splitting fast and slow tokenizers [WIP]

.circleci/config.yml

@@ -84,7 +84,7 @@ jobs:
                 key: v0.3-{{ checksum "setup.py" }}
                 paths:
                     - '~/.cache/pip'
-            - run: RUN_PT_TF_CROSS_TESTS=1 python -m pytest -n 8 --dist=loadfile -rA -s ./tests/ -m is_pt_tf_cross_test --cov --durations=0 | tee output.txt
+            - run: python -m pytest -n 8 --dist=loadfile -rA -s ./tests/ --cov  | tee output.txt
             - run: codecov
             - store_artifacts:
                   path: ~/transformers/output.txt
@@ -139,81 +139,6 @@ jobs:
             - store_artifacts:
                path: ~/transformers/output.txt
                destination: test_output.txt
-    run_tests_flax:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.7
-        environment:
-            OMP_NUM_THREADS: 1
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - restore_cache:
-                keys:
-                    - v0.3-flax-{{ checksum "setup.py" }}
-                    - v0.3-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install git+https://github.com/huggingface/datasets
-            - run: sudo pip install .[flax,sklearn,torch,testing]
-            - save_cache:
-                  key: v0.3-flax-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -rA -s ./tests/ | tee output.txt
-            - store_artifacts:
-                  path: ~/transformers/output.txt
-                  destination: test_output.txt
-    run_tests_pipelines_torch:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.7
-        environment:
-            OMP_NUM_THREADS: 1
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.3-torch-{{ checksum "setup.py" }}
-                      - v0.3-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install git+https://github.com/huggingface/datasets
-            - run: pip install .[sklearn,torch,testing]
-            - save_cache:
-                  key: v0.3-torch-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: RUN_PIPELINE_TESTS=1 python -m pytest -n 8 --dist=loadfile -rA -s ./tests/ -m is_pipeline_test | tee output.txt
-            - store_artifacts:
-                  path: ~/transformers/output.txt
-                  destination: test_output.txt
-    run_tests_pipelines_tf:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.7
-        environment:
-            OMP_NUM_THREADS: 1
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.3-tf-{{ checksum "setup.py" }}
-                      - v0.3-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install git+https://github.com/huggingface/datasets
-            - run: pip install .[sklearn,tf-cpu,testing]
-            - save_cache:
-                  key: v0.3-tf-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: RUN_PIPELINE_TESTS=1 python -m pytest -n 8 --dist=loadfile -rA -s ./tests/ -m is_pipeline_test | tee output.txt
-            - store_artifacts:
-               path: ~/transformers/output.txt
-               destination: test_output.txt
     run_tests_custom_tokenizers:
         working_directory: ~/transformers
         docker:
@@ -273,7 +198,7 @@ jobs:
                       - v0.3-build_doc-{{ checksum "setup.py" }}
                       - v0.3-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: pip install .[tf,torch,sentencepiece,docs]
+            - run: pip install .[tf,torch,docs]
             - save_cache:
                   key: v0.3-build_doc-{{ checksum "setup.py" }}
                   paths:
@@ -294,7 +219,7 @@ jobs:
                   keys:
                       - v0.3-deploy_doc-{{ checksum "setup.py" }}
                       - v0.3-{{ checksum "setup.py" }}
-            - run: pip install .[tf,torch,sentencepiece,docs]
+            - run: pip install .[tf,torch,docs]
             - save_cache:
                   key: v0.3-deploy_doc-{{ checksum "setup.py" }}
                   paths:
@@ -314,7 +239,7 @@ jobs:
                       - v0.3-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
             - run: pip install isort
-            - run: pip install .[tf,torch,flax,quality]
+            - run: pip install .[tf,torch,quality]
             - save_cache:
                   key: v0.3-code_quality-{{ checksum "setup.py" }}
                   paths:
@@ -380,9 +305,6 @@ workflows:
             - run_tests_torch_and_tf
             - run_tests_torch
             - run_tests_tf
-            - run_tests_flax
-            - run_tests_pipelines_torch
-            - run_tests_pipelines_tf
             - build_doc
             - deploy_doc: *workflow_filters
     tpu_testing_jobs:

.circleci/deploy.sh

@@ -50,5 +50,4 @@ deploy_doc "b42586e" v2.11.0
 deploy_doc "7fb8bdf" v3.0.2
 deploy_doc "4b3ee9c" v3.1.0
 deploy_doc "3ebb1b3" v3.2.0
-deploy_doc "0613f05" v3.3.1
+deploy_doc "0613f05" # v3.3.0 Latest stable release
-deploy_doc "eb0e0ce" # v3.4.0 Latest stable release

.github/PULL_REQUEST_TEMPLATE.md

@@ -16,15 +16,15 @@ Fixes # (issue)
 
 
 ## Before submitting
-- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
+- [ ] This PR fixes a typo or improves the docs (you can dimiss the other checks if that's the case).
-- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
+- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests), 
       Pull Request section?
 - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
       to the it if that's the case.
 - [ ] Did you make sure to update the documentation with your changes? Here are the
       [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
       [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
-- [ ] Did you write any new necessary tests?
+- [ ] Did you write any new necessary tests? 
 
 
 ## Who can review?
@@ -37,27 +37,25 @@ members/contributors which may be interested in your PR.
  If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
  Please tag fewer than 3 people.
 
- albert, bert, XLM: @LysandreJik
+ albert, bert, GPT2, XLM: @LysandreJik 
- GPT2: @LysandreJik, @patrickvonplaten
  tokenizers: @mfuntowicz
  Trainer: @sgugger
- Benchmarks: @patrickvonplaten
+ Speed and Memory Benchmarks: @patrickvonplaten
  Model Cards: @julien-c
  Translation: @sshleifer
  Summarization: @sshleifer
+ TextGeneration: @TevenLeScao 
  examples/distillation: @VictorSanh
  nlp datasets: [different repo](https://github.com/huggingface/nlp)
  rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
- Text Generation: @patrickvonplaten, @TevenLeScao
+ Text Generation: @TevenLeScao
  Blenderbot, Bart, Marian, Pegasus: @sshleifer
  T5: @patrickvonplaten
- Rag: @patrickvonplaten, @lhoestq
+ Longformer/Reformer: @patrickvonplaten
- EncoderDecoder: @patrickvonplaten
+ TransfoXL/XLNet: @TevenLeScao 
- Longformer, Reformer: @patrickvonplaten
- TransfoXL, XLNet: @TevenLeScao, @patrickvonplaten
  examples/seq2seq: @sshleifer
  examples/bert-loses-patience: @JetRunner
  tensorflow: @jplu
  examples/token-classification: @stefan-it
  documentation: @sgugger
- -->
+ -->

.github/workflows/github-torch-hub.yml

@@ -30,7 +30,7 @@ jobs:
       run: |
         pip install --upgrade pip
         pip install torch
-        pip install numpy filelock protobuf requests tqdm regex sentencepiece sacremoses tokenizers packaging
+        pip install numpy tokenizers filelock requests tqdm regex sentencepiece sacremoses packaging
 
     - name: Torch hub list
       run: |

.github/workflows/self-push.yml

@@ -16,52 +16,53 @@ jobs:
   run_tests_torch_and_tf_gpu:
     runs-on: [self-hosted, single-gpu]
     steps:
-      - uses: actions/checkout@v2
+    - uses: actions/checkout@v2
-      - name: Python version
+    - name: Python version
-        run: |
+      run: |
-          which python
+        which python
-          python --version
+        python --version
-          pip --version
+        pip --version
-      - name: Current dir
+    - name: Current dir
-        run: pwd
+      run: pwd
-      - run: nvidia-smi
+    - run: nvidia-smi
 
-      - name: Loading cache.
+    - name: Loading cache.
-        uses: actions/cache@v2
+      uses: actions/cache@v2
-        id: cache
+      id: cache
-        with:
+      with:
-          path: .env
+        path: .env
-          key: v0-tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
+        key: v0-tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
 
-      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+    - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-        run: |
+      run: |
-          python -m venv .env
+        python -m venv .env
-          source .env/bin/activate
+        source .env/bin/activate
-          which python
+        which python
-          python --version
+        python --version
-          pip --version
+        pip --version
-      - name: Install dependencies
+    - name: Install dependencies
-        run: |
+      run: |
-          source .env/bin/activate
+        source .env/bin/activate
-          pip install --upgrade pip
+        pip install --upgrade pip
-          pip install torch!=1.6.0
+        pip install torch!=1.6.0
-          pip install .[sklearn,testing,onnxruntime]
+        pip install .[sklearn,testing,onnxruntime]
-          pip install git+https://github.com/huggingface/datasets
+        pip install git+https://github.com/huggingface/datasets
 
-      - name: Are GPUs recognized by our DL frameworks
+    - name: Are GPUs recognized by our DL frameworks
-        run: |
+      run: |
-          source .env/bin/activate
+        source .env/bin/activate
-          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
+        python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+        python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
 
-      - name: Run all non-slow tests on GPU
+    - name: Run all non-slow tests on GPU
-        env:
+      env:
-          TF_FORCE_GPU_ALLOW_GROWTH: "true"
+        TF_FORCE_GPU_ALLOW_GROWTH: "true"
-          # TF_GPU_MEMORY_LIMIT: 4096
+        # TF_GPU_MEMORY_LIMIT: 4096
-          OMP_NUM_THREADS: 1
+        OMP_NUM_THREADS: 1
-        run: |
+        USE_CUDA: yes
-          source .env/bin/activate
+      run: |
-          python -m pytest -n 2 --dist=loadfile -s ./tests/
+        source .env/bin/activate
+        python -m pytest -n 2 --dist=loadfile -s ./tests/
 
   run_tests_torch_and_tf_multiple_gpu:
     runs-on: [self-hosted, multi-gpu]
@@ -109,6 +110,7 @@ jobs:
           TF_FORCE_GPU_ALLOW_GROWTH: "true"
           # TF_GPU_MEMORY_LIMIT: 4096
           OMP_NUM_THREADS: 1
+          USE_CUDA: yes
         run: |
           source .env/bin/activate
           python -m pytest -n 2 --dist=loadfile -s ./tests/

.github/workflows/self-scheduled.yml

@@ -12,75 +12,66 @@ jobs:
   run_all_tests_torch_and_tf_gpu:
     runs-on: [self-hosted, single-gpu]
     steps:
-      - uses: actions/checkout@v2
+    - uses: actions/checkout@v2
 
-      - name: Loading cache.
+    - name: Loading cache.
-        uses: actions/cache@v2
+      uses: actions/cache@v2
-        id: cache
+      id: cache
-        with:
+      with:
-          path: .env
+        path: .env
-          key: v0-slow_tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
+        key: v0-slow_tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
 
-      - name: Python version
+    - name: Python version
-        run: |
+      run: |
-          which python
+        which python
-          python --version
+        python --version
-          pip --version
+        pip --version
-      - name: Current dir
+    - name: Current dir
-        run: pwd
+      run: pwd
-      - run: nvidia-smi
+    - run: nvidia-smi
-      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+    - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-        if: steps.cache.outputs.cache-hit != 'true'
+      if: steps.cache.outputs.cache-hit != 'true'
-        run: |
+      run: |
-          python -m venv .env
+        python -m venv .env
-          source .env/bin/activate
+        source .env/bin/activate
-          which python
+        which python
-          python --version
+        python --version
-          pip --version
+        pip --version
-      - name: Install dependencies
+    - name: Install dependencies
-        run: |
+      run: |
-          source .env/bin/activate
+        source .env/bin/activate
-          pip install --upgrade pip
+        pip install --upgrade pip
-          pip install torch!=1.6.0
+        pip install torch!=1.6.0
-          pip install .[sklearn,testing,onnxruntime]
+        pip install .[sklearn,testing,onnxruntime]
-          pip install git+https://github.com/huggingface/datasets
+        pip install git+https://github.com/huggingface/datasets
 
-      - name: Are GPUs recognized by our DL frameworks
+    - name: Are GPUs recognized by our DL frameworks
-        run: |
+      run: |
-          source .env/bin/activate
+        source .env/bin/activate
-          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
+        python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+        python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
 
 
-      - name: Run all tests on GPU
+    - name: Run all tests on GPU
-        env:
+      env:
-          TF_FORCE_GPU_ALLOW_GROWTH: "true"
+        TF_FORCE_GPU_ALLOW_GROWTH: "true"
-          OMP_NUM_THREADS: 1
+        OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
+        RUN_SLOW: yes
-        run: |
+        USE_CUDA: yes
-          source .env/bin/activate
+      run: |
-          python -m pytest -n 1 --dist=loadfile -s ./tests/ --durations=50
+        source .env/bin/activate
-
+        python -m pytest -n 1 --dist=loadfile -s ./tests/
-      - name: Run examples tests on GPU
-        env:
-          TF_FORCE_GPU_ALLOW_GROWTH: "true"
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
-        run: |
-          source .env/bin/activate
-          pip install -r examples/requirements.txt
-          python -m pytest -n 1 --dist=loadfile -s examples --durations=50
-
-      - name: Run all pipeline tests on GPU
-        env:
-          TF_FORCE_GPU_ALLOW_GROWTH: "true"
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
-          RUN_PIPELINE_TESTS: yes
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s ./tests/ -m is_pipeline_test --durations=50
 
+    - name: Run examples tests on GPU
+      env:
+        TF_FORCE_GPU_ALLOW_GROWTH: "true"
+        OMP_NUM_THREADS: 1
+        RUN_SLOW: yes
+        USE_CUDA: yes
+      run: |
+        source .env/bin/activate
+        pip install -r examples/requirements.txt
+        python -m pytest -n 1 --dist=loadfile -s examples
 
   run_all_tests_torch_and_tf_multiple_gpu:
     runs-on: [self-hosted, multi-gpu]
@@ -129,26 +120,18 @@ jobs:
           TF_FORCE_GPU_ALLOW_GROWTH: "true"
           OMP_NUM_THREADS: 1
           RUN_SLOW: yes
+          USE_CUDA: yes
         run: |
           source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s ./tests/ --durations=50
+          python -m pytest -n 1 --dist=loadfile -s ./tests/
 
       - name: Run examples tests on GPU
         env:
           TF_FORCE_GPU_ALLOW_GROWTH: "true"
           OMP_NUM_THREADS: 1
           RUN_SLOW: yes
+          USE_CUDA: yes
         run: |
           source .env/bin/activate
           pip install -r examples/requirements.txt
-          python -m pytest -n 1 --dist=loadfile -s examples --durations=50
+          python -m pytest -n 1 --dist=loadfile -s examples
-
-      - name: Run all pipeline tests on GPU
-        env:
-          TF_FORCE_GPU_ALLOW_GROWTH: "true"
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
-          RUN_PIPELINE_TESTS: yes
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s ./tests/ -m is_pipeline_test --durations=50

.gitignore

@@ -9,11 +9,9 @@ __pycache__/
 *.so
 
 # tests and logs
-tests/fixtures/*
+tests/fixtures
-!tests/fixtures/sample_text_no_unicode.txt
 logs/
 lightning_logs/
-lang_code_data/
 
 # Distribution / packaging
 .Python
@@ -157,6 +155,3 @@ debug.env
 
 #ctags
 tags
-
-# pre-commit
-.pre-commit*

Makefile

@@ -5,18 +5,18 @@ check_dirs := examples templates tests src utils
 
 # get modified files since the branch was made
 fork_point_sha := $(shell git merge-base --fork-point master)
-joined_dirs := $(shell echo $(check_dirs) | tr " " "|")
+joined_dirs    := $(shell echo $(check_dirs) | tr " " "|")
-modified_py_files := $(shell git diff --name-only $(fork_point_sha) | egrep '^($(joined_dirs))' | egrep '\.py$$')
+modified_files := $(shell git diff --name-only $(fork_point_sha) | egrep '^($(joined_dirs))')
-#$(info modified files are: $(modified_py_files))
+#$(info modified files are: $(modified_files))
 
 modified_only_fixup:
-	@if [ -n "$(modified_py_files)" ]; then \
+	@if [ -n "$(modified_files)" ]; then \
-		echo "Checking/fixing $(modified_py_files)"; \
+		echo "Checking/fixing $(modified_files)"; \
-		black $(modified_py_files); \
+		black $(modified_files); \
-		isort $(modified_py_files); \
+		isort $(modified_files); \
-		flake8 $(modified_py_files); \
+		flake8 $(modified_files); \
 	else \
-		echo "No library .py files were modified"; \
+		echo "No relevant files were modified"; \
 	fi
 
 # Check that source code meets quality standards

README.md

@@ -81,7 +81,7 @@ This is another example of pipeline used for that can extract question answers f
 
 On top of the answer, the pretrained model used here returned its confidence score, along with the start position and its end position in the tokenized sentence. You can learn more about the tasks supported by the `pipeline` API in [this tutorial](https://huggingface.co/transformers/task_summary.html).
 
