLS] The woman worked as a cook. [SEP]',
-  'score': 0.029980547726154327,
-  'token': 9834,
-  'token_str': 'cook'}]
-```
-
-This bias will also affect all fine-tuned versions of this model.
-
-## Training data
-
-The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
-unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
-headers).
-
-## Training procedure
-
-### Preprocessing
-
-The texts are tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form:
-
-```
-[CLS] Sentence A [SEP] Sentence B [SEP]
-```
-
-With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
-the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
-consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
-"sentences" has a combined length of less than 512 tokens.
-
-The details of the masking procedure for each sentence are the following:
-- 15% of the tokens are masked.
-- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
-- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
-- In the 10% remaining cases, the masked tokens are left as is.
-
-### Pretraining
-
-The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
-of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
-used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
-learning rate warmup for 10,000 steps and linear decay of the learning rate after.
-
-## Evaluation results
-
-When fine-tuned on downstream tasks, this model achieves the following results:
-
-Glue test results:
-
-| Task | MNLI-(m/mm) | QQP  | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE  | Average |
-|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
-|      | 84.6/83.4   | 71.2 | 90.5 | 93.5  | 52.1 | 85.8  | 88.9 | 66.4 | 79.6    |
-
-
-### BibTeX entry and citation info
-
-```bibtex
-@article{DBLP:journals/corr/abs-1810-04805,
-  author    = {Jacob Devlin and
-               Ming{-}Wei Chang and
-               Kenton Lee and
-               Kristina Toutanova},
-  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
-               Understanding},
-  journal   = {CoRR},
-  volume    = {abs/1810.04805},
-  year      = {2018},
-  url       = {http://arxiv.org/abs/1810.04805},
-  archivePrefix = {arXiv},
-  eprint    = {1810.04805},
-  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-```
-
 <a href="https://huggingface.co/exbert/?model=bert-base-cased">
 	<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
 </a>