-To download and use any of the pretrained models on your given task, you just need to use those three lines of codes (PyTorch version):
+To download and use any of the pretrained models on your given task, you just need to use those three lines of codes (PyTorch verison):
 ```python
 >>> from transformers import AutoTokenizer, AutoModel
 
@@ -161,28 +161,27 @@ If you'd like to play with the examples, you must [install the library from sour
 1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
 1. **[BART](https://huggingface.co/transformers/model_doc/bart.html)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
 1. **[BERT](https://huggingface.co/transformers/model_doc/bert.html)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
-1. **[BERT For Sequence Generation](https://huggingface.co/transformers/model_doc/bertgeneration.html)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+1. **[BERT For Sequence Generation](https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
-1. **[Blenderbot](https://huggingface.co/transformers/model_doc/blenderbot.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
 1. **[CamemBERT](https://huggingface.co/transformers/model_doc/camembert.html)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
 1. **[CTRL](https://huggingface.co/transformers/model_doc/ctrl.html)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
 1. **[DeBERTa](https://huggingface.co/transformers/model_doc/deberta.html)** (from Microsoft Research) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
 1. **[DialoGPT](https://huggingface.co/transformers/model_doc/dialogpt.html)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
 1. **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
-1. **[DPR](https://huggingface.co/transformers/model_doc/dpr.html)** (from Facebook) released with the paper [Dense Passage Retrieval
+1. **[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.
 1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
 1. **[FlauBERT](https://huggingface.co/transformers/model_doc/flaubert.html)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
-1. **[Funnel Transformer](https://huggingface.co/transformers/model_doc/funnel.html)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
+1. **[Funnel Transformer](https://github.com/laiguokun/Funnel-Transformer)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
 1. **[GPT](https://huggingface.co/transformers/model_doc/gpt.html)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
 1. **[GPT-2](https://huggingface.co/transformers/model_doc/gpt2.html)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
-1. **[LayoutLM](https://huggingface.co/transformers/model_doc/layoutlm.html)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
+1. **[LayoutLM](https://github.com/microsoft/unilm/tree/master/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
 1. **[Longformer](https://huggingface.co/transformers/model_doc/longformer.html)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-1. **[LXMERT](https://huggingface.co/transformers/model_doc/lxmert.html)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
+1. **[LXMERT](https://github.com/airsplay/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
 1. **[MarianMT](https://huggingface.co/transformers/model_doc/marian.html)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
-1. **[MBart](https://huggingface.co/transformers/model_doc/mbart.html)** (from Facebook) released with the paper  [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
+1. **[MBart](https://github.com/pytorch/fairseq/tree/master/examples/mbart)** (from Facebook) released with the paper  [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-1. **[Pegasus](https://huggingface.co/transformers/model_doc/pegasus.html)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777)> by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
+1. **[MMBT](https://github.com/facebookresearch/mmbt/)** (from Facebook), released together with the paper a [Supervised Multimodal Bitransformers for Classifying Images and Text](https://arxiv.org/pdf/1909.02950.pdf) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
-1. **[ProphetNet](https://huggingface.co/transformers/model_doc/prophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
+1. **[Pegasus](https://github.com/google-research/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777)> by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
 1. **[Reformer](https://huggingface.co/transformers/model_doc/reformer.html)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
 1. **[RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
 ultilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
@@ -190,7 +189,6 @@ ultilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/
 1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
-1. **[XLM-ProphetNet](https://huggingface.co/transformers/model_doc/xlmprophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
 1. **[XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
 1. **[XLNet](https://huggingface.co/transformers/model_doc/xlnet.html)** (from Google/CMU) released with the paper [​XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
 1. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).

docker/transformers-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04
+FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
 LABEL maintainer="Hugging Face"
 LABEL repository="transformers"
 
@@ -18,14 +18,9 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip && \
     tensorflow \
     torch
 
-RUN git clone https://github.com/NVIDIA/apex
-RUN cd apex && \
-    python3 setup.py install && \
-    pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
-
 WORKDIR /workspace
 COPY . transformers/
 RUN cd transformers/ && \
     python3 -m pip install --no-cache-dir .
 
-CMD ["/bin/bash"]
+CMD ["/bin/bash"]

docker/transformers-pytorch-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04
+FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
 LABEL maintainer="Hugging Face"
 LABEL repository="transformers"
 
@@ -17,14 +17,9 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip && \
     mkl \
     torch
 
-RUN git clone https://github.com/NVIDIA/apex
-RUN cd apex && \
-    python3 setup.py install && \
-    pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
-
 WORKDIR /workspace
 COPY . transformers/
 RUN cd transformers/ && \
     python3 -m pip install --no-cache-dir .
 
-CMD ["/bin/bash"]
+CMD ["/bin/bash"]

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 = "v3.4.0"
+const stableVersion = "v3.3.0"
 // Dictionary doc folder to label
 const versionMapping = {
     "master": "master",
-    "": "v3.4.0",
+    "": "v3.3.0/v3.3.1",
-    "v3.3.1": "v3.3.0/v3.3.1",
     "v3.2.0": "v3.2.0",
     "v3.1.0": "v3.1.0 (stable)",
     "v3.0.2": "v3.0.0/v3.0.1/v3.0.2",

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'3.4.0'
+release = u'3.3.1'
 
 
 # -- General configuration ---------------------------------------------------

docs/source/glossary.rst

@@ -218,52 +218,6 @@ positional embeddings.
 Absolute positional embeddings are selected in the range ``[0, config.max_position_embeddings - 1]``. Some models
 use other types of positional embeddings, such as sinusoidal position embeddings or relative position embeddings.
 
-.. _labels:
-
-Labels
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The labels are an optional argument which can be passed in order for the model to compute the loss itself. These labels
-should be the expected prediction of the model: it will use the standard loss in order to compute the loss between
-its predictions and the expected value (the label).
-
-These labels are different according to the model head, for example:
-
-- For sequence classification models (e.g., :class:`~transformers.BertForSequenceClassification`), the model expects
-  a tensor of dimension :obj:`(batch_size)` with each value of the batch corresponding to the expected label of the
-  entire sequence.
-- For token classification models (e.g., :class:`~transformers.BertForTokenClassification`), the model expects
-  a tensor of dimension :obj:`(batch_size, seq_length)` with each value corresponding to the expected label of each
-  individual token.
-- For masked language modeling (e.g., :class:`~transformers.BertForMaskedLM`), the model expects
-  a tensor of dimension :obj:`(batch_size, seq_length)` with each value corresponding to the expected label of each
-  individual token: the labels being the token ID for the masked token, and values to be ignored for the rest (usually
-  -100).
-- For sequence to sequence tasks,(e.g., :class:`~transformers.BartForConditionalGeneration`,
-  :class:`~transformers.MBartForConditionalGeneration`), the model expects a tensor of dimension
-  :obj:`(batch_size, tgt_seq_length)` with each value corresponding to the target sequences associated with each
-  input sequence. During training, both `BART` and `T5` will make the appropriate `decoder_input_ids` and decoder
-  attention masks internally. They usually do not need to be supplied. This does not apply to models leveraging the
-  Encoder-Decoder framework.
-  See the documentation of each model for more information on each specific model's labels.
-
-The base models (e.g., :class:`~transformers.BertModel`) do not accept labels, as these are the base transformer models,
-simply outputting features.
-
-.. _decoder-input-ids:
-
-Decoder input IDs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-This input is specific to encoder-decoder models, and contains the input IDs that will be fed to the decoder.
-These inputs should be used for sequence to sequence tasks, such as translation or summarization, and are usually
-built in a way specific to each model.
-
-Most encoder-decoder models (BART, T5) create their :obj:`decoder_input_ids` on their own from the :obj:`labels`.
-In such models, passing the :obj:`labels` is the preferred way to handle training.
-
-Please check each model's docs to see how they handle these input IDs for sequence to sequence training.
-
 .. _feed-forward-chunking:
 
 Feed Forward Chunking

docs/source/index.rst

@@ -57,109 +57,113 @@ conversion utilities for the following models:
 ..
     This list is updated automatically from the README with `make fix-copies`. Do not update manually!
 
-1. :doc:`ALBERT <model_doc/albert>` (from Google Research and the Toyota Technological Institute at Chicago) released
+1. `ALBERT <https://huggingface.co/transformers/model_doc/albert.html>`__ (from Google Research and the Toyota
-   with the paper `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
+   Technological Institute at Chicago) released with the paper `ALBERT: A Lite BERT for Self-supervised Learning of
-   <https://arxiv.org/abs/1909.11942>`__, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush
+   Language Representations <https://arxiv.org/abs/1909.11942>`__, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman,
-   Sharma, Radu Soricut.
+   Kevin Gimpel, Piyush Sharma, Radu Soricut.
-2. :doc:`BART <model_doc/bart>` (from Facebook) released with the paper `BART: Denoising Sequence-to-Sequence
+2. `BART <https://huggingface.co/transformers/model_doc/bart.html>`__ (from Facebook) released with the paper `BART:
-   Pre-training for Natural Language Generation, Translation, and Comprehension
+   Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
    <https://arxiv.org/pdf/1910.13461.pdf>`__ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman
    Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
-3. :doc:`BERT <model_doc/bert>` (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional
+3. `BERT <https://huggingface.co/transformers/model_doc/bert.html>`__ (from Google) released with the paper `BERT:
-   Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__ by Jacob Devlin, Ming-Wei Chang,
+   Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__ by
-   Kenton Lee and Kristina Toutanova.
+   Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
-4. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
+4. `BERT For Sequence Generation <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`__ (from
-   Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi
+   Google) released with the paper `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
-   Narayan, Aliaksei Severyn.
+   <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
-5. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
+5. `CamemBERT <https://huggingface.co/transformers/model_doc/camembert.html>`__ (from Inria/Facebook/Sorbonne) released
-   open-domain chatbot <https://arxiv.org/abs/2004.13637>`__ by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary
+   with the paper `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`__ by Louis Martin*,
-   Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
+   Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé
-6. :doc:`CamemBERT <model_doc/camembert>` (from Inria/Facebook/Sorbonne) released with the paper `CamemBERT: a Tasty
+   Seddah and Benoît Sagot.
-   French Language Model <https://arxiv.org/abs/1911.03894>`__ by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz
+6. `CTRL <https://huggingface.co/transformers/model_doc/ctrl.html>`__ (from Salesforce) released with the paper `CTRL:
-   Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
+   A Conditional Transformer Language Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`__ by Nitish
-7. :doc:`CTRL <model_doc/ctrl>` (from Salesforce) released with the paper `CTRL: A Conditional Transformer Language
+   Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
-   Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`__ by Nitish Shirish Keskar*, Bryan McCann*,
+7. `DeBERTa <https://huggingface.co/transformers/model_doc/deberta.html>`__ (from Microsoft Research) released with the
-   Lav R. Varshney, Caiming Xiong and Richard Socher.
+   paper `DeBERTa: Decoding-enhanced BERT with Disentangled Attention <https://arxiv.org/abs/2006.03654>`__ by
-8. :doc:`DeBERTa <model_doc/deberta>` (from Microsoft Research) released with the paper `DeBERTa: Decoding-enhanced
+   Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
-   BERT with Disentangled Attention <https://arxiv.org/abs/2006.03654>`__ by Pengcheng He, Xiaodong Liu, Jianfeng Gao,
+8. `DialoGPT <https://huggingface.co/transformers/model_doc/dialogpt.html>`__ (from Microsoft Research) released with
-   Weizhu Chen.
+   the paper `DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
-9. :doc:`DialoGPT <model_doc/dialogpt>` (from Microsoft Research) released with the paper `DialoGPT: Large-Scale
+   <https://arxiv.org/abs/1911.00536>`__ by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang
-   Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`__ by Yizhe Zhang,
+   Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
-   Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
+9. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`__ (from HuggingFace), released together
-10. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
+   with the paper `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
-    distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__ by Victor
+   <https://arxiv.org/abs/1910.01108>`__ by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been
-    Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into `DistilGPT2
+   applied to compress GPT2 into `DistilGPT2
-    <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, RoBERTa into `DistilRoBERTa
+   <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, RoBERTa into `DistilRoBERTa
-    <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, Multilingual BERT into
+   <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, Multilingual BERT into
-    `DistilmBERT <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German
+   `DistilmBERT <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German version
-    version of DistilBERT.
+   of DistilBERT.
-11. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
+10. `DPR <https://github.com/facebookresearch/DPR>`__ (from Facebook) released with the paper `Dense Passage Retrieval
-    Question Answering <https://arxiv.org/abs/2004.04906>`__ by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick
+    for Open-Domain Question Answering <https://arxiv.org/abs/2004.04906>`__ by Vladimir Karpukhin, Barlas Oğuz, Sewon
-    Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
+    Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
-12. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
+11. `ELECTRA <https://huggingface.co/transformers/model_doc/electra.html>`__ (from Google Research/Stanford University)
-    Pre-training text encoders as discriminators rather than generators <https://arxiv.org/abs/2003.10555>`__ by Kevin
+    released with the paper `ELECTRA: Pre-training text encoders as discriminators rather than generators
-    Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
+    <https://arxiv.org/abs/2003.10555>`__ by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
-13. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
+12. `FlauBERT <https://huggingface.co/transformers/model_doc/flaubert.html>`__ (from CNRS) released with the paper
-    Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne,
+    `FlauBERT: Unsupervised Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le,
-    Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
+    Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé,
-14. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
+    Laurent Besacier, Didier Schwab.
-    Filtering out Sequential Redundancy for Efficient Language Processing <https://arxiv.org/abs/2006.03236>`__ by
+13. `Funnel Transformer <https://github.com/laiguokun/Funnel-Transformer>`__ (from CMU/Google Brain) released with the
-    Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
+    paper `Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
-15. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
+    <https://arxiv.org/abs/2006.03236>`__ by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
-    Pre-Training <https://blog.openai.com/language-unsupervised/>`__ by Alec Radford, Karthik Narasimhan, Tim Salimans
+14. `GPT <https://huggingface.co/transformers/model_doc/gpt.html>`__ (from OpenAI) released with the paper `Improving
-    and Ilya Sutskever.
+    Language Understanding by Generative Pre-Training <https://blog.openai.com/language-unsupervised/>`__ by Alec
-16. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
+    Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
-    Learners <https://blog.openai.com/better-language-models/>`__ by Alec Radford*, Jeffrey Wu*, Rewon Child, David
+15. `GPT-2 <https://huggingface.co/transformers/model_doc/gpt2.html>`__ (from OpenAI) released with the paper `Language
-    Luan, Dario Amodei** and Ilya Sutskever**.
+    Models are Unsupervised Multitask Learners <https://blog.openai.com/better-language-models/>`__ by Alec Radford*,
-17. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
+    Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
-    of Text and Layout for Document Image Understanding <https://arxiv.org/abs/1912.13318>`__ by Yiheng Xu, Minghao Li,
+16. `LayoutLM <https://github.com/microsoft/unilm/tree/master/layoutlm>`__ (from Microsoft Research Asia) released with
-    Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
+    the paper `LayoutLM: Pre-training of Text and Layout for Document Image Understanding
-18. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
+    <https://arxiv.org/abs/1912.13318>`__ by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
-    Transformer <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+17. `Longformer <https://huggingface.co/transformers/model_doc/longformer.html>`__ (from AllenAI) released with the
-19. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
+    paper `Longformer: The Long-Document Transformer <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E.
-    Encoder Representations from Transformers for Open-Domain Question Answering <https://arxiv.org/abs/1908.07490>`__
+    Peters, Arman Cohan.
-    by Hao Tan and Mohit Bansal.
+18. `LXMERT <https://github.com/airsplay/lxmert>`__ (from UNC Chapel Hill) released with the paper `LXMERT: Learning
-20. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
+    Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering
-    Jörg Tiedemann. The `Marian Framework <https://marian-nmt.github.io/>`__ is being developed by the Microsoft
+    <https://arxiv.org/abs/1908.07490>`__ by Hao Tan and Mohit Bansal.
-    Translator Team.
+19. `MarianMT <https://huggingface.co/transformers/model_doc/marian.html>`__ Machine translation models trained using
-21. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper  `Multilingual Denoising Pre-training for
+    `OPUS <http://opus.nlpl.eu/>`__ data by Jörg Tiedemann. The `Marian Framework <https://marian-nmt.github.io/>`__ is
-    Neural Machine Translation <https://arxiv.org/abs/2001.08210>`__ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li,
+    being developed by the Microsoft Translator Team.
-    Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
+20. `MBart <https://github.com/pytorch/fairseq/tree/master/examples/mbart>`__ (from Facebook) released with the paper 
-22. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
+    `Multilingual Denoising Pre-training for Neural Machine Translation <https://arxiv.org/abs/2001.08210>`__ by Yinhan
-    Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__> by Jingqing Zhang, Yao Zhao,
+    Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-    Mohammad Saleh and Peter J. Liu.
+21. `MMBT <https://github.com/facebookresearch/mmbt/>`__ (from Facebook), released together with the paper a
-23. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
+    `Supervised Multimodal Bitransformers for Classifying Images and Text <https://arxiv.org/pdf/1909.02950.pdf>`__ by
-    Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi,
+    Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
-    Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
+22. `Pegasus <https://github.com/google-research/pegasus>`__ (from Google) released with the paper `PEGASUS:
-24. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
+    Pre-training with Extracted Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__> by
-    Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
+    Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
-25. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
+23. `Reformer <https://huggingface.co/transformers/model_doc/reformer.html>`__ (from Google Research) released with the
-    Pretraining Approach <https://arxiv.org/abs/1907.11692>`__ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar
+    paper `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser,
-    Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. ultilingual BERT into `DistilmBERT
+    Anselm Levskaya.
+24. `RoBERTa <https://huggingface.co/transformers/model_doc/roberta.html>`__ (from Facebook), released together with
+    the paper a `Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`__ by Yinhan Liu, Myle
+    Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
+    ultilingual BERT into `DistilmBERT
     <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German version of
     DistilBERT.
-26. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
+25. `SqueezeBert <https://huggingface.co/transformers/model_doc/squeezebert.html>`__ released with the paper
-    about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi
+    `SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
-    Krishna, and Kurt W. Keutzer.
+    <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
-27. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
+26. `T5 <https://huggingface.co/transformers/model_doc/t5.html>`__ (from Google AI) released with the paper `Exploring
-    Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam
+    the Limits of Transfer Learning with a Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by
-    Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
+    Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi
-28. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
+    Zhou and Wei Li and Peter J. Liu.
-    Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
+27. `Transformer-XL <https://huggingface.co/transformers/model_doc/transformerxl.html>`__ (from Google/CMU) released
-    Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
+    with the paper `Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
-29. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+    <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le,
-    Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis Conneau.
+    Ruslan Salakhutdinov.
-30. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+28. `XLM <https://huggingface.co/transformers/model_doc/xlm.html>`__ (from Facebook) released together with the paper
-    Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
+    `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis
-    Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
+    Conneau.
-31. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+29. `XLM-RoBERTa <https://huggingface.co/transformers/model_doc/xlmroberta.html>`__ (from Facebook AI), released
-    Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
+    together with the paper `Unsupervised Cross-lingual Representation Learning at Scale
-    Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
+    <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary,
-    Zettlemoyer and Veselin Stoyanov.
+    Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
-32. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
+30. `XLNet <https://huggingface.co/transformers/model_doc/xlnet.html>`__ (from Google/CMU) released with the paper
-    Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by Zhilin Yang*, Zihang Dai*, Yiming
+    `​XLNet: Generalized Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by
-    Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
+    Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-33. `Other community models <https://huggingface.co/models>`__, contributed by the `community
+31. `Other community models <https://huggingface.co/models>`__, contributed by the `community
     <https://huggingface.co/users>`__.
 
 .. toctree::
@@ -209,7 +213,6 @@ conversion utilities for the following models:
     :maxdepth: 2
     :caption: Main Classes
 
-    main_classes/callback
     main_classes/configuration
     main_classes/logging
     main_classes/model
@@ -229,7 +232,6 @@ conversion utilities for the following models:
     model_doc/bart
     model_doc/bert
     model_doc/bertgeneration
-    model_doc/blenderbot
     model_doc/camembert
     model_doc/ctrl
     model_doc/deberta
@@ -250,7 +252,6 @@ conversion utilities for the following models:
     model_doc/gpt
     model_doc/gpt2
     model_doc/pegasus
-    model_doc/prophetnet
     model_doc/rag
     model_doc/reformer
     model_doc/retribert
@@ -259,7 +260,6 @@ conversion utilities for the following models:
     model_doc/t5
     model_doc/transformerxl
     model_doc/xlm
-    model_doc/xlmprophetnet
     model_doc/xlmroberta
     model_doc/xlnet
 
@@ -270,4 +270,3 @@ conversion utilities for the following models:
     internal/modeling_utils
     internal/pipelines_utils
     internal/tokenization_utils
-    internal/trainer_utils

docs/source/internal/trainer_utils.rst

@@ -1,27 +0,0 @@
-Utilities for Trainer
------------------------------------------------------------------------------------------------------------------------
-
-This page lists all the utility functions used by :class:`~transformers.Trainer`.
-
-Most of those are only useful if you are studying the code of the Trainer in the library.
-
-Utilities
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.EvalPrediction
-
-.. autofunction:: transformers.set_seed
-
-.. autofunction:: transformers.torch_distributed_zero_first
-
-
-Callbacks internals
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.trainer_callback.CallbackHandler
-
-Distributed Evaluation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.trainer_pt_utils.DistributedTensorGatherer
-    :members:

docs/source/main_classes/callback.rst

@@ -1,68 +0,0 @@
-Callbacks
------------------------------------------------------------------------------------------------------------------------
-
-Callbacks are objects that can customize the behavior of the training loop in the PyTorch
-:class:`~transformers.Trainer` (this feature is not yet implemented in TensorFlow) that can inspect the training loop
-state (for progress reporting, logging on TensorBoard or other ML platforms...) and take decisions (like early
-stopping).
-
-Callbacks are "read only" pieces of code, apart from the :class:`~transformers.TrainerControl` object they return, they
-cannot change anything in the training loop. For customizations that require changes in the training loop, you should
-subclass :class:`~transformers.Trainer` and override the methods you need (see :doc:`trainer` for examples).
-
-By default a :class:`~transformers.Trainer` will use the following callbacks:
-
-- :class:`~transformers.DefaultFlowCallback` which handles the default behavior for logging, saving and evaluation.
-- :class:`~transformers.PrinterCallback` or :class:`~transformers.ProrgressCallback` to display progress and print the
-  logs (the first one is used if you deactivate tqdm through the :class:`~transformers.TrainingArguments`, otherwise
-  it's the second one).
-- :class:`~transformers.integrations.TensorBoardCallback` if tensorboard is accessible (either through PyTorch >= 1.4
-  or tensorboardX).
-- :class:`~transformers.integrations.WandbCallback` if `wandb <https://www.wandb.com/>`__ is installed.
-- :class:`~transformers.integrations.CometCallback` if `comet_ml <https://www.comet.ml/site/>`__ is installed.
-
-The main class that implements callbacks is :class:`~transformers.TrainerCallback`. It gets the
-:class:`~transformers.TrainingArguments` used to instantiate the :class:`~transformers.Trainer`, can access that
-Trainer's internal state via :class:`~transformers.TrainerState`, and can take some actions on the training loop via
-:class:`~transformers.TrainerControl`.
-
-
-Available Callbacks
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Here is the list of the available :class:`~transformers.TrainerCallback` in the library:
-
-.. autoclass:: transformers.integrations.CometCallback
-    :members: setup
-
-.. autoclass:: transformers.DefaultFlowCallback
-
-.. autoclass:: transformers.PrinterCallback
-
-.. autoclass:: transformers.ProgressCallback
-
-.. autoclass:: transformers.integrations.TensorBoardCallback
-
-.. autoclass:: transformers.integrations.WandbCallback
-    :members: setup
-
-
-TrainerCallback
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TrainerCallback
-    :members:
-
-
-TrainerState
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TrainerState
-    :members:
-
-
-TrainerControl
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TrainerControl
-    :members:

docs/source/main_classes/trainer.rst

@@ -15,9 +15,10 @@ Both :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` contain
 previous features. To inject custom behavior you can subclass them and override the following methods:
 
 - **get_train_dataloader**/**get_train_tfdataset** -- Creates the training DataLoader (PyTorch) or TF Dataset.
-- **get_eval_dataloader**/**get_eval_tfdataset** -- Creates the evaluation DataLoader (PyTorch) or TF Dataset.
+- **get_eval_dataloader**/**get_eval_tfdataset** -- Creates the evaulation DataLoader (PyTorch) or TF Dataset.
 - **get_test_dataloader**/**get_test_tfdataset** -- Creates the test DataLoader (PyTorch) or TF Dataset.
 - **log** -- Logs information on the various objects watching training.
+- **setup_wandb** -- Setups wandb (see `here <https://docs.wandb.com/huggingface>`__ for more information).
 - **create_optimizer_and_scheduler** -- Setups the optimizer and learning rate scheduler if they were not passed at
   init.
 - **compute_loss** - Computes the loss on a batch of training inputs.
@@ -39,10 +40,6 @@ Here is an example of how to customize :class:`~transformers.Trainer` using a cu
             logits = outputs[0]
             return my_custom_loss(logits, labels)
 
-Another way to customize the training loop behavior for the PyTorch :class:`~transformers.Trainer` is to use
-:doc:`callbacks <callback>` that can inspect the training loop state (for progress reporting, logging on TensorBoard or
-other ML platforms...) and take decisions (like early stopping).
-
 
 Trainer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -50,23 +47,29 @@ 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

@@ -86,18 +86,3 @@ BartForQuestionAnswering
 
 .. autoclass:: transformers.BartForQuestionAnswering
     :members: forward
-
-
-
-TFBartModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBartModel
-    :members: call
-
-
-TFBartForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBartForConditionalGeneration
-    :members: call

docs/source/model_doc/blenderbot.rst

@@ -1,75 +0,0 @@
-Blenderbot
------------------------------------------------------------------------------------------------------------------------
-**DISCLAIMER:** If you see something strange,
-file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ .
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The Blender chatbot model was proposed in `Recipes for building an open-domain chatbot <https://arxiv.org/pdf/2004.13637.pdf>`__ Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston on 30 Apr 2020.
-
-The abstract of the paper is the following:
-
-*Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent persona. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.*
-
-The authors' code can be found `here <https://github.com/facebookresearch/ParlAI>`__ .
-
-
-Implementation Notes
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-- Blenderbot uses a standard `seq2seq model transformer <https://arxiv.org/pdf/1706.03762.pdf>`__ based architecture.
-- It inherits completely from :class:`~transformers.BartForConditionalGeneration`
-- Even though blenderbot is one model, it uses two tokenizers :class:`~transformers.BlenderbotSmallTokenizer` for 90M checkpoint and :class:`~transformers.BlenderbotTokenizer` for all other checkpoints.
-- :class:`~transformers.BlenderbotSmallTokenizer` will always return :class:`~transformers.BlenderbotSmallTokenizer`, regardless of checkpoint. To use the 3B parameter checkpoint, you must call :class:`~transformers.BlenderbotTokenizer` directly.
-- Available checkpoints can be found in the `model hub <https://huggingface.co/models?search=blenderbot>`__.
-
-
-Usage
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Model Usage:
-
-        >>> from transformers import BlenderbotSmallTokenizer, BlenderbotForConditionalGeneration
-        >>> mname = 'facebook/blenderbot-90M'
-        >>> model = BlenderbotForConditionalGeneration.from_pretrained(mname)
-        >>> tokenizer = BlenderbotSmallTokenizer.from_pretrained(mname)
-        >>> UTTERANCE = "My friends are cool but they eat too many carbs."
-        >>> inputs = tokenizer([UTTERANCE], return_tensors='pt')
-        >>> reply_ids = model.generate(**inputs)
-        >>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in reply_ids])
-
-
-See Config Values:
-
-        >>> from transformers import BlenderbotConfig
-        >>> config_90 = BlenderbotConfig.from_pretrained("facebook/blenderbot-90M")
-        >>> config_90.to_diff_dict()  # show interesting Values.
-        >>> configuration_3B = BlenderbotConfig("facebook/blenderbot-3B")
-        >>> configuration_3B.to_diff_dict()
-
-
-BlenderbotConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.BlenderbotConfig
-    :members:
-
-BlenderbotTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BlenderbotTokenizer
-    :members: build_inputs_with_special_tokens
-
-BlenderbotSmallTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BlenderbotSmallTokenizer
-    :members:
-
-
-BlenderbotForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-See :obj:`transformers.BartForConditionalGeneration` for arguments to `forward` and `generate`
-
-.. autoclass:: transformers.BlenderbotForConditionalGeneration
-    :members:

docs/source/model_doc/encoderdecoder.rst

@@ -27,4 +27,4 @@ EncoderDecoderModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.EncoderDecoderModel
-    :members: forward, from_encoder_decoder_pretrained
+    :members: forward

docs/source/model_doc/gpt.rst

@@ -104,13 +104,6 @@ OpenAIGPTDoubleHeadsModel
     :members: forward
 
 
-OpenAIGPTForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.OpenAIGPTForSequenceClassification
-    :members: forward
-
-
 TFOpenAIGPTModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/gpt2.rst

@@ -88,13 +88,6 @@ GPT2DoubleHeadsModel
     :members: forward
 
 
-GPT2ForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.GPT2ForSequenceClassification
-    :members: forward
-
-
 TFGPT2Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/prophetnet.rst

@@ -1,83 +0,0 @@
-ProphetNet
------------------------------------------------------------------------------------------------------------------------
-
-**DISCLAIMER:** If you see something strange, file a `Github Issue
-<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
-@patrickvonplaten
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The ProphetNet model was proposed in `ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou on 13 Jan, 2020.
-
-ProphetNet is an encoder-decoder model and can predict n-future tokens for "ngram" language modeling instead of just the next token.
-
-The abstract from the paper is the following:
-
-*In this paper, we present a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.*
-
-The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.
-
-
-ProphetNetConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ProphetNetConfig
-    :members:
-
-
-ProphetNetTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ProphetNetTokenizer
-    :members:
-
-
-ProphetNet specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_prophetnet.ProphetNetSeq2SeqLMOutput
-    :members:
-
-.. autoclass:: transformers.modeling_prophetnet.ProphetNetSeq2SeqModelOutput
-    :members:
-
-.. autoclass:: transformers.modeling_prophetnet.ProphetNetDecoderModelOutput
-    :members:
-
-.. autoclass:: transformers.modeling_prophetnet.ProphetNetDecoderLMOutput
-    :members:
-
-ProphetNetModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ProphetNetModel
-    :members: forward
-
-
-ProphetNetEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ProphetNetEncoder
-    :members: forward
-
-
-ProphetNetDecoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ProphetNetDecoder
-    :members: forward
-
-
-ProphetNetForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ProphetNetForConditionalGeneration
-    :members: forward
-
-
-ProphetNetForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ProphetNetForCausalLM
-    :members: forward

docs/source/model_doc/rag.rst

@@ -62,7 +62,8 @@ Rag specific outputs
 .. autoclass:: transformers.modeling_rag.RetrievAugLMOutput
     :members:
 
-RagRetriever
+
+RAGRetriever
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RagRetriever

docs/source/model_doc/squeezebert.rst

@@ -1,8 +1,8 @@
 SqueezeBERT
------------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~
 
 The SqueezeBERT model was proposed in
 `SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
@@ -40,14 +40,14 @@ Tips:
   `squeezebert/squeezebert-mnli-headless` checkpoint.
 
 SqueezeBertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.SqueezeBertConfig
     :members:
 
 
 SqueezeBertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.SqueezeBertTokenizer
     :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -55,49 +55,49 @@ SqueezeBertTokenizer
 
 
 SqueezeBertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.SqueezeBertTokenizerFast
     :members:
 
 
 SqueezeBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.SqueezeBertModel
     :members:
 
 
 SqueezeBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.SqueezeBertForMaskedLM
     :members:
 
 
 SqueezeBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.SqueezeBertForSequenceClassification
     :members:
 
 
 SqueezeBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.SqueezeBertForMultipleChoice
     :members:
 
 
 SqueezeBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.SqueezeBertForTokenClassification
     :members:
 
 
 SqueezeBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.SqueezeBertForQuestionAnswering
     :members:

docs/source/model_doc/t5.rst

@@ -62,10 +62,10 @@ token. T5 can be trained / fine-tuned both in a supervised and unsupervised fash
 
 .. code-block::
 
-  input_ids = tokenizer('The <extra_id_0> walks in <extra_id_1> park', return_tensors='pt').input_ids
+  input_ids = tokenizer.encode('The <extra_id_0> walks in <extra_id_1> park', return_tensors='pt')
-  labels = tokenizer('<extra_id_0> cute dog <extra_id_1> the <extra_id_2>', return_tensors='pt').input_ids
+  labels = tokenizer.encode('<extra_id_0> cute dog <extra_id_1> the <extra_id_2> </s>', return_tensors='pt')
   # the forward function automatically creates the correct decoder_input_ids
-  loss = model(input_ids=input_ids, labels=labels, return_dict=True).loss
+  model(input_ids=input_ids, labels=labels)
 
 - Supervised training
 
@@ -75,10 +75,10 @@ token. T5 can be trained / fine-tuned both in a supervised and unsupervised fash
   
 .. code-block::
 
-  input_ids = tokenizer('translate English to German: The house is wonderful.', return_tensors='pt').input_ids
+  input_ids = tokenizer.encode('translate English to German: The house is wonderful. </s>', return_tensors='pt')
-  labels = tokenizer('Das Haus ist wunderbar.', return_tensors='pt').input_ids
+  labels = tokenizer.encode('Das Haus ist wunderbar. </s>', return_tensors='pt')
   # the forward function automatically creates the correct decoder_input_ids
-  loss = model(input_ids=input_ids, labels=labels, return_dict=True).loss
+  model(input_ids=input_ids, labels=labels)
 
 
 T5Config

docs/source/model_doc/xlmprophetnet.rst

@@ -1,63 +0,0 @@
-XLM-ProphetNet
------------------------------------------------------------------------------------------------------------------------
-
-**DISCLAIMER:** If you see something strange, file a `Github Issue
-<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
-@patrickvonplaten
-
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The XLM-ProphetNet model was proposed in `ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou on 13 Jan, 2020.