model_cards/bert-base-multilingual-cased-README.md

@@ -1,152 +1,5 @@
 ---
 language: multilingual
+
 license: apache-2.0
-datasets:
-- wikipedia
 ---
-
-# BERT multilingual base model (uncased)
-
-Pretrained model on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
-It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in
-[this repository](https://github.com/google-research/bert). This model is case sensitive: it makes a difference
-between english and English.
-
-Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
-the Hugging Face team.
-
-## Model description
-
-BERT is a transformers model pretrained on a large corpus of multilingual data in a self-supervised fashion. This means
-it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
-publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
-was pretrained with two objectives:
-
-- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
-  the entire masked sentence through the model and has to predict the masked words. This is different from traditional
-  recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
-  GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
-  sentence.
-- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
-  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
-  predict if the two sentences were following each other or not.
-
-This way, the model learns an inner representation of the languages in the training set that can then be used to
-extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a
-standard classifier using the features produced by the BERT model as inputs.
-
-## Intended uses & limitations
-
-You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
-be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
-fine-tuned versions on a task that interests you.
-
-Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
-to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
-generation you should look at model like GPT2.
-
-### How to use
-
-You can use this model directly with a pipeline for masked language modeling:
-
-```python
->>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='bert-base-multilingual-cased')
->>> unmasker("Hello I'm a [MASK] model.")
-
-[{'sequence': "[CLS] Hello I'm a model model. [SEP]",
-  'score': 0.10182085633277893,
-  'token': 13192,
-  'token_str': 'model'},
- {'sequence': "[CLS] Hello I'm a world model. [SEP]",
-  'score': 0.052126359194517136,
-  'token': 11356,
-  'token_str': 'world'},
- {'sequence': "[CLS] Hello I'm a data model. [SEP]",
-  'score': 0.048930276185274124,
-  'token': 11165,
-  'token_str': 'data'},
- {'sequence': "[CLS] Hello I'm a flight model. [SEP]",
-  'score': 0.02036019042134285,
-  'token': 23578,
-  'token_str': 'flight'},
- {'sequence': "[CLS] Hello I'm a business model. [SEP]",
-  'score': 0.020079681649804115,
-  'token': 14155,
-  'token_str': 'business'}]
-```
-
-Here is how to use this model to get the features of a given text in PyTorch:
-
-```python
-from transformers import BertTokenizer, BertModel
-tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')
-model = BertModel.from_pretrained("bert-base-multilingual-cased")
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='pt')
-output = model(**encoded_input)
-```
-
-and in TensorFlow:
-
-```python
-from transformers import BertTokenizer, TFBertModel
-tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')
-model = TFBertModel.from_pretrained("bert-base-multilingual-cased")
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='tf')
-output = model(encoded_input)
-```
-
-## Training data
-
-The BERT model was pretrained on the 104 languages with the largest Wikipedias. You can find the complete list
-[here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
-
-## Training procedure
-
-### Preprocessing
-
-The texts are lowercased and tokenized using WordPiece and a shared vocabulary size of 110,000. The languages with a
-larger Wikipedia are under-sampled and the ones with lower resources are oversampled. For languages like Chinese,
-Japanese Kanji and Korean Hanja that don't have space, a CJK Unicode block is added around every character. 
-
-The inputs of the model are then of the form:
-
-```
-[CLS] Sentence A [SEP] Sentence B [SEP]
-```
-
-With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
-the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
-consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
-"sentences" has a combined length of less than 512 tokens.
-
-The details of the masking procedure for each sentence are the following:
-- 15% of the tokens are masked.
-- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
-- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
-- In the 10% remaining cases, the masked tokens are left as is.
-
-
-### BibTeX entry and citation info
-
-```bibtex
-@article{DBLP:journals/corr/abs-1810-04805,
-  author    = {Jacob Devlin and
-               Ming{-}Wei Chang and
-               Kenton Lee and
-               Kristina Toutanova},
-  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
-               Understanding},
-  journal   = {CoRR},
-  volume    = {abs/1810.04805},
-  year      = {2018},
-  url       = {http://arxiv.org/abs/1810.04805},
-  archivePrefix = {arXiv},
-  eprint    = {1810.04805},
-  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-```

model_cards/bert-base-multilingual-uncased-README.md

@@ -1,209 +1,5 @@
 ---
-language: english
+language: multilingual
+
 license: apache-2.0
-datasets:
-- wikipedia
 ---
-
-# BERT multilingual base model (uncased)
-
-Pretrained model on the top 102 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
-It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in
-[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
-between english and English.
-
-Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
-the Hugging Face team.
-
-## Model description
-
-BERT is a transformers model pretrained on a large corpus of multilingual data in a self-supervised fashion. This means
-it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
-publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
-was pretrained with two objectives:
-
-- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
-  the entire masked sentence through the model and has to predict the masked words. This is different from traditional
-  recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
-  GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
-  sentence.
-- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
-  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
-  predict if the two sentences were following each other or not.
-
-This way, the model learns an inner representation of the languages in the training set that can then be used to
-extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a
-standard classifier using the features produced by the BERT model as inputs.
-
-## Intended uses & limitations
-
-You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
-be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
-fine-tuned versions on a task that interests you.
-
-Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
-to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
-generation you should look at model like GPT2.
-
-### How to use
-
-You can use this model directly with a pipeline for masked language modeling:
-
-```python
->>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='bert-base-multilingual-uncased')
->>> unmasker("Hello I'm a [MASK] model.")
-
-[{'sequence': "[CLS] hello i'm a top model. [SEP]",
-  'score': 0.1507750153541565,
-  'token': 11397,
-  'token_str': 'top'},
- {'sequence': "[CLS] hello i'm a fashion model. [SEP]",
-  'score': 0.13075384497642517,
-  'token': 23589,
-  'token_str': 'fashion'},
- {'sequence': "[CLS] hello i'm a good model. [SEP]",
-  'score': 0.036272723227739334,
-  'token': 12050,
-  'token_str': 'good'},
- {'sequence': "[CLS] hello i'm a new model. [SEP]",
-  'score': 0.035954564809799194,
-  'token': 10246,
-  'token_str': 'new'},
- {'sequence': "[CLS] hello i'm a great model. [SEP]",
-  'score': 0.028643041849136353,
-  'token': 11838,
-  'token_str': 'great'}]
-```
-
-Here is how to use this model to get the features of a given text in PyTorch:
-
-```python
-from transformers import BertTokenizer, BertModel
-tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-uncased')
-model = BertModel.from_pretrained("bert-base-multilingual-uncased")
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='pt')
-output = model(**encoded_input)
-```
-
-and in TensorFlow:
-
-```python
-from transformers import BertTokenizer, TFBertModel
-tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-uncased')
-model = TFBertModel.from_pretrained("bert-base-multilingual-uncased")
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='tf')
-output = model(encoded_input)
-```
-
-### Limitations and bias
-
-Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
-predictions:
-
-```python
->>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='bert-base-multilingual-uncased')
->>> unmasker("The man worked as a [MASK].")
-
-[{'sequence': '[CLS] the man worked as a teacher. [SEP]',
-  'score': 0.07943806052207947,
-  'token': 21733,
-  'token_str': 'teacher'},
- {'sequence': '[CLS] the man worked as a lawyer. [SEP]',
-  'score': 0.0629938617348671,
-  'token': 34249,
-  'token_str': 'lawyer'},
- {'sequence': '[CLS] the man worked as a farmer. [SEP]',
-  'score': 0.03367974981665611,
-  'token': 36799,
-  'token_str': 'farmer'},
- {'sequence': '[CLS] the man worked as a journalist. [SEP]',
-  'score': 0.03172805905342102,
-  'token': 19477,
-  'token_str': 'journalist'},
- {'sequence': '[CLS] the man worked as a carpenter. [SEP]',
-  'score': 0.031021825969219208,
-  'token': 33241,
-  'token_str': 'carpenter'}]
-
->>> unmasker("The Black woman worked as a [MASK].")
-
-[{'sequence': '[CLS] the black woman worked as a nurse. [SEP]',
-  'score': 0.07045423984527588,
-  'token': 52428,
-  'token_str': 'nurse'},
- {'sequence': '[CLS] the black woman worked as a teacher. [SEP]',
-  'score': 0.05178029090166092,
-  'token': 21733,
-  'token_str': 'teacher'},
- {'sequence': '[CLS] the black woman worked as a lawyer. [SEP]',
-  'score': 0.032601192593574524,
-  'token': 34249,
-  'token_str': 'lawyer'},
- {'sequence': '[CLS] the black woman worked as a slave. [SEP]',
-  'score': 0.030507225543260574,
-  'token': 31173,
-  'token_str': 'slave'},
- {'sequence': '[CLS] the black woman worked as a woman. [SEP]',
-  'score': 0.027691684663295746,
-  'token': 14050,
-  'token_str': 'woman'}]
-```
-
-This bias will also affect all fine-tuned versions of this model.
-
-## Training data
-
-The BERT model was pretrained on the 102 languages with the largest Wikipedias. You can find the complete list
-[here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
-
-## Training procedure
-
-### Preprocessing
-
-The texts are lowercased and tokenized using WordPiece and a shared vocabulary size of 110,000. The languages with a
-larger Wikipedia are under-sampled and the ones with lower resources are oversampled. For languages like Chinese,
-Japanese Kanji and Korean Hanja that don't have space, a CJK Unicode block is added around every character. 
-
-The inputs of the model are then of the form:
-
-```
-[CLS] Sentence A [SEP] Sentence B [SEP]
-```
-
-With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
-the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
-consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
-"sentences" has a combined length of less than 512 tokens.
-
-The details of the masking procedure for each sentence are the following:
-- 15% of the tokens are masked.
-- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
-- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
-- In the 10% remaining cases, the masked tokens are left as is.
-
-
-### BibTeX entry and citation info
-
-```bibtex
-@article{DBLP:journals/corr/abs-1810-04805,
-  author    = {Jacob Devlin and
-               Ming{-}Wei Chang and
-               Kenton Lee and
-               Kristina Toutanova},
-  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
-               Understanding},
-  journal   = {CoRR},
-  volume    = {abs/1810.04805},
-  year      = {2018},
-  url       = {http://arxiv.org/abs/1810.04805},
-  archivePrefix = {arXiv},
-  eprint    = {1810.04805},
-  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-```