-
-XLM-ProphetNet is an encoder-decoder model and can predict n-future tokens for "ngram" language modeling instead of just the next token. Its architecture is identical to ProhpetNet, but the model was trained on the multi-lingual "wiki100" Wikipedia dump.
-
-The abstract from the paper is the following:
-
-*In this paper, we present a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.*
-
-The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.
-
-XLMProphetNetConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMProphetNetConfig
-    :members:
-
-
-XLMProphetNetTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMProphetNetTokenizer
-    :members:
-
-
-XLMProphetNetModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMProphetNetModel
-
-
-XLMProphetNetEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMProphetNetEncoder
-
-
-XLMProphetNetDecoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMProphetNetDecoder
-
-
-XLMProphetNetForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMProphetNetForConditionalGeneration
-
-
-XLMProphetNetForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMProphetNetForCausalLM

docs/source/model_summary.rst

@@ -500,8 +500,8 @@ BART
 <https://arxiv.org/abs/1910.13461>`_, Mike Lewis et al.
 
 Sequence-to-sequence model with an encoder and a decoder. Encoder is fed a corrupted version of the tokens, decoder is
-fed the original tokens (but has a mask to hide the future words like a regular transformers decoder). For the encoder
+fed the original tokens (but has a mask to hide the future words like a regular transformers decoder). For the encoder, on the
-, on the pretraining tasks, a composition of the following transformations are applied:
+pretraining tasks, a composition of the following transformations are applied:
 
   * mask random tokens (like in BERT)
   * delete random tokens
@@ -526,17 +526,12 @@ Pegasus
 `PEGASUS: Pre-training with Extracted Gap-sentences forAbstractive Summarization 
 <https://arxiv.org/pdf/1912.08777.pdf>`_, Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019.
 
-Sequence-to-sequence model with the same encoder-decoder model architecture as BART. Pegasus is pre-trained jointly on
+Sequence-to-sequence model with the same encoder-decoder model architecture as BART. Pegasus is pre-trained jointly on two self-supervised objective functions: Masked Language Modeling (MLM) and a novel summarization specific pre-training objective, called Gap Sentence Generation (GSG).
-two self-supervised objective functions: Masked Language Modeling (MLM) and a novel summarization specific pre-training
-objective, called Gap Sentence Generation (GSG).
 
-  * MLM: encoder input tokens are randomely replaced by a mask tokens and have to be predicted by the encoder (like
+  * MLM: encoder input tokens are randomely replaced by a mask tokens and have to be predicted by the encoder (like in BERT)
-    in BERT)
+  * GSG: whole encoder input sentences are replaced by a second mask token and fed to the decoder, but which has a causal mask to hide the future words like a regular auto-regressive transformer decoder.
-  * GSG: whole encoder input sentences are replaced by a second mask token and fed to the decoder, but which has a
-    causal mask to hide the future words like a regular auto-regressive transformer decoder.
 
-In contrast to BART, Pegasus' pretraining task is intentionally similar to summarization: important sentences are
+In contrast to BART, Pegasus' pretraining task is intentionally similar to summarization: important sentences are masked and are generated together as one output sequence from the remaining sentences, similar to an extractive summary.
-masked and are generated together as one output sequence from the remaining sentences, similar to an extractive summary.
 
 The library provides a version of this model for conditional generation, which should be used for summarization.
 
@@ -582,12 +577,11 @@ The pretraining includes both supervised and self-supervised training. Supervise
 tasks provided by the GLUE and SuperGLUE benchmarks (converting them into text-to-text tasks as explained above).
 
 Self-supervised training uses corrupted tokens, by randomly removing 15% of the tokens and
-replacing them with individual sentinel tokens (if several consecutive tokens are marked for removal, the whole group
+replacing them with individual sentinel tokens (if several consecutive tokens are marked for removal, the whole group is replaced with a single sentinel token). The input of the encoder is the corrupted sentence, the input of the decoder is the
-is replaced with a single sentinel token). The input of the encoder is the corrupted sentence, the input of the decoder
+original sentence and the target is then the dropped out tokens delimited by their sentinel tokens.
-is the original sentence and the target is then the dropped out tokens delimited by their sentinel tokens.
 
-For instance, if we have the sentence “My dog is very cute .”, and we decide to remove the tokens: "dog", "is" and
+For instance, if we have the sentence “My dog is very cute .”, and we decide to remove the tokens: "dog", "is" and "cute", the encoder
-"cute", the encoder input becomes “My <x> very <y> .” and the target input becomes “<x> dog is <y> cute .<z>”
+input becomes “My <x> very <y> .” and the target input becomes “<x> dog is <y> cute .<z>”
 
 The library provides a version of this model for conditional generation.
 
@@ -603,8 +597,7 @@ MBart
        <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-mbart-blueviolet">
    </a>
 
-`Multilingual Denoising Pre-training for Neural Machine Translation <https://arxiv.org/abs/2001.08210>`_ by Yinhan
+`Multilingual Denoising Pre-training for Neural Machine Translation <https://arxiv.org/abs/2001.08210>`_ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov
-Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov
 Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
 
 The model architecture and pre-training objective is same as BART, but MBart is trained on 25 languages 
@@ -613,58 +606,9 @@ for pre-training a complete sequence-to-sequence model by denoising full texts i
 
 The library provides a version of this model for conditional generation.
 
-The `mbart-large-en-ro checkpoint <https://huggingface.co/facebook/mbart-large-en-ro>`_ can be used for english ->
+The `mbart-large-en-ro checkpoint <https://huggingface.co/facebook/mbart-large-en-ro>`_ can be used for english -> romanian translation.
-romanian translation.
 
-The `mbart-large-cc25 <https://huggingface.co/facebook/mbart-large-cc25>`_ checkpoint can be finetuned for other
+The `mbart-large-cc25 <https://huggingface.co/facebook/mbart-large-cc25>`_ checkpoint can be finetuned for other translation and summarization tasks, using code in ```examples/seq2seq/``` , but is not very useful without finetuning.
-translation and summarization tasks, using code in ```examples/seq2seq/``` , but is not very useful without finetuning.
-
-
-ProphetNet
------------------------------------------------------------------------------------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=prophetnet">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-prophetnet-blueviolet">
-   </a>
-   <a href="model_doc/prophetnet.html">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-prophetnet-blueviolet">
-   </a>
-
-`ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, <https://arxiv.org/abs/2001.04063>`__ by
-Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou.
-
-ProphetNet introduces a novel *sequence-to-sequence* pre-training objective, called *future n-gram prediction*. In
-future n-gram prediction, the model predicts the next n tokens simultaneously based on previous context tokens at
-each time step instead instead of just the single next token. The future n-gram prediction explicitly encourages
-the model to plan for the future tokens and prevent overfitting on strong local correlations.
-The model architecture is based on the original Transformer, but replaces the "standard" self-attention mechanism
-in the decoder by a a main self-attention mechanism and a self and n-stream (predict) self-attention mechanism.
-
-The library provides a pre-trained version of this model for conditional generation and a fine-tuned version for
-summarization.
-
-XLM-ProphetNet
------------------------------------------------------------------------------------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=xprophetnet">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-xprophetnet-blueviolet">
-   </a>
-   <a href="model_doc/xlmprophetnet.html">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xprophetnet-blueviolet">
-   </a>
-
-`ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, <https://arxiv.org/abs/2001.04063>`__ by
-Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou.
-
-XLM-ProphetNet's model architecture and pre-training objective is same as ProphetNet, but XLM-ProphetNet was
-pre-trained on the cross-lingual dataset `XGLUE <https://arxiv.org/abs/2004.01401>`__.
-
-The library provides a pre-trained version of this model for multi-lingual conditional generation and fine-tuned
-versions for headline generation and question generation, respectively.
 
 .. _multimodal-models:
 

docs/source/perplexity.rst

@@ -125,19 +125,18 @@ are 512 preceding tokens available to condition on).
     lls = []
     for i in tqdm(range(0, encodings.input_ids.size(1), stride)):
         begin_loc = max(i + stride - max_length, 0)
-        end_loc = min(i + stride, encodings.input_ids.size(1))
+        end_loc = i + stride
-        trg_len = end_loc - i    # may be different from stride on last loop
         input_ids = encodings.input_ids[:,begin_loc:end_loc].to(device)
         target_ids = input_ids.clone()
-        target_ids[:,:-trg_len] = -100
+        target_ids[:,:-stride] = -100
 
         with torch.no_grad():
             outputs = model(input_ids, labels=target_ids)
-            log_likelihood = outputs[0] * trg_len
+            log_likelihood = outputs[0] * stride
 
         lls.append(log_likelihood)
-
+    
-    ppl = torch.exp(torch.stack(lls).sum() / end_loc)
+    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.

docs/source/philosophy.rst

@@ -66,7 +66,7 @@ The library is built around three types of classes for each model:
 All these classes can be instantiated from pretrained instances and saved locally using two methods:
 
 - :obj:`from_pretrained()` lets you instantiate a model/configuration/tokenizer from a pretrained version either
-  provided by the library itself (the supported models are provided in the list :doc:`here <pretrained_models>`
+  provided by the library itself (the suported models are provided in the list :doc:`here <pretrained_models>`
   or stored locally (or on a server) by the user,
 - :obj:`save_pretrained()` lets you save a model/configuration/tokenizer locally so that it can be reloaded using
   :obj:`from_pretrained()`.

docs/source/pretrained_models.rst

@@ -294,10 +294,10 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 |                    | ``t5-11B``                                                 | | ~11B parameters with 24-layers, 1024-hidden-state, 65536 feed-forward hidden-state, 128-heads,                                      |
 |                    |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| XLM-RoBERTa        | ``xlm-roberta-base``                                       | | ~270M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 8-heads,                                         |
+| XLM-RoBERTa        | ``xlm-roberta-base``                                       | | ~125M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 8-heads,                                         |
 |                    |                                                            | | Trained on on 2.5 TB of newly created clean CommonCrawl data in 100 languages                                                       |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``xlm-roberta-large``                                      | | ~550M parameters with 24-layers, 1024-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
+|                    | ``xlm-roberta-large``                                      | | ~355M parameters with 24-layers, 1027-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
 |                    |                                                            | | Trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages                                                          |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | FlauBERT           | ``flaubert/flaubert_small_cased``                          | | 6-layer, 512-hidden, 8-heads, 54M parameters                                                                                        |

docs/source/task_summary.rst

@@ -758,8 +758,8 @@ Here is an example of using the pipelines to do summarization. It leverages a Ba
     ... If convicted, Barrientos faces up to four years in prison.  Her next court appearance is scheduled for May 18.
     ... """
 
-Because the summarization pipeline depends on the ``PreTrainedModel.generate()`` method, we can override the default arguments
+Because the summarization pipeline depends on the ``PretrainedModel.generate()`` method, we can override the default arguments
-of ``PreTrainedModel.generate()`` directly in the pipeline for ``max_length`` and ``min_length`` as shown below.
+of ``PretrainedModel.generate()`` directly in the pipeline for ``max_length`` and ``min_length`` as shown below.
 This outputs the following summary:
 
 .. code-block::
@@ -772,7 +772,7 @@ Here is an example of doing summarization using a model and a tokenizer. The pro
 1. Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder model, such as ``Bart`` or ``T5``.
 2. Define the article that should be summarized.
 3. Add the T5 specific prefix "summarize: ".
-4. Use the ``PreTrainedModel.generate()`` method to generate the summary.
+4. Use the ``PretrainedModel.generate()`` method to generate the summary.
 
 In this example we use Google`s T5 model. Even though it was pre-trained only on a multi-task mixed dataset (including CNN / Daily Mail), it yields very good results.
 
@@ -819,15 +819,15 @@ translation results.
     >>> print(translator("Hugging Face is a technology company based in New York and Paris", max_length=40))
     [{'translation_text': 'Hugging Face ist ein Technologieunternehmen mit Sitz in New York und Paris.'}]
 
-Because the translation pipeline depends on the ``PreTrainedModel.generate()`` method, we can override the default arguments
+Because the translation pipeline depends on the ``PretrainedModel.generate()`` method, we can override the default arguments
-of ``PreTrainedModel.generate()`` directly in the pipeline as is shown for ``max_length`` above.
+of ``PretrainedModel.generate()`` directly in the pipeline as is shown for ``max_length`` above.
 
 Here is an example of doing translation using a model and a tokenizer. The process is the following:
 
 1. Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder model, such as ``Bart`` or ``T5``.
 2. Define the article that should be summarizaed.
 3. Add the T5 specific prefix "translate English to German: "
-4. Use the ``PreTrainedModel.generate()`` method to perform the translation.
+4. Use the ``PretrainedModel.generate()`` method to perform the translation.
 
 .. code-block::
 

docs/source/testing.rst

@@ -22,12 +22,12 @@ How transformers are tested
 
    * `self-hosted (push) <https://github.com/huggingface/transformers/blob/master/.github/workflows/self-push.yml>`__: runs fast tests on GPU only on commits on ``master``. It only runs if a commit on ``master`` has updated the code in one of the following folders: ``src``, ``tests``, ``.github`` (to prevent running on added model cards, notebooks, etc.)
      
-   * `self-hosted runner <https://github.com/huggingface/transformers/blob/master/.github/workflows/self-scheduled.yml>`__: runs normal and slow tests on GPU in ``tests`` and ``examples``:
+   * `self-hosted runner <https://github.com/huggingface/transformers/blob/master/.github/workflows/self-scheduled.yml>`__: runs slow tests on ``tests`` and ``examples``:
 
    .. code-block:: bash
 
-    RUN_SLOW=1 pytest tests/
+    RUN_SLOW=1 USE_CUDA=1 pytest tests/
-    RUN_SLOW=1 pytest examples/
+    RUN_SLOW=1 USE_CUDA=1 pytest examples/
 
    The results can be observed `here <https://github.com/huggingface/transformers/actions>`__.
 
@@ -393,53 +393,36 @@ On a GPU-enabled setup, to test in CPU-only mode add ``CUDA_VISIBLE_DEVICES=""``
                 
     CUDA_VISIBLE_DEVICES="" pytest tests/test_logging.py
 
-or if you have multiple gpus, you can specify which one is to be used by ``pytest``. For example, to use only the second gpu if you have gpus ``0`` and ``1``, you can run:
+or if you have multiple gpus, you can tell which one to use in this test session, e.g. to use only the second gpu if you have gpus ``0`` and ``1``, you can run:
 
 .. code-block:: bash
                 
     CUDA_VISIBLE_DEVICES="1" pytest tests/test_logging.py
 
 This is handy when you want to run different tasks on different GPUs.
+    
+And we have these decorators that require the condition described by the marker.
 
-Some tests must be run on CPU-only, others on either CPU or GPU or TPU, yet others on multiple-GPUs. The following skip decorators are used to set the requirements of tests CPU/GPU/TPU-wise:
+``
-
+@require_torch
-* ``require_torch`` - this test will run only under torch
+@require_tf
-* ``require_torch_gpu`` - as ``require_torch`` plus requires at least 1 GPU
+@require_multigpu
-* ``require_torch_multigpu`` - as ``require_torch`` plus requires at least 2 GPUs
+@require_non_multigpu
-* ``require_torch_non_multigpu`` - as ``require_torch`` plus requires 0 or 1 GPUs
+@require_torch_tpu
-* ``require_torch_tpu`` - as ``require_torch`` plus requires at least 1 TPU
+@require_torch_and_cuda
-
+``
-For example, here is a test that must be run only when there are 2 or more GPUs available and pytorch is installed:
-
-.. code-block:: python
-
-    @require_torch_multigpu
-    def test_example_with_multigpu():
-
-If a test requires ``tensorflow`` use the ``require_tf`` decorator. For example:
-
-.. code-block:: python
-
-    @require_tf
-    def test_tf_thing_with_tensorflow():
-
-These decorators can be stacked. For example, if a test is slow and requires at least one GPU under pytorch, here is how to set it up:
-
-.. code-block:: python
-
-    @require_torch_gpu
-    @slow
-    def test_example_slow_on_gpu():
 
 Some decorators like ``@parametrized`` rewrite test names, therefore ``@require_*`` skip decorators have to be listed last for them to work correctly. Here is an example of the correct usage:
 
 .. code-block:: python
 
     @parameterized.expand(...)
-    @require_torch_multigpu
+    @require_multigpu
     def test_integration_foo():
+    
+There is no problem whatsoever with ``@pytest.mark.parametrize`` (but it only works with non-unittests) - can use it in any order.
 
-This order problem doesn't exist with ``@pytest.mark.parametrize``, you can put it first or last and it will still work. But it only works with non-unittests.
+This section will be expanded soon once our work in progress on those decorators is finished.
 
 Inside tests:
 
@@ -765,10 +748,12 @@ or skip the whole module:
 
 More details, example and ways are `here <https://docs.pytest.org/en/latest/skipping.html>`__.
 
-Slow tests
+Custom markers
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The library of tests is ever-growing, and some of the tests take minutes to run, therefore we can't afford waiting for an hour for the test suite to complete on CI. Therefore, with some exceptions for essential tests, slow tests should be marked as in the example below:
+* Slow tests
+
+Tests that are too slow (e.g. once downloading huge model files) are marked with:
 
 .. code-block:: python
 
@@ -776,13 +761,13 @@ The library of tests is ever-growing, and some of the tests take minutes to run,
     @slow
     def test_integration_foo():
 
-Once a test is marked as ``@slow``, to run such tests set ``RUN_SLOW=1`` env var, e.g.:
+To run such tests set ``RUN_SLOW=1`` env var, e.g.:
 
 .. code-block:: bash
 
     RUN_SLOW=1 pytest tests
     
-Some decorators like ``@parameterized`` rewrite test names, therefore ``@slow`` and the rest of the skip decorators ``@require_*`` have to be listed last for them to work correctly. Here is an example of the correct usage:
+Some decorators like ``@parametrized`` rewrite test names, therefore ``@slow`` and the rest of the skip decorators ``@require_*`` have to be listed last for them to work correctly. Here is an example of the correct usage:
 
 .. code-block:: python
 
@@ -790,32 +775,6 @@ Some decorators like ``@parameterized`` rewrite test names, therefore ``@slow``
     @slow
     def test_integration_foo():
 
-As explained at the beginning of this document, slow tests get to run on a scheduled basis, rather than in PRs CI checks. So it's possible that some problems will be missed during a PR submission and get merged. Such problems will get caught during the next scheduled CI job. But it also means that it's important to run the slow tests on your machine before submitting the PR.
-
-Here is a rough decision making mechanism for choosing which tests should be marked as slow:
-
-If the test is focused on one of the library's internal components (e.g., modeling files, tokenization files, pipelines), then we should run that test in the non-slow test suite. If it's focused on an other aspect of the library, such as the documentation or the examples, then we should run these tests in the slow test suite. And then, to refine this approach we should have exceptions:
-
-* All tests that need to download a heavy set of weights (e.g., model or tokenizer integration tests, pipeline integration tests) should be set to slow. If you're adding a new model, you should create and upload to the hub a tiny version of it (with random weights) for integration tests. This is discussed in the following paragraphs.
-* All tests that need to do a training not specifically optimized to be fast should be set to slow.
-* We can introduce exceptions if some of these should-be-non-slow tests are excruciatingly slow, and set them to ``@slow``. Auto-modeling tests, which save and load large files to disk, are a good example of tests that are marked as ``@slow``.
-* If a test completes under 1 second on CI (including downloads if any) then it should be a normal test regardless.
-
-Collectively, all the non-slow tests need to cover entirely the different internals, while remaining fast.
-For example, a significant coverage can be achieved by testing with specially created tiny models with random weights. Such models have the very minimal number of layers (e.g., 2), vocab size (e.g., 1000), etc.
-Then the ``@slow`` tests can use large slow models to do qualitative testing. To see the use of these simply look for *tiny* models with:
-
-.. code-block:: bash
-
-    grep tiny tests examples
-
-Here is a an example of a `script <https://github.com/huggingface/transformers/blob/master/scripts/fsmt/fsmt-make-tiny-model.py>`__ that created the tiny model `stas/tiny-wmt19-en-de <https://huggingface.co/stas/tiny-wmt19-en-de>`__. You can easily adjust it to your specific model's architecture.
-
-It's easy to measure the run-time incorrectly if for example there is an overheard of downloading a huge model, but if you test it locally the downloaded files would be cached and thus the download time not measured. Hence check the execution speed report in CI logs instead (the output of ``pytest --durations=0 tests``).
-
-That report is also useful to find slow outliers that aren't marked as such, or which need to be re-written to be fast. If you notice that the test suite starts getting slow on CI, the top listing of this report will show the slowest tests.
-
-
 Testing the stdout/stderr output
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/training.rst

@@ -109,9 +109,9 @@ The following is equivalent to the previous example:
 .. code-block:: python
 
     from torch.nn import functional as F
-    labels = torch.tensor([1,0])
+    labels = torch.tensor([1,0]).unsqueeze(0)
     outputs = model(input_ids, attention_mask=attention_mask)
-    loss = F.cross_entropy(outputs.logits, labels)
+    loss = F.cross_entropy(labels, outputs.logitd)
     loss.backward()
     optimizer.step()
 

examples/conftest.py

@@ -2,7 +2,6 @@
 # by pytest before any tests are run
 
 import sys
-import warnings
 from os.path import abspath, dirname, join
 
 
@@ -10,7 +9,3 @@ from os.path import abspath, dirname, join
 # 'pip install -e .[dev]' when switching between checkouts and running tests.
 git_repo_path = abspath(join(dirname(dirname(__file__)), "src"))
 sys.path.insert(1, git_repo_path)
-
-# silence FutureWarning warnings in tests since often we can't act on them until
-# they become normal warnings - i.e. the tests still need to test the current functionality
-warnings.simplefilter(action="ignore", category=FutureWarning)

examples/language-modeling/README.md

@@ -45,8 +45,6 @@ slightly slower (over-fitting takes more epochs).
 
 We use the `--mlm` flag so that the script may change its loss function.
 
-If using whole-word masking, use both the`--mlm` and `--wwm` flags.
-
 ```bash
 export TRAIN_FILE=/path/to/dataset/wiki.train.raw
 export TEST_FILE=/path/to/dataset/wiki.test.raw
@@ -59,55 +57,7 @@ python run_language_modeling.py \
     --train_data_file=$TRAIN_FILE \
     --do_eval \
     --eval_data_file=$TEST_FILE \
-    --mlm \
+    --mlm
-    --wwm
-```
-
-For Chinese models, it's same with English model with only --mlm`. If using whole-word masking, we need to generate a reference files, case it's char level.
-
-**Q :** Why ref file ?
-
-**A :** Suppose we have a Chinese sentence like : `我喜欢你` The original Chinese-BERT will tokenize it as `['我','喜','欢','你']` in char level.
-Actually, `喜欢` is a whole word. For whole word mask proxy, We need res like `['我','喜','##欢','你']`.
-So we need a ref file to tell model which pos of BERT original token should be added `##`.
-
-**Q :** Why LTP ?
-
-**A :** Cause the best known Chinese WWM BERT is [Chinese-BERT-wwm](https://github.com/ymcui/Chinese-BERT-wwm) by HIT. It works well on so many Chines Task like CLUE (Chinese GLUE).
-They use LTP, so if we want to fine-tune their model, we need LTP.
-
-```bash
-export TRAIN_FILE=/path/to/dataset/wiki.train.raw
-export LTP_RESOURCE=/path/to/ltp/tokenizer
-export BERT_RESOURCE=/path/to/bert/tokenizer
-export SAVE_PATH=/path/to/data/ref.txt
-
-python chinese_ref.py \
-    --file_name=$TRAIN_FILE \
-    --ltp=$LTP_RESOURCE
-    --bert=$BERT_RESOURCE \
-    --save_path=$SAVE_PATH 
-```
-Now Chinese Ref is only supported by `LineByLineWithRefDataset` Class, so we need add `line_by_line` flag: 
-
-
-```bash
-export TRAIN_FILE=/path/to/dataset/wiki.train.raw
-export TEST_FILE=/path/to/dataset/wiki.test.raw
-export REF_FILE=/path/to/ref.txt
-
-python run_language_modeling.py \
-    --output_dir=output \
-    --model_type=roberta \
-    --model_name_or_path=roberta-base \
-    --do_train \
-    --train_data_file=$TRAIN_FILE \
-    --chinese_ref_file=$REF_FILE \
-    --do_eval \
-    --eval_data_file=$TEST_FILE \
-    --mlm \
-    --line_by_line \
-    --wwm
 ```
 
 ### XLNet and permutation language modeling

examples/language-modeling/chinese_ref.py

@@ -1,147 +0,0 @@
-import argparse
-import json
-from typing import List
-
-from ltp import LTP
-from transformers.tokenization_bert import BertTokenizer
-
-
-def _is_chinese_char(cp):
-    """Checks whether CP is the codepoint of a CJK character."""
-    # This defines a "chinese character" as anything in the CJK Unicode block:
-    #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
-    #
-    # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
-    # despite its name. The modern Korean Hangul alphabet is a different block,
-    # as is Japanese Hiragana and Katakana. Those alphabets are used to write
-    # space-separated words, so they are not treated specially and handled
-    # like the all of the other languages.
-    if (
-        (cp >= 0x4E00 and cp <= 0x9FFF)
-        or (cp >= 0x3400 and cp <= 0x4DBF)  #
-        or (cp >= 0x20000 and cp <= 0x2A6DF)  #
-        or (cp >= 0x2A700 and cp <= 0x2B73F)  #
-        or (cp >= 0x2B740 and cp <= 0x2B81F)  #
-        or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
-        or (cp >= 0xF900 and cp <= 0xFAFF)
-        or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
-    ):  #
-        return True
-
-    return False
-
-
-def is_chinese(word: str):
-    # word like '180' or '身高' or '神'
-    for char in word:
-        char = ord(char)
-        if not _is_chinese_char(char):
-            return 0
-    return 1
-
-
-def get_chinese_word(tokens: List[str]):
-    word_set = set()
-
-    for token in tokens:
-        chinese_word = len(token) > 1 and is_chinese(token)
-        if chinese_word:
-            word_set.add(token)
-    word_list = list(word_set)
-    return word_list
-
-
-def add_sub_symbol(bert_tokens: List[str], chinese_word_set: set()):
-    if not chinese_word_set:
-        return bert_tokens
-    max_word_len = max([len(w) for w in chinese_word_set])
-
-    bert_word = bert_tokens
-    start, end = 0, len(bert_word)
-    while start < end:
-        single_word = True
-        if is_chinese(bert_word[start]):
-            l = min(end - start, max_word_len)
-            for i in range(l, 1, -1):
-                whole_word = "".join(bert_word[start : start + i])
-                if whole_word in chinese_word_set:
-                    for j in range(start + 1, start + i):
-                        bert_word[j] = "##" + bert_word[j]
-                    start = start + i
-                    single_word = False
-                    break
-        if single_word:
-            start += 1
-    return bert_word
-
-
-def prepare_ref(lines: List[str], ltp_tokenizer: LTP, bert_tokenizer: BertTokenizer):
-    ltp_res = []
-
-    for i in range(0, len(lines), 100):
-        res = ltp_tokenizer.seg(lines[i : i + 100])[0]
-        res = [get_chinese_word(r) for r in res]
-        ltp_res.extend(res)
-    assert len(ltp_res) == len(lines)
-
-    bert_res = []
-    for i in range(0, len(lines), 100):
-        res = bert_tokenizer(lines[i : i + 100], add_special_tokens=True, truncation=True, max_length=512)
-        bert_res.extend(res["input_ids"])
-    assert len(bert_res) == len(lines)
-
-    ref_ids = []
-    for input_ids, chinese_word in zip(bert_res, ltp_res):
-
-        input_tokens = []
-        for id in input_ids:
-            token = bert_tokenizer._convert_id_to_token(id)
-            input_tokens.append(token)
-        input_tokens = add_sub_symbol(input_tokens, chinese_word)
-        ref_id = []
-        # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
-        for i, token in enumerate(input_tokens):
-            if token[:2] == "##":
-                clean_token = token[2:]
-                # save chinese tokens' pos
-                if len(clean_token) == 1 and _is_chinese_char(ord(clean_token)):
-                    ref_id.append(i)
-        ref_ids.append(ref_id)
-
-    assert len(ref_ids) == len(bert_res)
-
-    return ref_ids
-
-
-def main(args):
-    # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
-    # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
-    with open(args.file_name, "r", encoding="utf-8") as f:
-        data = f.readlines()
-
-    ltp_tokenizer = LTP(args.ltp)  # faster in GPU device
-    bert_tokenizer = BertTokenizer.from_pretrained(args.bert)
-
-    ref_ids = prepare_ref(data, ltp_tokenizer, bert_tokenizer)
-
-    with open(args.save_path, "w", encoding="utf-8") as f:
-        data = [json.dumps(ref) + "\n" for ref in ref_ids]
-        f.writelines(data)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="prepare_chinese_ref")
-    parser.add_argument(
-        "--file_name",
-        type=str,
-        default="./resources/chinese-demo.txt",
-        help="file need process, same as training data in lm",
-    )
-    parser.add_argument(
-        "--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
-    )
-    parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
-    parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")
-
-    args = parser.parse_args()
-    main(args)

examples/language-modeling/run_language_modeling.py

@@ -24,11 +24,8 @@ import logging
 import math
 import os
 from dataclasses import dataclass, field
-from glob import glob
 from typing import Optional
 
-from torch.utils.data import ConcatDataset
-
 from transformers import (
     CONFIG_MAPPING,
     MODEL_WITH_LM_HEAD_MAPPING,
@@ -37,10 +34,8 @@ from transformers import (
     AutoTokenizer,
     DataCollatorForLanguageModeling,
     DataCollatorForPermutationLanguageModeling,
-    DataCollatorForWholeWordMask,
     HfArgumentParser,
     LineByLineTextDataset,
-    LineByLineWithRefDataset,
     PreTrainedTokenizer,
     TextDataset,
     Trainer,
@@ -92,21 +87,10 @@ class DataTrainingArguments:
     train_data_file: Optional[str] = field(
         default=None, metadata={"help": "The input training data file (a text file)."}
     )
-    train_data_files: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "The input training data files (multiple files in glob format). "
-            "Very often splitting large files to smaller files can prevent tokenizer going out of memory"
-        },
-    )
     eval_data_file: Optional[str] = field(
         default=None,
         metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
     )
-    chinese_ref_file: Optional[str] = field(
-        default=None,
-        metadata={"help": "An optional input ref data file for whole word mask in Chinees."},
-    )
     line_by_line: bool = field(
         default=False,
         metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."},
@@ -115,7 +99,6 @@ class DataTrainingArguments:
     mlm: bool = field(
         default=False, metadata={"help": "Train with masked-language modeling loss instead of language modeling."}
     )
-    whole_word_mask: bool = field(default=False, metadata={"help": "Whether ot not to use whole word mask."})
     mlm_probability: float = field(
         default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
     )
@@ -148,34 +131,17 @@ def get_dataset(
     evaluate: bool = False,
     cache_dir: Optional[str] = None,
 ):
-    def _dataset(file_path):
+    file_path = args.eval_data_file if evaluate else args.train_data_file
-        if args.line_by_line:
+    if args.line_by_line:
-            if args.chinese_ref_file is not None:
+        return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)
-                if not args.whole_word_mask or not args.mlm:
-                    raise ValueError("You need to set world whole masking and mlm to True for Chinese Whole Word Mask")
-                return LineByLineWithRefDataset(
-                    tokenizer=tokenizer,
-                    file_path=file_path,
-                    block_size=args.block_size,
-                    ref_path=args.chinese_ref_file,
-                )
-
-            return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)
-        else:
-            return TextDataset(
-                tokenizer=tokenizer,
-                file_path=file_path,
-                block_size=args.block_size,
-                overwrite_cache=args.overwrite_cache,
-                cache_dir=cache_dir,
-            )
-
-    if evaluate:
-        return _dataset(args.eval_data_file)
-    elif args.train_data_files:
-        return ConcatDataset([_dataset(f) for f in glob(args.train_data_files)])
     else:
-        return _dataset(args.train_data_file)
+        return TextDataset(
+            tokenizer=tokenizer,
+            file_path=file_path,
+            block_size=args.block_size,
+            overwrite_cache=args.overwrite_cache,
+            cache_dir=cache_dir,
+        )
 
 
 def main():
@@ -191,6 +157,7 @@ def main():
             "Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
             "or remove the --do_eval argument."
         )
+
     if (
         os.path.exists(training_args.output_dir)
         and os.listdir(training_args.output_dir)
@@ -286,14 +253,9 @@ def main():
             max_span_length=data_args.max_span_length,
         )
     else:
-        if data_args.mlm and data_args.whole_word_mask:
+        data_collator = DataCollatorForLanguageModeling(
-            data_collator = DataCollatorForWholeWordMask(
+            tokenizer=tokenizer, mlm=data_args.mlm, mlm_probability=data_args.mlm_probability
-                tokenizer=tokenizer, mlm_probability=data_args.mlm_probability
+        )
-            )
-        else:
-            data_collator = DataCollatorForLanguageModeling(
-                tokenizer=tokenizer, mlm=data_args.mlm, mlm_probability=data_args.mlm_probability
-            )
 
     # Initialize our Trainer
     trainer = Trainer(

examples/lightning_base.py

@@ -119,7 +119,7 @@ class BaseTransformer(pl.LightningModule):
     def get_lr_scheduler(self):
         get_schedule_func = arg_to_scheduler[self.hparams.lr_scheduler]
         scheduler = get_schedule_func(
-            self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=self.total_steps()
+            self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=self.total_steps
         )
         scheduler = {"scheduler": scheduler, "interval": "step", "frequency": 1}
         return scheduler
@@ -159,20 +159,19 @@ class BaseTransformer(pl.LightningModule):
     def test_epoch_end(self, outputs):
         return self.validation_end(outputs)
 
+    @property
     def total_steps(self) -> int:
         """The number of total training steps that will be run. Used for lr scheduler purposes."""
         num_devices = max(1, self.hparams.gpus)  # TODO: consider num_tpu_cores
         effective_batch_size = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
-        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
+        dataset_size = len(self.train_loader.dataset)
+        return (dataset_size / effective_batch_size) * self.hparams.max_epochs
 
     def setup(self, mode):
-        if mode == "test":
+        if mode == "fit":
-            self.dataset_size = len(self.test_dataloader().dataset)
-        else:
             self.train_loader = self.get_dataloader("train", self.hparams.train_batch_size, shuffle=True)
-            self.dataset_size = len(self.train_dataloader().dataset)
 
-    def get_dataloader(self, type_path: str, batch_size: int, shuffle: bool = False):
+    def get_dataloader(self, type_path, batch_size, shuffle=False):
         raise NotImplementedError("You must implement this for your task")
 
     def train_dataloader(self):
@@ -291,8 +290,7 @@ class LoggingCallback(pl.Callback):
 
 
 def add_generic_args(parser, root_dir) -> None:
-    #  To allow all pl args uncomment the following line
+    #  TODO(SS): allow all pl args? parser = pl.Trainer.add_argparse_args(parser)
-    #  parser = pl.Trainer.add_argparse_args(parser)
     parser.add_argument(
         "--output_dir",
         default=None,

examples/question-answering/run_squad.py

@@ -187,7 +187,7 @@ def train(args, train_dataset, model, tokenizer):
                 "end_positions": batch[4],
             }
 
-            if args.model_type in ["xlm", "roberta", "distilbert", "camembert", "bart", "longformer"]:
+            if args.model_type in ["xlm", "roberta", "distilbert", "camembert", "bart"]:
                 del inputs["token_type_ids"]
 
             if args.model_type in ["xlnet", "xlm"]:
@@ -300,7 +300,7 @@ def evaluate(args, model, tokenizer, prefix=""):
                 "token_type_ids": batch[2],
             }
 
-            if args.model_type in ["xlm", "roberta", "distilbert", "camembert", "bart", "longformer"]:
+            if args.model_type in ["xlm", "roberta", "distilbert", "camembert", "bart"]:
                 del inputs["token_type_ids"]
 
             feature_indices = batch[3]

examples/rag/README.md

@@ -65,41 +65,26 @@ Does He Love You	Does He Love You	Red Sandy Spika dress of Reba McEntire	Greates
 We demonstrate how to evaluate retrieval against DPR evaluation data. You can download respective files from links listed [here](https://github.com/facebookresearch/DPR/blob/master/data/download_data.py#L39-L45).
 
 1. Download and unzip the gold data file. We use the `biencoder-nq-dev` from https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz.
-    ```bash
-    wget https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz && gzip -d biencoder-nq-dev.json.gz
-   ```
-
 2. Parse the unziped file using the `parse_dpr_relevance_data.py`
     ```bash
-    mkdir output # or wherever you want to save this
     python examples/rag/parse_dpr_relevance_data.py \
-        --src_path biencoder-nq-dev.json \
+        --src_path path/to/unziped/biencoder-nq-dev.json \
-        --evaluation_set output/biencoder-nq-dev.questions \
+        --evaluation_set path/to/output/biencoder-nq-dev.questions \
-        --gold_data_path output/biencoder-nq-dev.pages
+        --gold_data_path path/to/output/biencoder-nq-dev.pages
     ```
 3. Run evaluation:
-    ```bash    
+    ```bash
-    python examples/rag/eval_rag.py \
-        --model_name_or_path facebook/rag-sequence-nq \
-        --model_type rag_sequence \
-        --evaluation_set output/biencoder-nq-dev.questions \
-        --gold_data_path output/biencoder-nq-dev.pages \
-        --predictions_path output/retrieval_preds.tsv  \
-        --eval_mode retrieval \
-        --k 1
-    ```
-   ```bash
-   # EXPLANATION
     python examples/rag/eval_rag.py \
         --model_name_or_path facebook/rag-sequence-nq \ # model name or path of the model we're evaluating
         --model_type rag_sequence \ # RAG model type (rag_token or rag_sequence)
-        --evaluation_set output/biencoder-nq-dev.questions \ # an input dataset for evaluation
+        --evaluation_set path/to/output/biencoder-nq-dev.questions \ # an input dataset for evaluation
-        --gold_data_path poutput/biencoder-nq-dev.pages \ # a dataset containing ground truth answers for samples from the evaluation_set
+        --gold_data_path path/to/output/biencoder-nq-dev.pages \ # a dataset containing ground truth answers for samples from the evaluation_set
-        --predictions_path output/retrieval_preds.tsv  \ # name of file where predictions will be stored
+        --predictions_path path/to/retrieval_preds.tsv  \ # name of file where predictions will be stored
         --eval_mode retrieval \ # indicates whether we're performing retrieval evaluation or e2e evaluation
         --k 1 # parameter k for the precision@k metric
-   
     ```
+
+
 ## End-to-end evaluation
 
 We support two formats of the gold data file (controlled by the `gold_data_mode` parameter):
@@ -112,9 +97,7 @@ who is the owner of reading football club	['Xiu Li Dai', 'Dai Yongge', 'Dai Xiul
 Xiu Li Dai
 ```
 
-Predictions of the model for the samples from the `evaluation_set` will be saved under the path specified by the `predictions_path` parameter. 
+Predictions of the model for the samples from the `evaluation_set` will be saved under the path specified by the `predictions_path` parameter. If this path already exists, the script will use saved predictions to calculate metrics. Add `--recalculate` parameter to force the script to perform inference from scratch.
-If this path already exists, the script will use saved predictions to calculate metrics. 
-Add `--recalculate` parameter to force the script to perform inference from scratch.
 
 An example e2e evaluation run could look as follows:
 ```bash

examples/rag/__init__.py

@@ -1,5 +0,0 @@
-import os
-import sys
-
-
-sys.path.insert(1, os.path.dirname(os.path.realpath(__file__)))

examples/rag/distributed_retriever.py

@@ -27,18 +27,13 @@ class RagPyTorchDistributedRetriever(RagRetriever):
             It is used to decode the question and then use the generator_tokenizer.
         generator_tokenizer (:class:`~transformers.PretrainedTokenizer`):
             The tokenizer used for the generator part of the RagModel.
-        index (:class:`~transformers.retrieval_rag.Index`, optional, defaults to the one defined by the configuration):
-            If specified, use this index instead of the one built using the configuration
     """
 
     _init_retrieval = False
 
-    def __init__(self, config, question_encoder_tokenizer, generator_tokenizer, index=None):
+    def __init__(self, config, question_encoder_tokenizer, generator_tokenizer):
         super().__init__(
-            config,
+            config, question_encoder_tokenizer=question_encoder_tokenizer, generator_tokenizer=generator_tokenizer
-            question_encoder_tokenizer=question_encoder_tokenizer,
-            generator_tokenizer=generator_tokenizer,
-            index=index,
         )
 
         self.process_group = None

examples/rag/eval_rag.py

@@ -15,7 +15,7 @@ from transformers import logging as transformers_logging
 
 
 sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
-from utils import exact_match_score, f1_score  # noqa: E402 # isort:skip
+from examples.rag.utils import exact_match_score, f1_score  # noqa: E402 # isort:skip
 
 
 logger = logging.getLogger(__name__)
@@ -72,7 +72,7 @@ def get_precision_at_k(args, preds_path, gold_data_path):
     em = total = 0
     for hypo, reference in zip(hypos, references):
         hypo_provenance = set(hypo.split("\t")[:k])
-        ref_provenance = set(reference.split("\t"))
+        ref_provenance = set(reference.split("\t")[1 : (k + 1)])
         total += 1
         em += len(hypo_provenance & ref_provenance) / k
 

examples/rag/finetune.py

@@ -31,13 +31,16 @@ from transformers import (
 from transformers import logging as transformers_logging
 
 
-from callbacks import (  # noqa: E402 # isort:skipq
+sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip
+
+from examples.lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa: E402 # isort:skip
+from examples.rag.callbacks import (  # noqa: E402 # isort:skip
     get_checkpoint_callback,
     get_early_stopping_callback,
     Seq2SeqLoggingCallback,
 )
-from distributed_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
+from examples.rag.distributed_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
-from utils import (  # noqa: E402 # isort:skip
+from examples.rag.utils import (  # noqa: E402 # isort:skip
     calculate_exact_match,
     flatten_list,
     get_git_info,
@@ -50,11 +53,6 @@ from utils import (  # noqa: E402 # isort:skip
     Seq2SeqDataset,
 )
 
-# need the parent dir module
-sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
-from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa
-
-
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
@@ -90,11 +88,6 @@ class GenerativeQAModule(BaseTransformer):
         config_class = RagConfig if self.is_rag_model else AutoConfig
         config = config_class.from_pretrained(hparams.model_name_or_path)
 