model_cards/bert-base-uncased-README.md

@@ -93,9 +93,9 @@ output = model(**encoded_input)
 and in TensorFlow:
 
 ```python
-from transformers import BertTokenizer, TFBertModel
+from transformers import BertTokenizer, BertModel
 tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-model = TFBertModel.from_pretrained("bert-base-uncased")
+model = BertModel.from_pretrained("bert-base-uncased")
 text = "Replace me by any text you'd like."
 encoded_input = tokenizer(text, return_tensors='tf')
 output = model(encoded_input)

model_cards/chrisliu298/arxiv_ai_gpt2/README.md

@@ -1,65 +0,0 @@
----
-language: "en"
-tags:
-- gpt2
-- arxiv
-- transformers
-datasets:
-- https://github.com/staeiou/arxiv_archive/tree/v1.0.1
----
-
-# ArXiv AI GPT-2
-
-## Model description
-
-This GPT-2 (774M) model is capable of generating abstracts given paper titles. It was trained using all research paper titles and abstracts under artificial intelligence (AI), machine learning (LG), computation and language (CL), and computer vision and pattern recognition (CV) on arXiv.
-
-## Intended uses & limitations
-
-#### How to use
-
-To generate paper abstracts, use the provided `generate.py` [here](https://gist.github.com/chrisliu298/ccb8144888eace069da64ad3e6472d64). This is very similar to the HuggingFace's `run_generation.py` [here](https://github.com/huggingface/transformers/tree/master/examples/text-generation). You can simply replace the text with with your own model path (line 89) and change the input string to your paper title (line 127). If you want to use your own script, make sure to prepend `<|startoftext|> ` at the front and append ` <|sep|>` at the end of the paper title.
-
-## Training data
-I selected a subset of the [arXiv Archive](https://github.com/staeiou/arxiv_archive) dataset (Geiger, 2019) as the training and evaluation data to fine-tune GPT-2. The original arXiv Archive dataset contains a full archive of metadata about papers on arxiv.org, from the start of the site in 1993 to the end of 2019. Our subset includes all the paper titles (query) and abstracts (context) under the Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Computation and Language (cs.CL), and Computer Vision and Pattern Recognition (cs.CV) categories. I provide the information  of the sub-dataset and the distribution of the training and evaluation dataset as follows.
-
-
-|   Splits   |   Count    | Percentage (%) | BPE Token Count |
-| :--------: | :--------: | :------------: | :-------------: |
-|   Train    |   90,000   |     90.11      |   20,834,012    |
-| Validation |    4,940   |      4.95      |    1,195,056    |
-|    Test    |    4,940   |      4.95      |    1,218,754    |
-| **Total**  | **99,880** |    **100**     | **23,247,822**  |
-
-The original dataset is in the format of a tab-separated value, so we wrote a simple preprocessing script to convert it into a text file format, which is the input file type (a document) of the GPT-2 model. An example of a paper’s title and its abstract is shown below.
-
-```text
-<|startoftext|> Some paper title <|sep|> Some paper abstract <|endoftext|>
-```
-
-Because there are a lot of cross-domain papers in the dataset, I deduplicate the dataset using the arXiv ID, which is unique for every paper. I sort the paper by submission date, by doing so, one can examine GPT-2’s ability to use learned terminologies when it is prompted with paper titles from the “future.”
-
-
-## Training procedure
-
-I used block size = 512, batch size = 1, gradidnet accumulation = 1, learning rate = 1e-5, epochs = 5, and everything else follows the default model configuration.
-
-## Eval results
-
-The resulting GPT-2 large model's perplexity score on the test set is **14.9413**.
-
-## Reference
-
-```bibtex
-@dataset{r_stuart_geiger_2019_2533436,
-    author= {R. Stuart Geiger},
-    title={{ArXiV Archive: A tidy and complete archive of metadata for papers on arxiv.org, 1993-2019}},
-    month=jan,
-    year= 2019,
-    publisher={Zenodo},
-    version= {v1.0.1},
-    doi={10.5281/zenodo.2533436},
-    url={https://doi.org/10.5281/zenodo.2533436}
-}
-```
-

model_cards/distilbert-base-uncased-README.md

@@ -1,218 +1,10 @@
 ---
-language: english
 tags:
 - exbert
+
 license: apache-2.0
-datasets:
-- bookcorpus
-- wikipedia
 ---
 
-# DistilBERT base model (uncased)
-
-This model is a distilled version of the [BERT base mode](https://huggingface.co/distilbert-base-uncased). It was
-introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found
-[here](https://github.com/huggingface/transformers/tree/master/examples/distillation). This model is uncased: it does
-not make a difference between english and English.
-
-## Model description
-
-DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
-self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
-with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic
-process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained
-with three objectives:
-
-- Distillation loss: the model was trained to return the same probabilities as the BERT base model.
-- Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a
-  sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the
-  model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that
-  usually see the words one after the other, or from autoregressive models like GPT which internally mask the future
-  tokens. It allows the model to learn a bidirectional representation of the sentence.
-- Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base
-  model.
-
-This way, the model learns the same inner representation of the English language than its teacher model, while being
-faster for inference or downstream tasks.
-
-## Intended uses & limitations
-
-You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
-be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=distilbert) to look for
-fine-tuned versions on a task that interests you.
-
-Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
-to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
-generation you should look at model like GPT2.
-
-### How to use
-
-You can use this model directly with a pipeline for masked language modeling:
-
-```python
->>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased')
->>> unmasker("Hello I'm a [MASK] model.")
-
-[{'sequence': "[CLS] hello i'm a role model. [SEP]",
-  'score': 0.05292855575680733,
-  'token': 2535,
-  'token_str': 'role'},
- {'sequence': "[CLS] hello i'm a fashion model. [SEP]",
-  'score': 0.03968575969338417,
-  'token': 4827,
-  'token_str': 'fashion'},
- {'sequence': "[CLS] hello i'm a business model. [SEP]",
-  'score': 0.034743521362543106,
-  'token': 2449,
-  'token_str': 'business'},
- {'sequence': "[CLS] hello i'm a model model. [SEP]",
-  'score': 0.03462274372577667,
-  'token': 2944,
-  'token_str': 'model'},
- {'sequence': "[CLS] hello i'm a modeling model. [SEP]",
-  'score': 0.018145186826586723,
-  'token': 11643,
-  'token_str': 'modeling'}]
-```
-
-Here is how to use this model to get the features of a given text in PyTorch:
-
-```python
-from transformers import DistilBertTokenizer, DistilBertModel
-tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
-model = DistilBertModel.from_pretrained("distilbert-base-uncased")
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='pt')
-output = model(**encoded_input)
-```
-
-and in TensorFlow:
-
-```python
-from transformers import DistilBertTokenizer, TFDistilBertModel
-tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
-model = TFDistilBertModel.from_pretrained("distilbert-base-uncased")
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='tf')
-output = model(encoded_input)
-```
-
-### Limitations and bias
-
-Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
-predictions. It also inherits some of
-[the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias). 
-
-```python
->>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased')
->>> unmasker("The White man worked as a [MASK].")
-
-[{'sequence': '[CLS] the white man worked as a blacksmith. [SEP]',
-  'score': 0.1235365942120552,
-  'token': 20987,
-  'token_str': 'blacksmith'},
- {'sequence': '[CLS] the white man worked as a carpenter. [SEP]',
-  'score': 0.10142576694488525,
-  'token': 10533,
-  'token_str': 'carpenter'},
- {'sequence': '[CLS] the white man worked as a farmer. [SEP]',
-  'score': 0.04985016956925392,
-  'token': 7500,
-  'token_str': 'farmer'},
- {'sequence': '[CLS] the white man worked as a miner. [SEP]',
-  'score': 0.03932540491223335,
-  'token': 18594,
-  'token_str': 'miner'},
- {'sequence': '[CLS] the white man worked as a butcher. [SEP]',
-  'score': 0.03351764753460884,
-  'token': 14998,
-  'token_str': 'butcher'}]
-
->>> unmasker("The Black woman worked as a [MASK].")
-
-[{'sequence': '[CLS] the black woman worked as a waitress. [SEP]',
-  'score': 0.13283951580524445,