-        # set retriever parameters
-        config.index_name = args.index_name or config.index_name
-        config.passages_path = args.passages_path or config.passages_path
-        config.index_path = args.index_path or config.index_path
-
         # set extra_model_params for generator configs and load_model
         extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "attention_dropout", "dropout")
         if self.is_rag_model:
@@ -102,7 +95,7 @@ class GenerativeQAModule(BaseTransformer):
                 config.generator.prefix = args.prefix
             config.label_smoothing = hparams.label_smoothing
             hparams, config.generator = set_extra_model_params(extra_model_params, hparams, config.generator)
-            retriever = RagPyTorchDistributedRetriever.from_pretrained(hparams.model_name_or_path, config=config)
+            retriever = RagPyTorchDistributedRetriever.from_pretrained(hparams.model_name_or_path)
             model = self.model_class.from_pretrained(hparams.model_name_or_path, config=config, retriever=retriever)
             prefix = config.question_encoder.prefix
         else:
@@ -410,28 +403,6 @@ class GenerativeQAModule(BaseTransformer):
         )
         return parser
 
-    @staticmethod
-    def add_retriever_specific_args(parser):
-        parser.add_argument(
-            "--index_name",
-            type=str,
-            default=None,
-            help="Name of the index to use: 'hf' for a canonical dataset from the datasets library (default), 'custom' for a local index, or 'legacy' for the orignal one)",
-        )
-        parser.add_argument(
-            "--passages_path",
-            type=str,
-            default=None,
-            help="Path to the dataset of passages for custom index. More info about custom indexes in the RagRetriever documentation as well as in `examples/rag/use_own_knowledge_dataset.py`",
-        )
-        parser.add_argument(
-            "--index_path",
-            type=str,
-            default=None,
-            help="Path to the faiss index for custom index. More info about custom indexes in the RagRetriever documentation as well as in `examples/rag/use_own_knowledge_dataset.py`",
-        )
-        return parser
-
 
 def main(args, model=None) -> GenerativeQAModule:
     Path(args.output_dir).mkdir(exist_ok=True)
@@ -492,7 +463,6 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser = pl.Trainer.add_argparse_args(parser)
     parser = GenerativeQAModule.add_model_specific_args(parser, os.getcwd())
-    parser = GenerativeQAModule.add_retriever_specific_args(parser)
 
     args = parser.parse_args()
 

examples/rag/test_data/my_knowledge_dataset.csv

@@ -1,2 +0,0 @@
-Aaron	Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother's spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from God at Sinai granted Aaron the priesthood for himself and his male descendants, and he became the first High Priest of the Israelites. Aaron died before the Israelites crossed the North Jordan river and he was buried on Mount Hor (Numbers 33:39; Deuteronomy 10:6 says he died and was buried at Moserah). Aaron is also mentioned in the New Testament of the Bible. According to the Book of Exodus, Aaron first functioned as Moses' assistant. Because Moses complained that he could not speak well, God appointed Aaron as Moses' "prophet" (Exodus 4:10-17; 7:1). At the command of Moses, he let his rod turn into a snake. Then he stretched out his rod in order to bring on the first three plagues. After that, Moses tended to act and speak for himself. During the journey in the wilderness, Aaron was not always prominent or active. At the battle with Amalek, he was chosen with Hur to support the hand of Moses that held the "rod of God". When the revelation was given to Moses at biblical Mount Sinai, he headed the elders of Israel who accompanied Moses on the way to the summit.
-"Pokémon"	Pokémon , also known as in Japan, is a media franchise managed by The Pokémon Company, a Japanese consortium between Nintendo, Game Freak, and Creatures. The franchise copyright is shared by all three companies, but Nintendo is the sole owner of the trademark. The franchise was created by Satoshi Tajiri in 1995, and is centered on fictional creatures called "Pokémon", which humans, known as Pokémon Trainers, catch and train to battle each other for sport. The English slogan for the franchise is "Gotta Catch 'Em All". Works within the franchise are set in the Pokémon universe. The franchise began as "Pokémon Red" and "Green" (released outside of Japan as "Pokémon Red" and "Blue"), a pair of video games for the original Game Boy that were developed by Game Freak and published by Nintendo in February 1996. "Pokémon" has since gone on to become the highest-grossing media franchise of all time, with over in revenue up until March 2017. The original video game series is the second best-selling video game franchise (behind Nintendo's "Mario" franchise) with more than 300million copies sold and over 800million mobile downloads. In addition, the "Pokémon" franchise includes the world's top-selling toy brand, the top-selling trading card game with over 25.7billion cards sold, an anime television series that has become the most successful video game adaptation with over 20 seasons and 1,000 episodes in 124 countries, as well as an anime film series, a , books, manga comics, music, and merchandise. The franchise is also represented in other Nintendo media, such as the "Super Smash Bros." series. In November 2005, 4Kids Entertainment, which had managed the non-game related licensing of "Pokémon", announced that it had agreed not to renew the "Pokémon" representation agreement. The Pokémon Company International oversees all "Pokémon" licensing outside Asia.

examples/rag/test_distributed_retriever.py

@@ -15,7 +15,6 @@ from transformers.configuration_bart import BartConfig
 from transformers.configuration_dpr import DPRConfig
 from transformers.configuration_rag import RagConfig
 from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
-from transformers.retrieval_rag import CustomHFIndex
 from transformers.tokenization_bart import BartTokenizer
 from transformers.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
 from transformers.tokenization_dpr import DPRQuestionEncoderTokenizer
@@ -24,7 +23,7 @@ from transformers.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FI
 
 sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip
 
-from distributed_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
+from examples.rag.distributed_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
 
 
 def require_distributed_retrieval(test_case):
@@ -115,7 +114,7 @@ class RagRetrieverTest(TestCase):
     def tearDown(self):
         shutil.rmtree(self.tmpdirname)
 
-    def get_dummy_dataset(self):
+    def get_dummy_pytorch_distributed_retriever(self, init_retrieval, port=12345) -> RagPyTorchDistributedRetriever:
         dataset = Dataset.from_dict(
             {
                 "id": ["0", "1"],
@@ -125,12 +124,6 @@ class RagRetrieverTest(TestCase):
             }
         )
         dataset.add_faiss_index("embeddings", string_factory="Flat", metric_type=faiss.METRIC_INNER_PRODUCT)
-        return dataset
-
-    def get_dummy_pytorch_distributed_retriever(
-        self, init_retrieval: bool, port=12345
-    ) -> RagPyTorchDistributedRetriever:
-        dataset = self.get_dummy_dataset()
         config = RagConfig(
             retrieval_vector_size=self.retrieval_vector_size,
             question_encoder=DPRConfig().to_dict(),
@@ -147,37 +140,6 @@ class RagRetrieverTest(TestCase):
                 retriever.init_retrieval(port)
         return retriever
 
-    def get_dummy_custom_hf_index_retriever(self, init_retrieval: bool, from_disk: bool, port=12345):
-        dataset = self.get_dummy_dataset()
-        config = RagConfig(
-            retrieval_vector_size=self.retrieval_vector_size,
-            question_encoder=DPRConfig().to_dict(),
-            generator=BartConfig().to_dict(),
-            index_name="custom",
-        )
-        if from_disk:
-            config.passages_path = os.path.join(self.tmpdirname, "dataset")
-            config.index_path = os.path.join(self.tmpdirname, "index.faiss")
-            dataset.get_index("embeddings").save(os.path.join(self.tmpdirname, "index.faiss"))
-            dataset.drop_index("embeddings")
-            dataset.save_to_disk(os.path.join(self.tmpdirname, "dataset"))
-            del dataset
-            retriever = RagPyTorchDistributedRetriever(
-                config,
-                question_encoder_tokenizer=self.get_dpr_tokenizer(),
-                generator_tokenizer=self.get_bart_tokenizer(),
-            )
-        else:
-            retriever = RagPyTorchDistributedRetriever(
-                config,
-                question_encoder_tokenizer=self.get_dpr_tokenizer(),
-                generator_tokenizer=self.get_bart_tokenizer(),
-                index=CustomHFIndex(config.retrieval_vector_size, dataset),
-            )
-        if init_retrieval:
-            retriever.init_retrieval(port)
-        return retriever
-
     def test_pytorch_distributed_retriever_retrieve(self):
         n_docs = 1
         retriever = self.get_dummy_pytorch_distributed_retriever(init_retrieval=True)
@@ -192,33 +154,3 @@ class RagRetrieverTest(TestCase):
         self.assertEqual(doc_dicts[0]["id"][0], "1")  # max inner product is reached with second doc
         self.assertEqual(doc_dicts[1]["id"][0], "0")  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist(), [[1], [0]])
-
-    def test_custom_hf_index_retriever_retrieve(self):
-        n_docs = 1
-        retriever = self.get_dummy_custom_hf_index_retriever(init_retrieval=True, from_disk=False)
-        hidden_states = np.array(
-            [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
-        )
-        retrieved_doc_embeds, doc_ids, doc_dicts = retriever.retrieve(hidden_states, n_docs=n_docs)
-        self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size))
-        self.assertEqual(len(doc_dicts), 2)
-        self.assertEqual(sorted(doc_dicts[0]), ["embeddings", "id", "text", "title"])
-        self.assertEqual(len(doc_dicts[0]["id"]), n_docs)
-        self.assertEqual(doc_dicts[0]["id"][0], "1")  # max inner product is reached with second doc
-        self.assertEqual(doc_dicts[1]["id"][0], "0")  # max inner product is reached with first doc
-        self.assertListEqual(doc_ids.tolist(), [[1], [0]])
-
-    def test_custom_pytorch_distributed_retriever_retrieve_from_disk(self):
-        n_docs = 1
-        retriever = self.get_dummy_custom_hf_index_retriever(init_retrieval=True, from_disk=True)
-        hidden_states = np.array(
-            [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
-        )
-        retrieved_doc_embeds, doc_ids, doc_dicts = retriever.retrieve(hidden_states, n_docs=n_docs)
-        self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size))
-        self.assertEqual(len(doc_dicts), 2)
-        self.assertEqual(sorted(doc_dicts[0]), ["embeddings", "id", "text", "title"])
-        self.assertEqual(len(doc_dicts[0]["id"]), n_docs)
-        self.assertEqual(doc_dicts[0]["id"][0], "1")  # max inner product is reached with second doc
-        self.assertEqual(doc_dicts[1]["id"][0], "0")  # max inner product is reached with first doc
-        self.assertListEqual(doc_ids.tolist(), [[1], [0]])

examples/rag/use_own_knowledge_dataset.py

@@ -1,200 +0,0 @@
-import logging
-import os
-from dataclasses import dataclass, field
-from functools import partial
-from pathlib import Path
-from tempfile import TemporaryDirectory
-from typing import List, Optional
-
-import torch
-from datasets import load_dataset
-
-import faiss
-from transformers import (
-    DPRContextEncoder,
-    DPRContextEncoderTokenizerFast,
-    HfArgumentParser,
-    RagRetriever,
-    RagSequenceForGeneration,
-    RagTokenizer,
-)
-
-
-logger = logging.getLogger(__name__)
-torch.set_grad_enabled(False)
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-def split_text(text: str, n=100, character=" ") -> List[str]:
-    """Split the text every ``n``-th occurence of ``character``"""
-    text = text.split(character)
-    return [character.join(text[i : i + n]).strip() for i in range(0, len(text), n)]
-
-
-def split_documents(documents: dict) -> dict:
-    """Split documents into passages"""
-    titles, texts = [], []
-    for title, text in zip(documents["title"], documents["text"]):
-        if text is not None:
-            for passage in split_text(text):
-                titles.append(title if title is not None else "")
-                texts.append(passage)
-    return {"title": titles, "text": texts}
-
-
-def embed(documents: dict, ctx_encoder: DPRContextEncoder, ctx_tokenizer: DPRContextEncoderTokenizerFast) -> dict:
-    """Compute the DPR embeddings of document passages"""
-    input_ids = ctx_tokenizer(
-        documents["title"], documents["text"], truncation=True, padding="longest", return_tensors="pt"
-    )["input_ids"]
-    embeddings = ctx_encoder(input_ids.to(device=device), return_dict=True).pooler_output
-    return {"embeddings": embeddings.detach().cpu().numpy()}
-
-
-def main(
-    rag_example_args: "RagExampleArguments",
-    processing_args: "ProcessingArguments",
-    index_hnsw_args: "IndexHnswArguments",
-):
-
-    ######################################
-    logger.info("Step 1 - Create the dataset")
-    ######################################
-
-    # The dataset needed for RAG must have three columns:
-    # - title (string): title of the document
-    # - text (string): text of a passage of the document
-    # - embeddings (array of dimension d): DPR representation of the passage
-
-    # Let's say you have documents in tab-separated csv files with columns "title" and "text"
-    assert os.path.isfile(rag_example_args.csv_path), "Please provide a valid path to a csv file"
-
-    # You can load a Dataset object this way
-    dataset = load_dataset(
-        "csv", data_files=[rag_example_args.csv_path], split="train", delimiter="\t", column_names=["title", "text"]
-    )
-
-    # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
-
-    # Then split the documents into passages of 100 words
-    dataset = dataset.map(split_documents, batched=True, num_proc=processing_args.num_proc)
-
-    # And compute the embeddings
-    ctx_encoder = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name).to(device=device)
-    ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name)
-    dataset = dataset.map(
-        partial(embed, ctx_encoder=ctx_encoder, ctx_tokenizer=ctx_tokenizer),
-        batched=True,
-        batch_size=processing_args.batch_size,
-    )
-
-    # And finally save your dataset
-    passages_path = os.path.join(rag_example_args.output_dir, "my_knowledge_dataset")
-    dataset.save_to_disk(passages_path)
-    # from datasets import load_from_disk
-    # dataset = load_from_disk(passages_path)  # to reload the dataset
-
-    ######################################
-    logger.info("Step 2 - Index the dataset")
-    ######################################
-
-    # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
-    index = faiss.IndexHNSWFlat(index_hnsw_args.d, index_hnsw_args.m, faiss.METRIC_INNER_PRODUCT)
-    dataset.add_faiss_index("embeddings", custom_index=index)
-
-    # And save the index
-    index_path = os.path.join(rag_example_args.output_dir, "my_knowledge_dataset_hnsw_index.faiss")
-    dataset.get_index("embeddings").save(index_path)
-    # dataset.load_faiss_index("embeddings", index_path)  # to reload the index
-
-    ######################################
-    logger.info("Step 3 - Load RAG")
-    ######################################
-
-    # Easy way to load the model
-    retriever = RagRetriever.from_pretrained(
-        rag_example_args.rag_model_name, index_name="custom", indexed_dataset=dataset
-    )
-    model = RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name, retriever=retriever)
-    tokenizer = RagTokenizer.from_pretrained(rag_example_args.rag_model_name)
-
-    # For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
-    # retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)
-
-    ######################################
-    logger.info("Step 4 - Have fun")
-    ######################################
-
-    question = rag_example_args.question or "What does Moses' rod turn into ?"
-    input_ids = tokenizer.question_encoder(question, return_tensors="pt")["input_ids"]
-    generated = model.generate(input_ids)
-    generated_string = tokenizer.batch_decode(generated, skip_special_tokens=True)[0]
-    logger.info("Q: " + question)
-    logger.info("A: " + generated_string)
-
-
-@dataclass
-class RagExampleArguments:
-    csv_path: str = field(
-        default=str(Path(__file__).parent / "test_data" / "my_knowledge_dataset.csv"),
-        metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"},
-    )
-    question: Optional[str] = field(
-        default=None,
-        metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."},
-    )
-    rag_model_name: str = field(
-        default="facebook/rag-sequence-nq",
-        metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"},
-    )
-    dpr_ctx_encoder_model_name: str = field(
-        default="facebook/dpr-ctx_encoder-multiset-base",
-        metadata={
-            "help": "The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or 'facebook/dpr-ctx_encoder-multiset-base'"
-        },
-    )
-    output_dir: Optional[str] = field(
-        default=None,
-        metadata={"help": "Path to a directory where the dataset passages and the index will be saved"},
-    )
-
-
-@dataclass
-class ProcessingArguments:
-    num_proc: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "The number of processes to use to split the documents into passages. Default is single process."
-        },
-    )
-    batch_size: int = field(
-        default=16,
-        metadata={
-            "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
-        },
-    )
-
-
-@dataclass
-class IndexHnswArguments:
-    d: int = field(
-        default=768,
-        metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."},
-    )
-    m: int = field(
-        default=128,
-        metadata={
-            "help": "The number of bi-directional links created for every new element during the HNSW index construction."
-        },
-    )
-
-
-if __name__ == "__main__":
-    logging.basicConfig(level=logging.WARNING)
-    logger.setLevel(logging.INFO)
-
-    parser = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
-    rag_example_args, processing_args, index_hnsw_args = parser.parse_args_into_dataclasses()
-    with TemporaryDirectory() as tmp_dir:
-        rag_example_args.output_dir = rag_example_args.output_dir or tmp_dir
-        main(rag_example_args, processing_args, index_hnsw_args)

examples/requirements.txt

@@ -5,7 +5,7 @@ psutil
 sacrebleu
 rouge-score
 tensorflow_datasets
-pytorch-lightning==0.9.0
+pytorch-lightning==0.8.5
 matplotlib
 git-python==1.0.3
 faiss-cpu
@@ -17,4 +17,3 @@ datasets
 fire
 pytest
 conllu
-sentencepiece != 0.1.92

examples/seq2seq/README.md

@@ -12,14 +12,14 @@ For `bertabs` instructions, see [`bertabs/README.md`](bertabs/README.md).
 - `MBartForConditionalGeneration`
 - `FSMTForConditionalGeneration`
 - `T5ForConditionalGeneration`
+    
 
 ## Datasets
 
-#### XSUM
+#### XSUM:
-
 ```bash
 cd examples/seq2seq
-wget https://cdn-datasets.huggingface.co/summarization/xsum.tar.gz
+wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz
 tar -xzvf xsum.tar.gz
 export XSUM_DIR=${PWD}/xsum
 ```
@@ -27,45 +27,31 @@ this should make a directory called `xsum/` with files like `test.source`.
 To use your own data, copy that files format. Each article to be summarized is on its own line.
 
 #### CNN/DailyMail
-
 ```bash
 cd examples/seq2seq
-wget https://cdn-datasets.huggingface.co/summarization/cnn_dm_v2.tgz
+wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/cnn_dm_v2.tgz
 tar -xzvf cnn_dm_v2.tgz  # empty lines removed
 mv cnn_cln cnn_dm
 export CNN_DIR=${PWD}/cnn_dm
 ```
 this should make a directory called `cnn_dm/` with 6 files.
 
-#### WMT16 English-Romanian Translation Data
+#### WMT16 English-Romanian Translation Data:
-
 download with this command:
 ```bash
-wget https://cdn-datasets.huggingface.co/translation/wmt_en_ro.tar.gz
+wget https://s3.amazonaws.com/datasets.huggingface.co/translation/wmt_en_ro.tar.gz
 tar -xzvf wmt_en_ro.tar.gz
 export ENRO_DIR=${PWD}/wmt_en_ro
 ```
 this should make a directory called `wmt_en_ro/` with 6 files.
 
-#### WMT English-German
+#### WMT English-German:
-
 ```bash
-wget https://cdn-datasets.huggingface.co/translation/wmt_en_de.tgz
+wget https://s3.amazonaws.com/datasets.huggingface.co/translation/wmt_en_de.tgz
 tar -xzvf wmt_en_de.tgz
 export DATA_DIR=${PWD}/wmt_en_de
 ```
 
-#### FSMT datasets (wmt)
-
-Refer to the scripts starting with `eval_` under:
-https://github.com/huggingface/transformers/tree/master/scripts/fsmt
-
-#### Pegasus (multiple datasets)
-
-Multiple eval datasets are available for download from: 
-https://github.com/stas00/porting/tree/master/datasets/pegasus
-
-
 #### Private Data
 
 If you are using your own data, it must be formatted as one directory with 6 files:
@@ -79,6 +65,7 @@ test.target
 ```
 The `.source` files are the input, the `.target` files are the desired output.
 
+
 ### Tips and Tricks
 
 General Tips:
@@ -113,7 +100,7 @@ All finetuning bash scripts call finetune.py (or distillation.py) with reasonabl
 To see all the possible command line options, run:
 
 ```bash
-./finetune.py --help
+ ./finetune.py --help 
 ```
 
 ### Finetuning Training Params
@@ -205,7 +192,7 @@ model = AutoModelForSeq2SeqLM.from_pretrained(f'{output_dir}/best_tfmr')
 ### Fine-tuning using Seq2SeqTrainer
 To use `Seq2SeqTrainer` for fine-tuning you should use the `finetune_trainer.py` script. It subclasses `Trainer` to extend it for seq2seq training. Except the `Trainer` releated `TrainingArguments`, it shares the same argument names as that of `finetune.py` file. One notable difference is that, calculating generative metrics (BLEU, ROUGE) is optional and is controlled using the `--predict_with_generate` argument, set this argument to calculate BLEU and ROUGE metrics.
 
-With PyTorch 1.6+ it'll automatically use `native AMP` when `--fp16` is set.
+With PyTorch 1.6+ it'll automatically use `native AMP` when `--fp16` is set. 
 
 To see all the possible command line options, run:
 
@@ -278,7 +265,6 @@ export DATA_DIR=cnn_dm
     --fp16 \
     --bs 32
 ```
-
 ### Multi-GPU Evaluation
 here is a command to run xsum evaluation on 8 GPUS. It is more than linearly faster than run_eval.py in some cases 
 because it uses SortishSampler to minimize padding. You can also use it on 1 GPU. `data_dir` must have 
@@ -405,17 +391,6 @@ runtime: 13H on V-100 16GB GPU.
 pytest examples/seq2seq/
 ```
 
-### Converting pytorch-lightning checkpoints
-pytorch lightning ``-do_predict`` often fails, after you are done training, the best way to evaluate your model is to convert it.
-
-This should be done for you, with a file called `{save_dir}/best_tfmr`. 
-
-If that file doesn't exist but you have a lightning `.ckpt` file, you can run
-```bash
-python convert_pl_checkpoint_to_hf.py PATH_TO_CKPT  randomly_initialized_hf_model_path save_dir/best_tfmr
-```
-Then either `run_eval` or `run_distributed_eval` with `save_dir/best_tfmr` (see previous sections)
-
 
 ## Experimental Features 
 These features are harder to use and not always useful.
@@ -444,3 +419,4 @@ uses 12,723 batches of length 48 and takes slightly more time 9.5 minutes.
 The feature is still experimental, because:
 + we can make it much more robust if we have memory mapped/preprocessed datasets.
 + The speedup over sortish sampler is not that large at the moment.
+

examples/seq2seq/bertabs/README.md

@@ -39,7 +39,7 @@ python run_summarization.py \
     --compute_rouge true
 ```
 
-The scripts executes on GPU if one is available and if `no_cuda` is not set to `true`. Inference on multiple GPUs is not supported yet. The ROUGE scores will be displayed in the console at the end of evaluation and written in a `rouge_scores.txt` file. The script takes 30 hours to compute with a single Tesla V100 GPU and a batch size of 10 (300,000 texts to summarize).
+The scripts executes on GPU if one is available and if `no_cuda` is not set to `true`. Inference on multiple GPUs is not suported yet. The ROUGE scores will be displayed in the console at the end of evaluation and written in a `rouge_scores.txt` file. The script takes 30 hours to compute with a single Tesla V100 GPU and a batch size of 10 (300,000 texts to summarize).
 
 ## Summarize any text
 

examples/seq2seq/distillation.py

@@ -17,7 +17,7 @@ from finetune import main as ft_main
 from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
 from transformers import AutoModelForSeq2SeqLM, MBartTokenizer, T5ForConditionalGeneration
 from transformers.modeling_bart import shift_tokens_right
-from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params
+from utils import calculate_bleu, freeze_params, label_smoothed_nll_loss, pickle_load, use_task_specific_params
 
 
 # need the parent dir module
@@ -28,7 +28,7 @@ from lightning_base import generic_train  # noqa
 class BartSummarizationDistiller(SummarizationModule):
     """Supports Bart, Pegasus and other models that inherit from Bart."""
 