-  'token': 13877,
-  'token_str': 'waitress'},
- {'sequence': '[CLS] the black woman worked as a nurse. [SEP]',
-  'score': 0.12586183845996857,
-  'token': 6821,
-  'token_str': 'nurse'},
- {'sequence': '[CLS] the black woman worked as a maid. [SEP]',
-  'score': 0.11708822101354599,
-  'token': 10850,
-  'token_str': 'maid'},
- {'sequence': '[CLS] the black woman worked as a prostitute. [SEP]',
-  'score': 0.11499975621700287,
-  'token': 19215,
-  'token_str': 'prostitute'},
- {'sequence': '[CLS] the black woman worked as a housekeeper. [SEP]',
-  'score': 0.04722772538661957,
-  'token': 22583,
-  'token_str': 'housekeeper'}]
-```
-
-This bias will also affect all fine-tuned versions of this model.
-
-## Training data
-
-DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset
-consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia)
-(excluding lists, tables and headers).
-
-## Training procedure
-
-### Preprocessing
-
-The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
-then of the form:
-
-```
-[CLS] Sentence A [SEP] Sentence B [SEP]
-```
-
-With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
-the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
-consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
-"sentences" has a combined length of less than 512 tokens.
-
-The details of the masking procedure for each sentence are the following:
-- 15% of the tokens are masked.
-- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
-- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
-- In the 10% remaining cases, the masked tokens are left as is.
-
-### Pretraining
-
-The model was trained on 8 16 GB V100 for 90 hours. See the
-[training code](https://github.com/huggingface/transformers/tree/master/examples/distillation) for all hyperparameters
-details.
-
-## Evaluation results
-
-When fine-tuned on downstream tasks, this model achieves the following results:
-
-Glue test results:
-
-| Task | MNLI | QQP  | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE  | Average |
-|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
-|      | 82.2 | 88.5 | 89.2 | 91.3  | 51.3 | 85.8  | 87.5 | 59.9 | 77.0    |
-
-
-### BibTeX entry and citation info
-
-```bibtex
-@article{Sanh2019DistilBERTAD,
-  title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
-  author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
-  journal={ArXiv},
-  year={2019},
-  volume={abs/1910.01108}
-}
-```
-
 <a href="https://huggingface.co/exbert/?model=distilbert-base-uncased">
 	<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
 </a>

model_cards/google/reformer-enwik8/README.md

@@ -14,7 +14,7 @@ Therefore, this model does not need a tokenizer. The following function can inst
 import torch
 
 # Encoding
-def encode(list_of_strings, pad_token_id=0):
+def encode(list_of_strings, pad_to_max_length=True, pad_token_id=0):
     max_length = max([len(string) for string in list_of_strings])
 
     # create emtpy tensors

model_cards/gpt2-README.md

@@ -13,9 +13,8 @@ Pretrained model on English language using a causal language modeling (CLM) obje
 [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
 and first released at [this page](https://openai.com/blog/better-language-models/).
 
-Disclaimer: The team releasing GPT-2 also wrote a
-[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
-has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
+Disclaimer: The team releasing GPT-2 did not write a model card for this model so this model card has been written by
+the Hugging Face team.
 
 ## Model description
 
@@ -80,19 +79,7 @@ output = model(encoded_input)
 ### Limitations and bias
 
 The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
-unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
-[model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):
-
-> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
-> that require the generated text to be true.
->
-> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
-> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
-> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
-> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
-> levels of caution around use cases that are sensitive to biases around human attributes.
-
-Here's an example of how the model can have biased predictions:
+unfiltered from the internet, which is far from neutral. Therefore, the model can have biased predictions:
 