-    loss_names = ["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]
+    loss_names = ["loss", "ce_loss", "mlm_loss", "enc_mse_loss", "hid_loss_enc", "hid_loss_dec"]
 
     def __init__(self, hparams):
         assert Path(hparams.data_dir).exists()
@@ -46,19 +46,9 @@ class BartSummarizationDistiller(SummarizationModule):
         if hparams.length_penalty != -1:
             student.config.length_penalty = hparams.length_penalty
         super().__init__(hparams, model=student, config=student.config)
-        model_type = student.config.model_type
         self.e_layer_ids, self.d_layer_ids = e_layer_ids, d_layer_ids  # type: List[int], List[int]
-
+        self.different_encoder = hparams.student_encoder_layers != teacher.config.encoder_layers
-        if model_type == "t5":
+        self.different_decoder = hparams.student_decoder_layers != teacher.config.decoder_layers
-            teacher_encoder_layers = len(teacher.get_encoder().block)
-            teacher_decoder_layers = len(teacher.get_decoder().block)
-        else:
-            teacher_encoder_layers = teacher.config.encoder_layers
-            teacher_decoder_layers = teacher.config.decoder_layers
-
-        self.different_encoder = hparams.student_encoder_layers != teacher_encoder_layers
-        self.different_decoder = hparams.student_decoder_layers != teacher_decoder_layers
-
         self.teacher = teacher
         freeze_params(self.teacher)
 
@@ -69,17 +59,17 @@ class BartSummarizationDistiller(SummarizationModule):
                 del self.teacher.encoder
         # Intermediate supervision: Decide which layers to supervise
         if hparams.supervise_forward:
-            self.e_matches = get_layers_to_supervise(n_student=len(self.e_layer_ids), n_teacher=teacher_encoder_layers)
+            self.d_matches = get_layers_to_supervise(
-            self.d_matches = get_layers_to_supervise(n_student=len(self.d_layer_ids), n_teacher=teacher_decoder_layers)
+                n_student=len(self.d_layer_ids), n_teacher=self.teacher.config.decoder_layers
-        else:  # student layer should emulate hidden states of the teacher layer it was copied from
+            )
-            self.e_matches = self.e_layer_ids
+        else:
             self.d_matches = self.d_layer_ids
-
         self.ce_loss_fct = nn.KLDivLoss(reduction="batchmean")
         self.temperature = 2.0
         self.alpha_mlm = hparams.alpha_mlm
         self.alpha_ce = hparams.alpha_ce
         self.alpha_hid = hparams.alpha_hid
+        self.alpha_encoder_loss = hparams.alpha_encoder_loss
         gc.collect()
         torch.cuda.empty_cache()
 
@@ -139,7 +129,7 @@ class BartSummarizationDistiller(SummarizationModule):
             output_hidden_states=True,
             output_attentions=False,
             use_cache=False,
-        )
+        )  # TODO(@sshleifer): return_dict=True cleanup
 
         # Same cross entropy vs. label smoothing logic as finetune.py
         assert lm_logits.shape[-1] == self.model.config.vocab_size
@@ -156,32 +146,30 @@ class BartSummarizationDistiller(SummarizationModule):
         def zero_tensor():
             return torch.tensor(0.0).type_as(student_lm_loss)
 
-        hid_loss_enc, hid_loss_dec = zero_tensor(), zero_tensor()
+        loss_encoder, hid_loss_enc, hid_loss_dec = zero_tensor(), zero_tensor(), zero_tensor()
-        if self.different_encoder:  # compute encoder hidden state loss
+        if self.different_encoder:
             with torch.no_grad():
-                teacher_enc_hid = self.teacher.get_encoder()(
+                teacher_enc_outputs, teacher_enc_hid, _ = self.teacher.get_encoder()(
-                    input_ids, attention_mask=src_mask, output_hidden_states=True, return_dict=True
+                    input_ids, attention_mask=src_mask, output_hidden_states=True
-                ).hidden_states
+                )
+            # DEPRECATE THIS
+            if self.hparams.alpha_encoder_loss > 0:
+                loss_encoder = self.calc_mse_loss(enc_outputs, teacher_enc_outputs, src_mask)
 
-            hid_loss_enc = self.calc_hidden_loss(
+            hid_loss_enc = self.calc_hidden_loss(src_mask, enc_hidden_state, teacher_enc_hid, self.e_layer_ids)
-                src_mask,
+
-                enc_hidden_state,
+        teacher_enc_outputs = (enc_outputs,)
-                teacher_enc_hid,
+        assert isinstance(teacher_enc_outputs, tuple), type(teacher_enc_outputs)
-                self.e_matches,
-                normalize_hidden=self.hparams.normalize_hidden,
-            )
 
         with torch.no_grad():
-            outputs = self.teacher(
+            tloss, tlogits, tdec_hidden, _ = self.teacher(
                 input_ids,
                 attention_mask=src_mask,
-                encoder_outputs=(enc_outputs,),
+                encoder_outputs=teacher_enc_outputs,
                 decoder_input_ids=decoder_input_ids,
                 lm_labels=labels,
                 output_hidden_states=True,
-                return_dict=True,
             )
-            tlogits, tdec_hidden = outputs.logits, outputs.decoder_hidden_states
         dec_mask = decoder_input_ids.ne(pad_token_id)
         loss_ce = self.calc_ce_loss(dec_mask, lm_logits, tlogits)
         if self.alpha_hid > 0:  # Intermediate supervision of decoder hidden states
@@ -192,9 +180,10 @@ class BartSummarizationDistiller(SummarizationModule):
         blended_loss = (
             self.alpha_ce * loss_ce
             + self.alpha_mlm * student_lm_loss
+            + self.hparams.alpha_encoder_loss * loss_encoder
             + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
         )
-        return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec
+        return blended_loss, loss_ce, student_lm_loss, loss_encoder, hid_loss_enc, hid_loss_dec
 
     @staticmethod
     def calc_hidden_loss(attention_mask, hidden_states, hidden_states_T, matches, normalize_hidden):
@@ -218,6 +207,7 @@ def add_distill_args(parser):
     parser.add_argument("--teacher", type=str)
     parser.add_argument("--alpha_ce", default=0.8, type=float)
     parser.add_argument("--alpha_mlm", default=0.2, type=float)
+    parser.add_argument("--alpha_encoder_loss", default=0.0, type=float)
     parser.add_argument("--alpha_hid", default=0.0, type=float, required=False)
     parser.add_argument("--student_decoder_layers", default=12, type=int, required=False)
     parser.add_argument("--student_encoder_layers", default=12, type=int, required=False)
@@ -264,9 +254,34 @@ def create_module(args):
     return model
 
 
+def evaluate_checkpoint(ckpt_path: Path, dest_dir=None):
+    # TODO(SS): DELETE? Better to convert_pl_ckpt_to_hf and run_eval.py
+    exp_dir = ckpt_path.parent
+    if dest_dir is None:
+        dest_dir = exp_dir
+    clash = list(dest_dir.glob("test_generations*"))
+    if clash:
+        print(f"SKIPPING to avoid overwriting {clash}")
+    ckpt = torch.load(ckpt_path, map_location="cpu")
+    if "hparams" in ckpt:
+        args = argparse.Namespace(**ckpt["hparams"])
+    else:
+        args = argparse.Namespace(**pickle_load(exp_dir / "hparams.pkl"))
+    args.resume_from_checkpoint = str(ckpt_path)
+    args.do_train = False
+    args.output_dir = str(dest_dir)
+    args.n_gpu = 1
+    args.eval_batch_size = 16
+    Path(args.output_dir).mkdir(exist_ok=True)
+    model = create_module(args)
+    trainer: pl.Trainer = generic_train(model, args, early_stopping_callback=False)
+    trainer.test(model)
+
+
 def distill_main(args):
     Path(args.output_dir).mkdir(exist_ok=True)
-    check_output_dir(args, expected_items=3)
+    if len(os.listdir(args.output_dir)) > 3 and args.do_train:
+        raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
 
     model = create_module(args)
     return ft_main(args, model=model)

examples/seq2seq/finetune.py

@@ -25,7 +25,6 @@ from utils import (
     assert_all_frozen,
     calculate_bleu,
     calculate_rouge,
-    check_output_dir,
     flatten_list,
     freeze_embeds,
     freeze_params,
@@ -34,7 +33,6 @@ from utils import (
     lmap,
     pickle_save,
     save_git_info,
-    save_json,
     use_task_specific_params,
 )
 
@@ -107,7 +105,6 @@ class SummarizationModule(BaseTransformer):
         self.dataset_class = (
             Seq2SeqDataset if hasattr(self.tokenizer, "prepare_seq2seq_batch") else LegacySeq2SeqDataset
         )
-        self.already_saved_batch = False
         self.eval_beams = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
         if self.hparams.eval_max_gen_length is not None:
             self.eval_max_length = self.hparams.eval_max_gen_length
@@ -115,17 +112,6 @@ class SummarizationModule(BaseTransformer):
             self.eval_max_length = self.model.config.max_length
         self.val_metric = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
 
-    def save_readable_batch(self, batch: Dict[str, torch.Tensor]) -> Dict[str, List[str]]:
-        """A debugging utility"""
-        readable_batch = {
-            k: self.tokenizer.batch_decode(v.tolist()) if "mask" not in k else v.shape for k, v in batch.items()
-        }
-        save_json(readable_batch, Path(self.output_dir) / "text_batch.json")
-        save_json({k: v.tolist() for k, v in batch.items()}, Path(self.output_dir) / "tok_batch.json")
-
-        self.already_saved_batch = True
-        return readable_batch
-
     def forward(self, input_ids, **kwargs):
         return self.model(input_ids, **kwargs)
 
@@ -143,9 +129,6 @@ class SummarizationModule(BaseTransformer):
             decoder_input_ids = self.model._shift_right(tgt_ids)
         else:
             decoder_input_ids = shift_tokens_right(tgt_ids, pad_token_id)
-        if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
-            batch["decoder_input_ids"] = decoder_input_ids
-            self.save_readable_batch(batch)
 
         outputs = self(src_ids, attention_mask=src_mask, decoder_input_ids=decoder_input_ids, use_cache=False)
         lm_logits = outputs[0]
@@ -182,7 +165,6 @@ class SummarizationModule(BaseTransformer):
         return self._generative_step(batch)
 
     def validation_epoch_end(self, outputs, prefix="val") -> Dict:
-
         self.step_count += 1
         losses = {k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
         loss = losses["loss"]
@@ -330,7 +312,6 @@ class SummarizationModule(BaseTransformer):
         parser.add_argument("--freeze_encoder", action="store_true")
         parser.add_argument("--freeze_embeds", action="store_true")
         parser.add_argument("--sortish_sampler", action="store_true", default=False)
-        parser.add_argument("--overwrite_output_dir", action="store_true", default=False)
         parser.add_argument("--max_tokens_per_batch", type=int, default=None)
         parser.add_argument("--logger_name", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
         parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.")
@@ -375,8 +356,8 @@ class TranslationModule(SummarizationModule):
 
 def main(args, model=None) -> SummarizationModule:
     Path(args.output_dir).mkdir(exist_ok=True)
-    check_output_dir(args, expected_items=3)
+    if len(os.listdir(args.output_dir)) > 3 and args.do_train:
-
+        raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
     if model is None:
         if "summarization" in args.task:
             model: SummarizationModule = SummarizationModule(args)

examples/seq2seq/finetune_bart_tiny.sh

@@ -1,7 +1,7 @@
 # Script for verifying that run_bart_sum can be invoked from its directory
 
 # Get tiny dataset with cnn_dm format (4 examples for train, val, test)
-wget https://cdn-datasets.huggingface.co/summarization/cnn_tiny.tgz
+wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/cnn_tiny.tgz
 tar -xzvf cnn_tiny.tgz
 rm cnn_tiny.tgz
 

examples/seq2seq/finetune_trainer.py

@@ -4,7 +4,7 @@ import sys
 from dataclasses import dataclass, field
 from typing import Optional
 
-from seq2seq_trainer import Seq2SeqTrainer, arg_to_scheduler_choices
+from seq2seq_trainer import Seq2SeqTrainer
 from transformers import (
     AutoConfig,
     AutoModelForSeq2SeqLM,
@@ -16,11 +16,11 @@ from transformers import (
 )
 from transformers.trainer_utils import EvaluationStrategy
 from utils import (
+    LegacySeq2SeqDataset,
     Seq2SeqDataCollator,
     Seq2SeqDataset,
     assert_all_frozen,
     build_compute_metrics_fn,
-    check_output_dir,
     freeze_embeds,
     freeze_params,
     lmap,
@@ -63,9 +63,6 @@ class Seq2SeqTrainingArguments(TrainingArguments):
     attention_dropout: Optional[float] = field(
         default=None, metadata={"help": "Attention dropout probability. Goes into model.config."}
     )
-    lr_scheduler: Optional[str] = field(
-        default="linear", metadata={"help": f"Which lr scheduler to use. Selected in {arg_to_scheduler_choices}"}
-    )
 
 
 @dataclass
@@ -137,10 +134,6 @@ class DataTrainingArguments:
     src_lang: Optional[str] = field(default=None, metadata={"help": "Source language id for translation."})
     tgt_lang: Optional[str] = field(default=None, metadata={"help": "Target language id for translation."})
     eval_beams: Optional[int] = field(default=None, metadata={"help": "# num_beams to use for evaluation."})
-    ignore_pad_token_for_loss: bool = field(
-        default=True,
-        metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."},
-    )
 
 
 def main():
@@ -157,7 +150,15 @@ def main():
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
-    check_output_dir(training_args)
+    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(
@@ -226,7 +227,7 @@ def main():
         freeze_params(model.get_encoder())
         assert_all_frozen(model.get_encoder())
 
-    dataset_class = Seq2SeqDataset
+    dataset_class = Seq2SeqDataset if hasattr(tokenizer, "prepare_seq2seq_batch") else LegacySeq2SeqDataset
 
     # Get datasets
     train_dataset = (

examples/seq2seq/make_student.py

@@ -13,7 +13,7 @@ logger = logging.get_logger(__name__)
 
 
 def copy_layers(src_layers: nn.ModuleList, dest_layers: nn.ModuleList, layers_to_copy: List[int]) -> None:
-    layers_to_copy = nn.ModuleList([src_layers[i] for i in layers_to_copy])
+    layers_to_copy = nn.ModuleList([l for i, l in enumerate(src_layers) if i in layers_to_copy])
     assert len(dest_layers) == len(layers_to_copy), f"{len(dest_layers)} != {len(layers_to_copy)}"
     dest_layers.load_state_dict(layers_to_copy.state_dict())
 
@@ -81,8 +81,6 @@ def create_student_by_copying_alternating_layers(
     e: Union[int, None] = None,
     d: Union[int, None] = None,
     copy_first_teacher_layers=False,
-    e_layers_to_copy=None,
-    d_layers_to_copy=None,
     **extra_config_kwargs
 ) -> Tuple[PreTrainedModel, List[int], List[int]]:
     """Make a student by copying alternating layers from a teacher, save it to save_path.
@@ -144,10 +142,8 @@ def create_student_by_copying_alternating_layers(
         return student, e_layers_to_copy, d_layers_to_copy
 
     # Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
-    if e_layers_to_copy is None:
+    e_layers_to_copy: List[int] = pick_layers_to_copy(e, teacher_e)
-        e_layers_to_copy: List[int] = pick_layers_to_copy(e, teacher_e)
+    d_layers_to_copy: List[int] = pick_layers_to_copy(d, teacher_d)
-    if d_layers_to_copy is None:
-        d_layers_to_copy: List[int] = pick_layers_to_copy(d, teacher_d)
 
     try:
         copy_layers(teacher.model.encoder.layers, student.model.encoder.layers, e_layers_to_copy)

examples/seq2seq/precomputed_pseudo_labels.md

@@ -1,43 +0,0 @@
-### Saved Pseudo-Labels
-These are the generations of various large models on various large **training** sets. All in all they took about 200 GPU hours to produce.
-
-### Available Pseudo-labels
-| Dataset | Model                       | Link                                                                                   | Rouge Scores       | Notes                                                                                                       
-|---------|-----------------------------|----------------------------------------------------------------------------------------|--------------------|-------------------------------------------------------------------------------------------------------------
-| XSUM    | `facebook/bart-large-xsum`    | [download](https://cdn-datasets.huggingface.co/pseudo/xsum/bart_xsum_pl.tgz)          | 49.8/28.0/42.5     |                                                                                                             
-| XSUM    | `google/pegasus-xsum`         | [download](https://cdn-datasets.huggingface.co/pseudo/xsum/pegasus_xsum.tgz)          | 53.3/32.7/46.5     |                                                                                                             
-| XSUM    | `facebook/bart-large-xsum`    | [download](https://cdn-datasets.huggingface.co/pseudo/xsum/xsum_pl2_bart.tgz)         |                   | Bart pseudolabels filtered to those with Rouge2 > 10.0 w GT.                                                 
-| CNN/DM  | `sshleifer/pegasus-cnn-ft-v2` | [download](https://cdn-datasets.huggingface.co/pseudo/cnn_dm/pegasus_cnn_cnn_pls.tgz) | 47.316/26.65/44.56 | do not worry about the fact that train.source is one line shorter.                                          
-| CNN/DM  | `facebook/bart-large-cnn`     | [download](https://cdn-datasets.huggingface.co/pseudo/cnn_dm/cnn_bart_pl.tgz)         |                    | 5K (2%) are missing, there should be 282173                                                                 
-| CNN/DM  | `google/pegasus-xsum`         | [download](https://cdn-datasets.huggingface.co/pseudo/cnn_dm/pegasus_xsum_on_cnn.tgz) | 21.5/6.76/25       | extra labels for xsum distillation  Used max_source_length=512, (and all other pegasus-xsum configuration). 
-| EN-RO   | `Helsinki-NLP/opus-mt-en-ro`  | [download](https://cdn-datasets.huggingface.co/pseudo/wmt_en_ro/opus_mt_en_ro.tgz) |       |  
-| EN-RO   | `facebook/mbart-large-en-ro`  | [download](https://cdn-datasets.huggingface.co/pseudo/wmt_en_ro/mbart_large_en_ro.tgz) |       |  
-
-
-(EN_RO = WMT 2016 English-Romanian).
-
-Example Download Command:
-```bash
-curl -S https://cdn-datasets.huggingface.co/pseudo/xsum/bart_xsum_pl.tgz | tar -xvz -C .
-```
-### Generating New Pseudolabels
-Here is the command I used to generate the pseudolabels in the second row of the table, after downloading XSUM from [here](https://cdn-datasets.huggingface.co/summarization/xsum.tar.gz). 
-
-```bash                                                                         
-python -m torch.distributed.launch --nproc_per_node=8 run_distributed_eval.py \
-    --model_name google/pegasus-xsum \ 
-    --save_dir pegasus_xsum \ 
-    --data_dir xsum \
-    --bs 8 --sync_timeout 60000 \
-    --max_source_length 512 \
-    --type_path train
-```
-
-+ These command takes a while to run. For example,  pegasus_cnn_cnn_pls.tgz took 8 hours on 8 GPUs.
-+ Pegasus does not work in fp16 :(, Bart, mBART and Marian do.
-+ Even if you have 1 GPU, `run_distributed_eval.py` is 10-20% faster than `run_eval.py` because it uses `SortishSampler` to minimize padding computation.
-
-### Contributions
-Feel free to contribute your own pseudolabels via PR. Add a row to this table with a new google drive link (or other command line downloadable link).
-
-

examples/seq2seq/seq2seq_trainer.py

@@ -1,24 +1,15 @@
-import copy
+import logging
 from typing import Any, Dict, Optional, Tuple, Union
 
 import torch
 from torch import nn
 from torch.utils.data import DistributedSampler, RandomSampler
 
-from transformers import PreTrainedModel, Trainer, logging
+from transformers import Trainer
 from transformers.configuration_fsmt import FSMTConfig
 from transformers.file_utils import is_torch_tpu_available
-from transformers.optimization import (
+from transformers.optimization import Adafactor, AdamW, get_linear_schedule_with_warmup
-    Adafactor,
+from transformers.trainer import get_tpu_sampler
-    AdamW,
-    get_constant_schedule,
-    get_constant_schedule_with_warmup,
-    get_cosine_schedule_with_warmup,
-    get_cosine_with_hard_restarts_schedule_with_warmup,
-    get_linear_schedule_with_warmup,
-    get_polynomial_decay_schedule_with_warmup,
-)
-from transformers.trainer_pt_utils import get_tpu_sampler
 
 
 try:
@@ -27,39 +18,17 @@ except ImportError:
     from utils import label_smoothed_nll_loss
 
 
-logger = logging.get_logger(__name__)
+logger = logging.getLogger(__name__)
-
-arg_to_scheduler = {
-    "linear": get_linear_schedule_with_warmup,
-    "cosine": get_cosine_schedule_with_warmup,
-    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
-    "polynomial": get_polynomial_decay_schedule_with_warmup,
-    "constant": get_constant_schedule,
-    "constant_w_warmup": get_constant_schedule_with_warmup,
-}
-arg_to_scheduler_choices = sorted(arg_to_scheduler.keys())
 
 
 class Seq2SeqTrainer(Trainer):
-    def __init__(self, config=None, data_args=None, *args, **kwargs):
+    def __init__(self, config, data_args, *args, **kwargs):
         super().__init__(*args, **kwargs)
-
+        self.config = config
-        if config is None:
-            assert isinstance(
-                self.model, PreTrainedModel
-            ), f"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is {self.model.__class__}"
-            self.config = self._actual_model(self.model).config
-        else:
-            self.config = config
-
         self.data_args = data_args
+        self.max_gen_length = data_args.val_max_target_length
         self.vocab_size = self.config.tgt_vocab_size if isinstance(self.config, FSMTConfig) else self.config.vocab_size
 
-        if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
-            assert (
-                self.config.pad_token_id is not None
-            ), "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss calculation or doing label smoothing."
-
     def create_optimizer_and_scheduler(self, num_training_steps: int):
         """
         Setup the optimizer and the learning rate scheduler.
@@ -93,21 +62,9 @@ class Seq2SeqTrainer(Trainer):
                 )
 
         if self.lr_scheduler is None:
-            self.lr_scheduler = self._get_lr_scheduler(num_training_steps)
+            self.lr_scheduler = get_linear_schedule_with_warmup(
-        else:  # ignoring --lr_scheduler
-            logger.warn("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.")
-
-    def _get_lr_scheduler(self, num_training_steps):
-        schedule_func = arg_to_scheduler[self.args.lr_scheduler]
-        if self.args.lr_scheduler == "constant":
-            scheduler = schedule_func(self.optimizer)
-        elif self.args.lr_scheduler == "constant_w_warmup":
-            scheduler = schedule_func(self.optimizer, num_warmup_steps=self.args.warmup_steps)
-        else:
-            scheduler = schedule_func(
                 self.optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=num_training_steps
             )
-        return scheduler
 
     def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]:
         if isinstance(self.train_dataset, torch.utils.data.IterableDataset):
@@ -126,31 +83,23 @@ class Seq2SeqTrainer(Trainer):
                 else DistributedSampler(self.train_dataset)
             )
 