 ```python
 >>> from transformers import pipeline, set_seed
@@ -123,8 +110,7 @@ This bias will also affect all fine-tuned versions of this model.
 The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
 pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
 this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
-40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
-[here](https://github.com/openai/gpt-2/blob/master/domains.txt).
+40GB of texts but has not been publicly released.
 
 ## Training procedure
 

model_cards/krevas/finance-koelectra-base-discriminator/README.md

@@ -1,45 +0,0 @@
----
-language: korean
----
-
-# 📈 Financial Korean ELECTRA model
-
-Pretrained ELECTRA Language Model for Korean (`finance-koelectra-base-discriminator`)
-
-> ELECTRA is a new method for self-supervised language representation learning. It can be used to
-> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
-> distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to
-> the discriminator of a GAN.
-
-More details about ELECTRA can be found in the [ICLR paper](https://openreview.net/forum?id=r1xMH1BtvB)
-or in the [official ELECTRA repository](https://github.com/google-research/electra) on GitHub.
-
-## Stats
-
-The current version of the model is trained on a financial news data of Naver news.
-
-The final training corpus has a size of 25GB and 2.3B tokens.
-
-This model was trained a cased model on a TITAN RTX for 500k steps.
-
-## Usage
-
-```python
-from transformers import ElectraForPreTraining, ElectraTokenizer
-import torch
-discriminator = ElectraForPreTraining.from_pretrained("krevas/finance-koelectra-base-discriminator")
-tokenizer = ElectraTokenizer.from_pretrained("krevas/finance-koelectra-base-discriminator")
-sentence = "내일 해당 종목이 대폭 상승할 것이다"
-fake_sentence = "내일 해당 종목이 맛있게 상승할 것이다"
-fake_tokens = tokenizer.tokenize(fake_sentence)
-fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
-discriminator_outputs = discriminator(fake_inputs)
-predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
-[print("%7s" % token, end="") for token in fake_tokens]
-[print("%7s" % int(prediction), end="") for prediction in predictions.tolist()[1:-1]]
-print("fake token : %s" % fake_tokens[predictions.tolist()[1:-1].index(1)])
-```
-
-# Huggingface model hub
-
-All models are available on the [Huggingface model hub](https://huggingface.co/krevas).

model_cards/krevas/finance-koelectra-base-generator/README.md

@@ -1,41 +0,0 @@
----
-language: korean
----
-
-# 📈 Financial Korean ELECTRA model
-
-Pretrained ELECTRA Language Model for Korean (`finance-koelectra-base-generator`)
-
-> ELECTRA is a new method for self-supervised language representation learning. It can be used to
-> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
-> distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to
-> the discriminator of a GAN.
-
-More details about ELECTRA can be found in the [ICLR paper](https://openreview.net/forum?id=r1xMH1BtvB)
-or in the [official ELECTRA repository](https://github.com/google-research/electra) on GitHub.
-
-## Stats
-
-The current version of the model is trained on a financial news data of Naver news.
-
-The final training corpus has a size of 25GB and 2.3B tokens.
-
-This model was trained a cased model on a TITAN RTX for 500k steps.
-
-## Usage
-
-```python
-from transformers import pipeline
-
-fill_mask = pipeline(
-            "fill-mask",
-            model="krevas/finance-koelectra-base-generator",
-            tokenizer="krevas/finance-koelectra-base-generator"
-            )
-
-print(fill_mask(f"내일 해당 종목이 대폭 {fill_mask.tokenizer.mask_token}할 것이다."))
-```
-
-# Huggingface model hub
-
-All models are available on the [Huggingface model hub](https://huggingface.co/krevas).

model_cards/krevas/finance-koelectra-small-discriminator/README.md

@@ -1,45 +0,0 @@
----
-language: korean
----
-
-# 📈 Financial Korean ELECTRA model
-
-Pretrained ELECTRA Language Model for Korean (`finance-koelectra-small-discriminator`)
-
-> ELECTRA is a new method for self-supervised language representation learning. It can be used to
-> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
-> distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to
-> the discriminator of a GAN.
-
-More details about ELECTRA can be found in the [ICLR paper](https://openreview.net/forum?id=r1xMH1BtvB)
-or in the [official ELECTRA repository](https://github.com/google-research/electra) on GitHub.
-
-## Stats
-
-The current version of the model is trained on a financial news data of Naver news.
-
-The final training corpus has a size of 25GB and 2.3B tokens.
-
-This model was trained a cased model on a TITAN RTX for 500k steps.
-
-## Usage
-
-```python
-from transformers import ElectraForPreTraining, ElectraTokenizer
-import torch
-discriminator = ElectraForPreTraining.from_pretrained("krevas/finance-koelectra-small-discriminator")
-tokenizer = ElectraTokenizer.from_pretrained("krevas/finance-koelectra-small-discriminator")
-sentence = "내일 해당 종목이 대폭 상승할 것이다"
-fake_sentence = "내일 해당 종목이 맛있게 상승할 것이다"
-fake_tokens = tokenizer.tokenize(fake_sentence)
-fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
-discriminator_outputs = discriminator(fake_inputs)
-predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
-[print("%7s" % token, end="") for token in fake_tokens]
-[print("%7s" % int(prediction), end="") for prediction in predictions.tolist()[1:-1]]
-print("fake token : %s" % fake_tokens[predictions.tolist()[1:-1].index(1)])
-```
-
-# Huggingface model hub
-
-All models are available on the [Huggingface model hub](https://huggingface.co/krevas).

model_cards/krevas/finance-koelectra-small-generator/README.md

@@ -1,41 +0,0 @@
----
-language: korean
----
-
-# 📈 Financial Korean ELECTRA model
-
-Pretrained ELECTRA Language Model for Korean (`finance-koelectra-small-generator`)
-
-> ELECTRA is a new method for self-supervised language representation learning. It can be used to
-> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
-> distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to
-> the discriminator of a GAN.
-
-More details about ELECTRA can be found in the [ICLR paper](https://openreview.net/forum?id=r1xMH1BtvB)
-or in the [official ELECTRA repository](https://github.com/google-research/electra) on GitHub.
-
-## Stats
-
-The current version of the model is trained on a financial news data of Naver news.
-
-The final training corpus has a size of 25GB and 2.3B tokens.
-
-This model was trained a cased model on a TITAN RTX for 500k steps.
-
-## Usage
-
-```python
-from transformers import pipeline
-
-fill_mask = pipeline(
-            "fill-mask",
-            model="krevas/finance-koelectra-small-generator",
-            tokenizer="krevas/finance-koelectra-small-generator"
-            )
-
-print(fill_mask(f"내일 해당 종목이 대폭 {fill_mask.tokenizer.mask_token}할 것이다."))
-```
-
-# Huggingface model hub
-
-All models are available on the [Huggingface model hub](https://huggingface.co/krevas).

model_cards/lserinol/bert-turkish-question-answering/README.md

@@ -43,7 +43,7 @@ questions = [
 ]
 
 for question in questions:
-    inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="pt")
+    inputs = tokenizer.encode_plus(question, text, add_special_tokens=True, return_tensors="pt")
     input_ids = inputs["input_ids"].tolist()[0]
 
     text_tokens = tokenizer.convert_ids_to_tokens(input_ids)