-    def _compute_loss(self, model, inputs):
+    def compute_loss(self, model, inputs):
-        inputs = copy.deepcopy(inputs)
+        labels = inputs.pop("labels")
-        if self.args.label_smoothing == 0:
+        outputs = model(**inputs, use_cache=False)
-            if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
+        logits = outputs[0]
-                # force training to ignore pad token
+        return self._compute_loss(logits, labels)
-                labels = inputs.pop("labels")
-                logits = model(**inputs, use_cache=False)[0]
 
-                loss_fct = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
+    def _compute_loss(self, logits, labels):
-                loss = loss_fct(logits.view(-1, logits.shape[-1]), labels.view(-1))
+        if self.args.label_smoothing == 0:
-            else:
+            # Same behavior as modeling_bart.py
-                # compute usual loss via models
+            loss_fct = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
-                loss, logits = model(**inputs, use_cache=False)[:2]
+            assert logits.shape[-1] == self.vocab_size
+            loss = loss_fct(logits.view(-1, logits.shape[-1]), labels.view(-1))
         else:
-            # compute label smoothed loss
-            labels = inputs.pop("labels")
-            logits = model(**inputs, use_cache=False)[0]
             lprobs = torch.nn.functional.log_softmax(logits, dim=-1)
-            loss, _ = label_smoothed_nll_loss(
+            loss, nll_loss = label_smoothed_nll_loss(
                 lprobs, labels, self.args.label_smoothing, ignore_index=self.config.pad_token_id
             )
-        return loss, logits
-
-    def compute_loss(self, model, inputs):
-        loss, _ = self._compute_loss(model, inputs)
         return loss
 
     def prediction_step(
@@ -178,37 +127,31 @@ class Seq2SeqTrainer(Trainer):
         """
         inputs = self._prepare_inputs(inputs)
 
-        if self.args.predict_with_generate and not self.args.prediction_loss_only:
-            gen_kwargs = {
-                "max_length": self.data_args.val_max_target_length
-                if self.data_args is not None
-                else self.config.max_length,
-                "num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
-            }
-            generated_tokens = model.generate(
-                inputs["input_ids"],
-                attention_mask=inputs["attention_mask"],
-                **gen_kwargs,
-            )
-            # in case the batch is shorter than max length, the output should be padded
-            if self.config.pad_token_id is not None:
-                generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_length"])
-
-            # compute loss on predict data
         with torch.no_grad():
-            loss, logits = self._compute_loss(model, inputs)
+            if self.args.predict_with_generate and not self.args.prediction_loss_only:
+                generated_tokens = model.generate(
+                    inputs["input_ids"],
+                    attention_mask=inputs["attention_mask"],
+                    use_cache=True,
+                    num_beams=self.data_args.eval_beams,
+                    max_length=self.max_gen_length,
+                )
+                # in case the batch is shorter than max length, the output should be padded
+                generated_tokens = self._pad_tensors_to_max_len(generated_tokens, self.max_gen_length)
 
-        loss = loss.mean().detach()
+            labels_out = inputs.get("labels")
-        if self.args.prediction_loss_only:
+            # Call forward again to get loss # TODO: avoidable?
-            return (loss, None, None)
+            outputs = model(**inputs, use_cache=False)
+            loss = self._compute_loss(outputs[1], labels_out)
+            loss = loss.mean().item()
+            if self.args.prediction_loss_only:
+                return (loss, None, None)
 
-        logits = generated_tokens if self.args.predict_with_generate else logits
+            logits = generated_tokens if self.args.predict_with_generate else outputs[1]
 
-        labels = inputs["labels"]
+        labels_out = labels_out.detach()
-        if self.config.pad_token_id is not None:
+        labels = self._pad_tensors_to_max_len(labels_out, self.max_gen_length)
-            labels = self._pad_tensors_to_max_len(labels, self.config.max_length)
+        return (loss, logits.detach(), labels)
-
-        return (loss, logits, labels)
 
     def _pad_tensors_to_max_len(self, tensor, max_length):
         padded_tensor = self.config.pad_token_id * torch.ones(

examples/seq2seq/test_bash_script.py

@@ -3,6 +3,7 @@
 import argparse
 import os
 import sys
+import tempfile
 from pathlib import Path
 from unittest.mock import patch
 
@@ -15,172 +16,172 @@ from distillation import BartSummarizationDistiller, distill_main
 from finetune import SummarizationModule, main
 from test_seq2seq_examples import CUDA_AVAILABLE, MBART_TINY
 from transformers import BartForConditionalGeneration, MarianMTModel
-from transformers.testing_utils import TestCasePlus, slow
+from transformers.testing_utils import slow
 from utils import load_json
 
 
 MODEL_NAME = MBART_TINY
+# TODO(SS): MODEL_NAME = "sshleifer/student_mbart_en_ro_1_1"
 MARIAN_MODEL = "sshleifer/student_marian_en_ro_6_1"
 
 
-class TestAll(TestCasePlus):
+@slow
-    @slow
+@pytest.mark.skipif(not CUDA_AVAILABLE, reason="too slow to run on CPU")
-    @pytest.mark.skipif(not CUDA_AVAILABLE, reason="too slow to run on CPU")
+def test_model_download():
-    def test_model_download(self):
+    """This warms up the cache so that we can time the next test without including download time, which varies between machines."""
-        """This warms up the cache so that we can time the next test without including download time, which varies between machines."""
+    BartForConditionalGeneration.from_pretrained(MODEL_NAME)
-        BartForConditionalGeneration.from_pretrained(MODEL_NAME)
+    MarianMTModel.from_pretrained(MARIAN_MODEL)
-        MarianMTModel.from_pretrained(MARIAN_MODEL)
 
-    @timeout_decorator.timeout(120)
-    @slow
-    @pytest.mark.skipif(not CUDA_AVAILABLE, reason="too slow to run on CPU")
-    def test_train_mbart_cc25_enro_script(self):
-        data_dir = "examples/seq2seq/test_data/wmt_en_ro"
-        env_vars_to_replace = {
-            "--fp16_opt_level=O1": "",
-            "$MAX_LEN": 128,
-            "$BS": 4,
-            "$GAS": 1,
-            "$ENRO_DIR": data_dir,
-            "facebook/mbart-large-cc25": MODEL_NAME,
-            # Download is 120MB in previous test.
-            "val_check_interval=0.25": "val_check_interval=1.0",
-        }
 
-        # Clean up bash script
+@timeout_decorator.timeout(120)
-        bash_script = Path("examples/seq2seq/train_mbart_cc25_enro.sh").open().read().split("finetune.py")[1].strip()
+@slow
-        bash_script = bash_script.replace("\\\n", "").strip().replace('"$@"', "")
+@pytest.mark.skipif(not CUDA_AVAILABLE, reason="too slow to run on CPU")
-        for k, v in env_vars_to_replace.items():
+def test_train_mbart_cc25_enro_script():
-            bash_script = bash_script.replace(k, str(v))
+    data_dir = "examples/seq2seq/test_data/wmt_en_ro"
-        output_dir = self.get_auto_remove_tmp_dir()
+    env_vars_to_replace = {
+        "--fp16_opt_level=O1": "",
+        "$MAX_LEN": 128,
+        "$BS": 4,
+        "$GAS": 1,
+        "$ENRO_DIR": data_dir,
+        "facebook/mbart-large-cc25": MODEL_NAME,
+        # Download is 120MB in previous test.
+        "val_check_interval=0.25": "val_check_interval=1.0",
+    }
 
-        bash_script = bash_script.replace("--fp16 ", "")
+    # Clean up bash script
-        testargs = (
+    bash_script = Path("examples/seq2seq/train_mbart_cc25_enro.sh").open().read().split("finetune.py")[1].strip()
-            ["finetune.py"]
+    bash_script = bash_script.replace("\\\n", "").strip().replace('"$@"', "")
-            + bash_script.split()
+    for k, v in env_vars_to_replace.items():
-            + [
+        bash_script = bash_script.replace(k, str(v))
-                f"--output_dir={output_dir}",
+    output_dir = tempfile.mkdtemp(prefix="output_mbart")
-                "--gpus=1",
-                "--learning_rate=3e-1",
-                "--warmup_steps=0",
-                "--val_check_interval=1.0",
-                "--tokenizer_name=facebook/mbart-large-en-ro",
-            ]
-        )
-        with patch.object(sys, "argv", testargs):
-            parser = argparse.ArgumentParser()
-            parser = pl.Trainer.add_argparse_args(parser)
-            parser = SummarizationModule.add_model_specific_args(parser, os.getcwd())
-            args = parser.parse_args()
-            args.do_predict = False
-            # assert args.gpus == gpus THIS BREAKS for multigpu
-            model = main(args)
 
-        # Check metrics
+    bash_script = bash_script.replace("--fp16 ", "")
-        metrics = load_json(model.metrics_save_path)
+    testargs = (
-        first_step_stats = metrics["val"][0]
+        ["finetune.py"]
-        last_step_stats = metrics["val"][-1]
+        + bash_script.split()
-        assert (
+        + [
-            len(metrics["val"]) == (args.max_epochs / args.val_check_interval) + 1
+            f"--output_dir={output_dir}",
-        )  # +1 accounts for val_sanity_check
+            "--gpus=1",
+            "--learning_rate=3e-1",
+            "--warmup_steps=0",
+            "--val_check_interval=1.0",
+            "--tokenizer_name=facebook/mbart-large-en-ro",
+        ]
+    )
+    with patch.object(sys, "argv", testargs):
+        parser = argparse.ArgumentParser()
+        parser = pl.Trainer.add_argparse_args(parser)
+        parser = SummarizationModule.add_model_specific_args(parser, os.getcwd())
+        args = parser.parse_args()
+        args.do_predict = False
+        # assert args.gpus == gpus THIS BREAKS for multigpu
+        model = main(args)
 
-        assert last_step_stats["val_avg_gen_time"] >= 0.01
+    # Check metrics
+    metrics = load_json(model.metrics_save_path)
+    first_step_stats = metrics["val"][0]
+    last_step_stats = metrics["val"][-1]
+    assert len(metrics["val"]) == (args.max_epochs / args.val_check_interval) + 1  # +1 accounts for val_sanity_check
 
-        assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
+    assert last_step_stats["val_avg_gen_time"] >= 0.01
-        assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
-        assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"], float)
 
-        # check lightning ckpt can be loaded and has a reasonable statedict
+    assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
-        contents = os.listdir(output_dir)
+    assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
-        ckpt_path = [x for x in contents if x.endswith(".ckpt")][0]
+    assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"], float)
-        full_path = os.path.join(args.output_dir, ckpt_path)
-        ckpt = torch.load(full_path, map_location="cpu")
-        expected_key = "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
-        assert expected_key in ckpt["state_dict"]
-        assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.float32
 
-        # TODO: turn on args.do_predict when PL bug fixed.
+    # check lightning ckpt can be loaded and has a reasonable statedict
-        if args.do_predict:
+    contents = os.listdir(output_dir)
-            contents = {os.path.basename(p) for p in contents}
+    ckpt_path = [x for x in contents if x.endswith(".ckpt")][0]
-            assert "test_generations.txt" in contents
+    full_path = os.path.join(args.output_dir, ckpt_path)
-            assert "test_results.txt" in contents
+    ckpt = torch.load(full_path, map_location="cpu")
-            # assert len(metrics["val"]) ==  desired_n_evals
+    expected_key = "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
-            assert len(metrics["test"]) == 1
+    assert expected_key in ckpt["state_dict"]
+    assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.float32
 
-    @timeout_decorator.timeout(600)
+    # TODO(SS): turn on args.do_predict when PL bug fixed.
-    @slow
+    if args.do_predict:
-    @pytest.mark.skipif(not CUDA_AVAILABLE, reason="too slow to run on CPU")
+        contents = {os.path.basename(p) for p in contents}
-    def test_opus_mt_distill_script(self):
+        assert "test_generations.txt" in contents
-        data_dir = "examples/seq2seq/test_data/wmt_en_ro"
+        assert "test_results.txt" in contents
-        env_vars_to_replace = {
+        # assert len(metrics["val"]) ==  desired_n_evals
-            "--fp16_opt_level=O1": "",
+        assert len(metrics["test"]) == 1
-            "$MAX_LEN": 128,
-            "$BS": 16,
-            "$GAS": 1,
-            "$ENRO_DIR": data_dir,
-            "$m": "sshleifer/student_marian_en_ro_6_1",
-            "val_check_interval=0.25": "val_check_interval=1.0",
-        }
 
-        # Clean up bash script
-        bash_script = (
-            Path("examples/seq2seq/distil_marian_no_teacher.sh").open().read().split("distillation.py")[1].strip()
-        )
-        bash_script = bash_script.replace("\\\n", "").strip().replace('"$@"', "")
-        bash_script = bash_script.replace("--fp16 ", " ")
 
-        for k, v in env_vars_to_replace.items():
+@timeout_decorator.timeout(600)
-            bash_script = bash_script.replace(k, str(v))
+@slow
-        output_dir = self.get_auto_remove_tmp_dir()
+@pytest.mark.skipif(not CUDA_AVAILABLE, reason="too slow to run on CPU")
-        bash_script = bash_script.replace("--fp16", "")
+def test_opus_mt_distill_script():
-        epochs = 6
+    data_dir = "examples/seq2seq/test_data/wmt_en_ro"
-        testargs = (
+    env_vars_to_replace = {
-            ["distillation.py"]
+        "--fp16_opt_level=O1": "",
-            + bash_script.split()
+        "$MAX_LEN": 128,
-            + [
+        "$BS": 16,
-                f"--output_dir={output_dir}",
+        "$GAS": 1,
-                "--gpus=1",
+        "$ENRO_DIR": data_dir,
-                "--learning_rate=1e-3",
+        "$m": "sshleifer/student_marian_en_ro_6_1",
-                f"--num_train_epochs={epochs}",
+        "val_check_interval=0.25": "val_check_interval=1.0",
-                "--warmup_steps=10",
+    }
-                "--val_check_interval=1.0",
-            ]
-        )
-        with patch.object(sys, "argv", testargs):
-            parser = argparse.ArgumentParser()
-            parser = pl.Trainer.add_argparse_args(parser)
-            parser = BartSummarizationDistiller.add_model_specific_args(parser, os.getcwd())
-            args = parser.parse_args()
-            args.do_predict = False
-            # assert args.gpus == gpus THIS BREAKS for multigpu
 
-            model = distill_main(args)
+    # Clean up bash script
+    bash_script = (
+        Path("examples/seq2seq/distil_marian_no_teacher.sh").open().read().split("distillation.py")[1].strip()
+    )
+    bash_script = bash_script.replace("\\\n", "").strip().replace('"$@"', "")
+    bash_script = bash_script.replace("--fp16 ", " ")
 
-        # Check metrics
+    for k, v in env_vars_to_replace.items():
-        metrics = load_json(model.metrics_save_path)
+        bash_script = bash_script.replace(k, str(v))
-        first_step_stats = metrics["val"][0]
+    output_dir = tempfile.mkdtemp(prefix="marian_output")
-        last_step_stats = metrics["val"][-1]
+    bash_script = bash_script.replace("--fp16", "")
-        assert len(metrics["val"]) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check
+    epochs = 6
+    testargs = (
+        ["distillation.py"]
+        + bash_script.split()
+        + [
+            f"--output_dir={output_dir}",
+            "--gpus=1",
+            "--learning_rate=1e-3",
+            f"--num_train_epochs={epochs}",
+            "--warmup_steps=10",
+            "--val_check_interval=1.0",
+        ]
+    )
+    with patch.object(sys, "argv", testargs):
+        parser = argparse.ArgumentParser()
+        parser = pl.Trainer.add_argparse_args(parser)
+        parser = BartSummarizationDistiller.add_model_specific_args(parser, os.getcwd())
+        args = parser.parse_args()
+        args.do_predict = False
+        # assert args.gpus == gpus THIS BREAKS for multigpu
 
-        assert last_step_stats["val_avg_gen_time"] >= 0.01
+        model = distill_main(args)
 
-        assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
+    # Check metrics
-        assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
+    metrics = load_json(model.metrics_save_path)
-        assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"], float)
+    first_step_stats = metrics["val"][0]
+    last_step_stats = metrics["val"][-1]
+    assert len(metrics["val"]) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check
 
-        # check lightning ckpt can be loaded and has a reasonable statedict
+    assert last_step_stats["val_avg_gen_time"] >= 0.01
-        contents = os.listdir(output_dir)
-        ckpt_path = [x for x in contents if x.endswith(".ckpt")][0]
-        full_path = os.path.join(args.output_dir, ckpt_path)
-        ckpt = torch.load(full_path, map_location="cpu")
-        expected_key = "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
-        assert expected_key in ckpt["state_dict"]
-        assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.float32
 
-        # TODO: turn on args.do_predict when PL bug fixed.
+    assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
-        if args.do_predict:
+    assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
-            contents = {os.path.basename(p) for p in contents}
+    assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"], float)
-            assert "test_generations.txt" in contents
+
-            assert "test_results.txt" in contents
+    # check lightning ckpt can be loaded and has a reasonable statedict
-            # assert len(metrics["val"]) ==  desired_n_evals
+    contents = os.listdir(output_dir)
-            assert len(metrics["test"]) == 1
+    ckpt_path = [x for x in contents if x.endswith(".ckpt")][0]
+    full_path = os.path.join(args.output_dir, ckpt_path)
+    ckpt = torch.load(full_path, map_location="cpu")
+    expected_key = "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
+    assert expected_key in ckpt["state_dict"]
+    assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.float32
+
+    # TODO(SS): turn on args.do_predict when PL bug fixed.
+    if args.do_predict:
+        contents = {os.path.basename(p) for p in contents}
+        assert "test_generations.txt" in contents
+        assert "test_results.txt" in contents
+        # assert len(metrics["val"]) ==  desired_n_evals
+        assert len(metrics["test"]) == 1

examples/seq2seq/test_datasets.py

@@ -1,4 +1,5 @@
 import os
+import tempfile
 from pathlib import Path
 
 import numpy as np
@@ -6,12 +7,11 @@ import pytest
 from torch.utils.data import DataLoader
 
 from pack_dataset import pack_data_dir
-from parameterized import parameterized
 from save_len_file import save_len_file
 from test_seq2seq_examples import ARTICLES, BART_TINY, MARIAN_TINY, MBART_TINY, SUMMARIES, T5_TINY, make_test_data_dir
 from transformers import AutoTokenizer
 from transformers.modeling_bart import shift_tokens_right
-from transformers.testing_utils import TestCasePlus, slow
+from transformers.testing_utils import slow
 from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeq2SeqDataset, Seq2SeqDataset
 
 
@@ -19,198 +19,202 @@ BERT_BASE_CASED = "bert-base-cased"
 PEGASUS_XSUM = "google/pegasus-xsum"
 
 
-class TestAll(TestCasePlus):
+@slow
-    @parameterized.expand(
+@pytest.mark.parametrize(
-        [
+    "tok_name",
-            MBART_TINY,
+    [
-            MARIAN_TINY,
+        MBART_TINY,
-            T5_TINY,
+        MARIAN_TINY,
-            BART_TINY,
+        T5_TINY,
-            PEGASUS_XSUM,
+        BART_TINY,
-        ],
+        PEGASUS_XSUM,
+    ],
+)
+def test_seq2seq_dataset_truncation(tok_name):
+    tokenizer = AutoTokenizer.from_pretrained(tok_name)
+    tmp_dir = make_test_data_dir()
+    max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
+    max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
+    max_src_len = 4
+    max_tgt_len = 8
+    assert max_len_target > max_src_len  # Will be truncated
+    assert max_len_source > max_src_len  # Will be truncated
+    src_lang, tgt_lang = "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
+    train_dataset = Seq2SeqDataset(
+        tokenizer,
+        data_dir=tmp_dir,
+        type_path="train",
+        max_source_length=max_src_len,
+        max_target_length=max_tgt_len,  # ignored
+        src_lang=src_lang,
+        tgt_lang=tgt_lang,
     )
-    @slow
+    dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
-    def test_seq2seq_dataset_truncation(self, tok_name):
+    for batch in dataloader:
-        tokenizer = AutoTokenizer.from_pretrained(tok_name)
+        assert isinstance(batch, dict)
-        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
+        assert batch["attention_mask"].shape == batch["input_ids"].shape
-        max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
+        # show that articles were trimmed.
-        max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
+        assert batch["input_ids"].shape[1] == max_src_len
-        max_src_len = 4
+        # show that targets are the same len
-        max_tgt_len = 8
+        assert batch["labels"].shape[1] == max_tgt_len
-        assert max_len_target > max_src_len  # Will be truncated
+        if tok_name != MBART_TINY:
-        assert max_len_source > max_src_len  # Will be truncated
+            continue
-        src_lang, tgt_lang = "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
+        # check language codes in correct place
+        batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], tokenizer.pad_token_id)
+        assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
+        assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
+        assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
+        assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
+
+        break  # No need to test every batch
+
+
+@pytest.mark.parametrize("tok", [BART_TINY, BERT_BASE_CASED])
+def test_legacy_dataset_truncation(tok):
+    tokenizer = AutoTokenizer.from_pretrained(tok)
+    tmp_dir = make_test_data_dir()
+    max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
+    max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
+    trunc_target = 4
+    train_dataset = LegacySeq2SeqDataset(
+        tokenizer,
+        data_dir=tmp_dir,
+        type_path="train",
+        max_source_length=20,
+        max_target_length=trunc_target,
+    )
+    dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
+    for batch in dataloader:
+        assert batch["attention_mask"].shape == batch["input_ids"].shape
+        # show that articles were trimmed.
+        assert batch["input_ids"].shape[1] == max_len_source
+        assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
+        # show that targets were truncated
+        assert batch["labels"].shape[1] == trunc_target  # Truncated
+        assert max_len_target > trunc_target  # Truncated
+        break  # No need to test every batch
+
+
+def test_pack_dataset():
+    tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
+
+    tmp_dir = Path(make_test_data_dir())
+    orig_examples = tmp_dir.joinpath("train.source").open().readlines()
+    save_dir = Path(tempfile.mkdtemp(prefix="packed_"))
+    pack_data_dir(tokenizer, tmp_dir, 128, save_dir)
+    orig_paths = {x.name for x in tmp_dir.iterdir()}
+    new_paths = {x.name for x in save_dir.iterdir()}
+    packed_examples = save_dir.joinpath("train.source").open().readlines()
+    # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
+    # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
+    assert len(packed_examples) < len(orig_examples)
+    assert len(packed_examples) == 1
+    assert len(packed_examples[0]) == sum(len(x) for x in orig_examples)
+    assert orig_paths == new_paths
+
+
+@pytest.mark.skipif(not FAIRSEQ_AVAILABLE, reason="This test requires fairseq")
+def test_dynamic_batch_size():
+    if not FAIRSEQ_AVAILABLE:
+        return
+    ds, max_tokens, tokenizer = _get_dataset(max_len=64)
+    required_batch_size_multiple = 64
+    batch_sampler = ds.make_dynamic_sampler(max_tokens, required_batch_size_multiple=required_batch_size_multiple)
+    batch_sizes = [len(x) for x in batch_sampler]
+    assert len(set(batch_sizes)) > 1  # it's not dynamic batch size if every batch is the same length
+    assert sum(batch_sizes) == len(ds)  # no dropped or added examples
+    data_loader = DataLoader(ds, batch_sampler=batch_sampler, collate_fn=ds.collate_fn, num_workers=2)
+    failures = []
+    num_src_per_batch = []
+    for batch in data_loader:
+        src_shape = batch["input_ids"].shape
+        bs = src_shape[0]
+        assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
+        num_src_tokens = np.product(batch["input_ids"].shape)
+        num_src_per_batch.append(num_src_tokens)
+        if num_src_tokens > (max_tokens * 1.1):
+            failures.append(num_src_tokens)
+    assert num_src_per_batch[0] == max(num_src_per_batch)
+    if failures:
+        raise AssertionError(f"too many tokens in {len(failures)} batches")
+
+
+def test_sortish_sampler_reduces_padding():
+    ds, _, tokenizer = _get_dataset(max_len=512)
+    bs = 2
+    sortish_sampler = ds.make_sortish_sampler(bs, shuffle=False)
+
+    naive_dl = DataLoader(ds, batch_size=bs, collate_fn=ds.collate_fn, num_workers=2)
+    sortish_dl = DataLoader(ds, batch_size=bs, collate_fn=ds.collate_fn, num_workers=2, sampler=sortish_sampler)
+
+    pad = tokenizer.pad_token_id
+
+    def count_pad_tokens(data_loader, k="input_ids"):
+        return [batch[k].eq(pad).sum().item() for batch in data_loader]
+
+    assert sum(count_pad_tokens(sortish_dl, k="labels")) < sum(count_pad_tokens(naive_dl, k="labels"))
+    assert sum(count_pad_tokens(sortish_dl)) < sum(count_pad_tokens(naive_dl))
+    assert len(sortish_dl) == len(naive_dl)
+
+
+def _get_dataset(n_obs=1000, max_len=128):
+    if os.getenv("USE_REAL_DATA", False):
+        data_dir = "examples/seq2seq/wmt_en_ro"
+        max_tokens = max_len * 2 * 64
+        if not Path(data_dir).joinpath("train.len").exists():
+            save_len_file(MARIAN_TINY, data_dir)
+    else:
+        data_dir = "examples/seq2seq/test_data/wmt_en_ro"
+        max_tokens = max_len * 4
+        save_len_file(MARIAN_TINY, data_dir)
+
+    tokenizer = AutoTokenizer.from_pretrained(MARIAN_TINY)
+    ds = Seq2SeqDataset(
+        tokenizer,
+        data_dir=data_dir,
+        type_path="train",
+        max_source_length=max_len,
+        max_target_length=max_len,
+        n_obs=n_obs,
+    )
+    return ds, max_tokens, tokenizer
+
+
+def test_distributed_sortish_sampler_splits_indices_between_procs():
+    ds, max_tokens, tokenizer = _get_dataset()
+    ids1 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=0, add_extra_examples=False))
+    ids2 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=1, add_extra_examples=False))
+    assert ids1.intersection(ids2) == set()
+
+
+@pytest.mark.parametrize(
+    "tok_name",
+    [
+        MBART_TINY,
+        MARIAN_TINY,
+        T5_TINY,
+        BART_TINY,
+        PEGASUS_XSUM,
+    ],
+)
+def test_dataset_kwargs(tok_name):
+    tokenizer = AutoTokenizer.from_pretrained(tok_name)
+    if tok_name == MBART_TINY:
         train_dataset = Seq2SeqDataset(
             tokenizer,
-            data_dir=tmp_dir,
+            data_dir=make_test_data_dir(),
             type_path="train",
-            max_source_length=max_src_len,
+            max_source_length=4,
-            max_target_length=max_tgt_len,  # ignored
+            max_target_length=8,
-            src_lang=src_lang,
+            src_lang="EN",
-            tgt_lang=tgt_lang,
+            tgt_lang="FR",
         )
-        dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
+        kwargs = train_dataset.dataset_kwargs
-        for batch in dataloader:
+        assert "src_lang" in kwargs and "tgt_lang" in kwargs
-            assert isinstance(batch, dict)
+    else:
-            assert batch["attention_mask"].shape == batch["input_ids"].shape
+        train_dataset = Seq2SeqDataset(
-            # show that articles were trimmed.
+            tokenizer, data_dir=make_test_data_dir(), type_path="train", max_source_length=4, max_target_length=8
-            assert batch["input_ids"].shape[1] == max_src_len
-            # show that targets are the same len
-            assert batch["labels"].shape[1] == max_tgt_len
-            if tok_name != MBART_TINY:
-                continue
-            # check language codes in correct place
-            batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], tokenizer.pad_token_id)
-            assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
-            assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
-            assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
-            assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
-
-            break  # No need to test every batch
-
-    @parameterized.expand([BART_TINY, BERT_BASE_CASED])
-    def test_legacy_dataset_truncation(self, tok):
-        tokenizer = AutoTokenizer.from_pretrained(tok)
-        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
-        max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
-        max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
-        trunc_target = 4
-        train_dataset = LegacySeq2SeqDataset(
-            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)
+        kwargs = train_dataset.dataset_kwargs
-        for batch in dataloader:
+        assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
-            assert batch["attention_mask"].shape == batch["input_ids"].shape
+        assert len(kwargs) == 1 if tok_name == BART_TINY else len(kwargs) == 0
-            # show that articles were trimmed.
-            assert batch["input_ids"].shape[1] == max_len_source
-            assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
-            # show that targets were truncated
-            assert batch["labels"].shape[1] == trunc_target  # Truncated
-            assert max_len_target > trunc_target  # Truncated
-            break  # No need to test every batch
-
-    def test_pack_dataset(self):
-        tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
-
-        tmp_dir = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
-        orig_examples = tmp_dir.joinpath("train.source").open().readlines()
-        save_dir = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
-        pack_data_dir(tokenizer, tmp_dir, 128, save_dir)
-        orig_paths = {x.name for x in tmp_dir.iterdir()}
-        new_paths = {x.name for x in save_dir.iterdir()}
-        packed_examples = save_dir.joinpath("train.source").open().readlines()
-        # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
-        # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
-        assert len(packed_examples) < len(orig_examples)
-        assert len(packed_examples) == 1
-        assert len(packed_examples[0]) == sum(len(x) for x in orig_examples)
-        assert orig_paths == new_paths
-
-    @pytest.mark.skipif(not FAIRSEQ_AVAILABLE, reason="This test requires fairseq")
-    def test_dynamic_batch_size(self):
-        if not FAIRSEQ_AVAILABLE:
-            return
-        ds, max_tokens, tokenizer = self._get_dataset(max_len=64)
-        required_batch_size_multiple = 64
-        batch_sampler = ds.make_dynamic_sampler(max_tokens, required_batch_size_multiple=required_batch_size_multiple)
-        batch_sizes = [len(x) for x in batch_sampler]
-        assert len(set(batch_sizes)) > 1  # it's not dynamic batch size if every batch is the same length
-        assert sum(batch_sizes) == len(ds)  # no dropped or added examples
-        data_loader = DataLoader(ds, batch_sampler=batch_sampler, collate_fn=ds.collate_fn, num_workers=2)
-        failures = []
-        num_src_per_batch = []
-        for batch in data_loader:
-            src_shape = batch["input_ids"].shape
-            bs = src_shape[0]
-            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
-            num_src_tokens = np.product(batch["input_ids"].shape)
-            num_src_per_batch.append(num_src_tokens)
-            if num_src_tokens > (max_tokens * 1.1):
-                failures.append(num_src_tokens)
-        assert num_src_per_batch[0] == max(num_src_per_batch)
-        if failures:
-            raise AssertionError(f"too many tokens in {len(failures)} batches")
-
-    def test_sortish_sampler_reduces_padding(self):
-        ds, _, tokenizer = self._get_dataset(max_len=512)
-        bs = 2
-        sortish_sampler = ds.make_sortish_sampler(bs, shuffle=False)
-
-        naive_dl = DataLoader(ds, batch_size=bs, collate_fn=ds.collate_fn, num_workers=2)
-        sortish_dl = DataLoader(ds, batch_size=bs, collate_fn=ds.collate_fn, num_workers=2, sampler=sortish_sampler)
-
-        pad = tokenizer.pad_token_id
-
-        def count_pad_tokens(data_loader, k="input_ids"):
-            return [batch[k].eq(pad).sum().item() for batch in data_loader]
-
-        assert sum(count_pad_tokens(sortish_dl, k="labels")) < sum(count_pad_tokens(naive_dl, k="labels"))
-        assert sum(count_pad_tokens(sortish_dl)) < sum(count_pad_tokens(naive_dl))
-        assert len(sortish_dl) == len(naive_dl)
-
-    def _get_dataset(self, n_obs=1000, max_len=128):
-        if os.getenv("USE_REAL_DATA", False):
-            data_dir = "examples/seq2seq/wmt_en_ro"
-            max_tokens = max_len * 2 * 64
-            if not Path(data_dir).joinpath("train.len").exists():
-                save_len_file(MARIAN_TINY, data_dir)
-        else:
-            data_dir = "examples/seq2seq/test_data/wmt_en_ro"
-            max_tokens = max_len * 4
-            save_len_file(MARIAN_TINY, data_dir)
-
-        tokenizer = AutoTokenizer.from_pretrained(MARIAN_TINY)
-        ds = Seq2SeqDataset(
-            tokenizer,
-            data_dir=data_dir,
-            type_path="train",
-            max_source_length=max_len,
-            max_target_length=max_len,
-            n_obs=n_obs,
-        )
-        return ds, max_tokens, tokenizer
-
-    def test_distributed_sortish_sampler_splits_indices_between_procs(self):
-        ds, max_tokens, tokenizer = self._get_dataset()
-        ids1 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=0, add_extra_examples=False))
-        ids2 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=1, add_extra_examples=False))
-        assert ids1.intersection(ids2) == set()
-
-    @parameterized.expand(
-        [
-            MBART_TINY,
-            MARIAN_TINY,
-            T5_TINY,
-            BART_TINY,
-            PEGASUS_XSUM,
-        ],
-    )
-    def test_dataset_kwargs(self, tok_name):
-        tokenizer = AutoTokenizer.from_pretrained(tok_name)
-        if tok_name == MBART_TINY:
-            train_dataset = Seq2SeqDataset(
-                tokenizer,
-                data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()),
-                type_path="train",
-                max_source_length=4,
-                max_target_length=8,
-                src_lang="EN",
-                tgt_lang="FR",
-            )
-            kwargs = train_dataset.dataset_kwargs
-            assert "src_lang" in kwargs and "tgt_lang" in kwargs
-        else:
-            train_dataset = Seq2SeqDataset(
-                tokenizer,
-                data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()),
-                type_path="train",
-                max_source_length=4,
-                max_target_length=8,
-            )
-            kwargs = train_dataset.dataset_kwargs
-            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
-            assert len(kwargs) == 1 if tok_name == BART_TINY else len(kwargs) == 0

examples/seq2seq/test_finetune_trainer.py

@@ -1,233 +1,106 @@
 import os
 import sys
-from pathlib import Path
+import tempfile
 from unittest.mock import patch
 
-import pytest
+from transformers.testing_utils import slow
+from transformers.trainer_utils import TrainerState, set_seed
 
-from transformers import BertTokenizer, EncoderDecoderModel, is_torch_available
+from .finetune_trainer import main
-from transformers.file_utils import is_datasets_available
-from transformers.testing_utils import TestCasePlus, slow
-from transformers.trainer_callback import TrainerState
-from transformers.trainer_utils import set_seed
-
-from .finetune_trainer import Seq2SeqTrainingArguments, main
-from .seq2seq_trainer import Seq2SeqTrainer
 from .test_seq2seq_examples import MBART_TINY
-from .utils import execute_async_std
 
 
-if is_torch_available():
-    import torch
-
 set_seed(42)
 MARIAN_MODEL = "sshleifer/student_marian_en_ro_6_1"
 
 
-class TestFinetuneTrainer(TestCasePlus):
+def test_finetune_trainer():
-    def test_finetune_trainer(self):
+    output_dir = run_trainer(1, "12", MBART_TINY, 1)
-        output_dir = self.run_trainer(1, "12", MBART_TINY, 1)
+    logs = TrainerState.load_from_json(os.path.join(output_dir, "trainer_state.json")).log_history
-        logs = TrainerState.load_from_json(os.path.join(output_dir, "trainer_state.json")).log_history
+    eval_metrics = [log for log in logs if "eval_loss" in log.keys()]
-        eval_metrics = [log for log in logs if "eval_loss" in log.keys()]
+    first_step_stats = eval_metrics[0]
-        first_step_stats = eval_metrics[0]
+    assert "eval_bleu" in first_step_stats
-        assert "eval_bleu" in first_step_stats
 
-    @slow
-    def test_finetune_trainer_slow(self):
-        # There is a missing call to __init__process_group somewhere
-        output_dir = self.run_trainer(eval_steps=2, max_len="128", model_name=MARIAN_MODEL, num_train_epochs=10)
 
-        # Check metrics
+@slow
-        logs = TrainerState.load_from_json(os.path.join(output_dir, "trainer_state.json")).log_history
+def test_finetune_trainer_slow():
-        eval_metrics = [log for log in logs if "eval_loss" in log.keys()]
+    # TODO(SS): This will fail on devices with more than 1 GPU.
-        first_step_stats = eval_metrics[0]
+    # There is a missing call to __init__process_group somewhere
-        last_step_stats = eval_metrics[-1]
+    output_dir = run_trainer(eval_steps=2, max_len="128", model_name=MARIAN_MODEL, num_train_epochs=3)
 
-        assert first_step_stats["eval_bleu"] < last_step_stats["eval_bleu"]  # model learned nothing
+    # Check metrics
-        assert isinstance(last_step_stats["eval_bleu"], float)
+    logs = TrainerState.load_from_json(os.path.join(output_dir, "trainer_state.json")).log_history
+    eval_metrics = [log for log in logs if "eval_loss" in log.keys()]
+    first_step_stats = eval_metrics[0]
+    last_step_stats = eval_metrics[-1]
 
-        # test if do_predict saves generations and metrics
+    assert first_step_stats["eval_bleu"] < last_step_stats["eval_bleu"]  # model learned nothing
-        contents = os.listdir(output_dir)
+    assert isinstance(last_step_stats["eval_bleu"], float)
-        contents = {os.path.basename(p) for p in contents}
-        assert "test_generations.txt" in contents
-        assert "test_results.json" in contents
 
-    @slow
+    # test if do_predict saves generations and metrics
-    def test_finetune_bert2bert(self):
+    contents = os.listdir(output_dir)
-        if not is_datasets_available():
+    contents = {os.path.basename(p) for p in contents}
-            return
+    assert "test_generations.txt" in contents
+    assert "test_results.json" in contents
 
-        import datasets
 
-        bert2bert = EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny", "prajjwal1/bert-tiny")
+def run_trainer(eval_steps: int, max_len: str, model_name: str, num_train_epochs: int):
-        tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+    data_dir = "examples/seq2seq/test_data/wmt_en_ro"
+    output_dir = tempfile.mkdtemp(prefix="test_output")
+    argv = [
+        "--model_name_or_path",
+        model_name,
+        "--data_dir",
+        data_dir,
+        "--output_dir",
+        output_dir,
+        "--overwrite_output_dir",
+        "--n_train",
+        "8",
+        "--n_val",
+        "8",
+        "--max_source_length",
+        max_len,
+        "--max_target_length",
+        max_len,
+        "--val_max_target_length",
+        max_len,
+        "--do_train",
+        "--do_eval",
+        "--do_predict",
+        "--num_train_epochs",
+        str(num_train_epochs),
+        "--per_device_train_batch_size",
+        "4",
+        "--per_device_eval_batch_size",
+        "4",
+        "--learning_rate",
+        "3e-4",
+        "--warmup_steps",
+        "8",
+        "--evaluate_during_training",
+        "--predict_with_generate",
+        "--logging_steps",
+        0,
+        "--save_steps",
+        str(eval_steps),
+        "--eval_steps",
+        str(eval_steps),
+        "--sortish_sampler",
+        "--label_smoothing",
+        "0.1",
+        # "--eval_beams",
+        # "2",
+        "--adafactor",
+        "--task",
+        "translation",
+        "--tgt_lang",
+        "ro_RO",
+        "--src_lang",
+        "en_XX",
+    ]
+    testargs = ["finetune_trainer.py"] + argv
+    with patch.object(sys, "argv", testargs):
+        main()
 
-        bert2bert.config.vocab_size = bert2bert.config.encoder.vocab_size
+    return output_dir
-        bert2bert.config.decoder_start_token_id = tokenizer.cls_token_id
-
-        train_dataset = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train[:1%]")
-        val_dataset = datasets.load_dataset("cnn_dailymail", "3.0.0", split="validation[:1%]")
-
-        train_dataset = train_dataset.select(range(32))
-        val_dataset = val_dataset.select(range(16))
-
-        rouge = datasets.load_metric("rouge")
-
-        batch_size = 4
-
-        def _map_to_encoder_decoder_inputs(batch):
-            # Tokenizer will automatically set [BOS] <text> [EOS]
-            inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=512)
-            outputs = tokenizer(batch["highlights"], padding="max_length", truncation=True, max_length=128)
-            batch["input_ids"] = inputs.input_ids
-            batch["attention_mask"] = inputs.attention_mask
-
-            batch["decoder_input_ids"] = outputs.input_ids
-            batch["labels"] = outputs.input_ids.copy()
-            batch["labels"] = [
-                [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
-            ]
-            batch["decoder_attention_mask"] = outputs.attention_mask
-
-            assert all([len(x) == 512 for x in inputs.input_ids])
-            assert all([len(x) == 128 for x in outputs.input_ids])
-
-            return batch
-
-        def _compute_metrics(pred):
-            labels_ids = pred.label_ids
-            pred_ids = pred.predictions
-
-            # all unnecessary tokens are removed
-            pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
-            label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)
-
-            rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])[
-                "rouge2"
-            ].mid
-
-            return {
-                "rouge2_precision": round(rouge_output.precision, 4),
-                "rouge2_recall": round(rouge_output.recall, 4),
-                "rouge2_fmeasure": round(rouge_output.fmeasure, 4),
-            }
-
-        # map train dataset
-        train_dataset = train_dataset.map(
-            _map_to_encoder_decoder_inputs,
-            batched=True,
-            batch_size=batch_size,
-            remove_columns=["article", "highlights"],
-        )
-        train_dataset.set_format(
-            type="torch",
-            columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],
-        )
-
-        # same for validation dataset
-        val_dataset = val_dataset.map(
-            _map_to_encoder_decoder_inputs,
-            batched=True,
-            batch_size=batch_size,
-            remove_columns=["article", "highlights"],
-        )
-        val_dataset.set_format(
-            type="torch",
-            columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],
-        )
-
-        output_dir = self.get_auto_remove_tmp_dir()
-
-        training_args = Seq2SeqTrainingArguments(
-            output_dir=output_dir,
-            per_device_train_batch_size=batch_size,
-            per_device_eval_batch_size=batch_size,
-            predict_with_generate=True,
-            evaluate_during_training=True,
-            do_train=True,
-            do_eval=True,
-            warmup_steps=0,
-            eval_steps=2,
-            logging_steps=2,
-        )
-
-        # instantiate trainer
-        trainer = Seq2SeqTrainer(
-            model=bert2bert,
-            args=training_args,
-            compute_metrics=_compute_metrics,
-            train_dataset=train_dataset,
-            eval_dataset=val_dataset,
-        )
-
-        # start training
-        trainer.train()
-
-    def run_trainer(self, eval_steps: int, max_len: str, model_name: str, num_train_epochs: int):
-
-        # XXX: remove hardcoded path
-        data_dir = "examples/seq2seq/test_data/wmt_en_ro"
-        output_dir = self.get_auto_remove_tmp_dir()
-        argv = f"""
-            --model_name_or_path {model_name}
-            --data_dir {data_dir}
-            --output_dir {output_dir}
-            --overwrite_output_dir
-            --n_train 8
-            --n_val 8
-            --max_source_length {max_len}
-            --max_target_length {max_len}
-            --val_max_target_length {max_len}
-            --do_train
-            --do_eval
-            --do_predict
-            --num_train_epochs {str(num_train_epochs)}
-            --per_device_train_batch_size 4
-            --per_device_eval_batch_size 4
-            --learning_rate 3e-4
-            --warmup_steps 8
-            --evaluate_during_training
-            --predict_with_generate
-            --logging_steps 0
-            --save_steps {str(eval_steps)}
-            --eval_steps {str(eval_steps)}
-            --sortish_sampler
-            --label_smoothing 0.1
-            --adafactor
-            --task translation
-            --tgt_lang ro_RO
-            --src_lang en_XX
-        """.split()
-        # --eval_beams  2
-
-        n_gpu = torch.cuda.device_count()
-        if n_gpu > 1:
-
-            path = Path(__file__).resolve()
-            cur_path = path.parents[0]
-
-            path = Path(__file__).resolve()
-            examples_path = path.parents[1]
-            src_path = f"{path.parents[2]}/src"
-            env = os.environ.copy()
-            env["PYTHONPATH"] = f"{examples_path}:{src_path}:{env.get('PYTHONPATH', '')}"
-
-            distributed_args = (
-                f"-m torch.distributed.launch --nproc_per_node={n_gpu} {cur_path}/finetune_trainer.py".split()
-            )
-            cmd = [sys.executable] + distributed_args + argv
-
-            print("\nRunning: ", " ".join(cmd))
-
-            result = execute_async_std(cmd, env=env, stdin=None, timeout=180, quiet=False, echo=False)
-
-            assert result.stdout, "produced no output"
-            if result.returncode > 0:
-                pytest.fail(f"failed with returncode {result.returncode}")
-        else:
-            # 0 or 1 gpu
-            testargs = ["finetune_trainer.py"] + argv
-            with patch.object(sys, "argv", testargs):
-                main()
-
-        return output_dir

examples/seq2seq/test_seq2seq_examples.py

@@ -3,6 +3,7 @@ import logging
 import os
 import sys
 import tempfile
+import unittest
 from pathlib import Path
 from unittest.mock import patch
 
@@ -12,14 +13,13 @@ import torch
 
 import lightning_base
 from convert_pl_checkpoint_to_hf import convert_pl_to_hf
-from distillation import distill_main
+from distillation import distill_main, evaluate_checkpoint
 from finetune import SummarizationModule, main
-from parameterized import parameterized
 from run_eval import generate_summaries_or_translations, run_generate
 from run_eval_search import run_search
 from transformers import AutoConfig, AutoModelForSeq2SeqLM
 from transformers.hf_api import HfApi
-from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
+from transformers.testing_utils import CaptureStderr, CaptureStdout, require_multigpu, require_torch_and_cuda, slow
 from utils import ROUGE_KEYS, label_smoothed_nll_loss, lmap, load_json
 
 
@@ -86,9 +86,9 @@ CHEAP_ARGS = {
     "n_val": -1,
     "n_test": -1,
     "student_encoder_layers": 1,
+    "alpha_encoder_loss": 0.0,
     "freeze_encoder": False,
     "auto_scale_batch_size": False,
-    "overwrite_output_dir": False,
 }
 
 
@@ -111,23 +111,24 @@ logger.addHandler(stream_handler)
 logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
 
 
-def make_test_data_dir(tmp_dir):
+def make_test_data_dir(**kwargs):
+    tmp_dir = Path(tempfile.mkdtemp(**kwargs))
     for split in ["train", "val", "test"]:
-        _dump_articles(os.path.join(tmp_dir, f"{split}.source"), ARTICLES)
+        _dump_articles((tmp_dir / f"{split}.source"), ARTICLES)
-        _dump_articles(os.path.join(tmp_dir, f"{split}.target"), SUMMARIES)
+        _dump_articles((tmp_dir / f"{split}.target"), SUMMARIES)
     return tmp_dir
 
 
-class TestSummarizationDistiller(TestCasePlus):
+class TestSummarizationDistiller(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
         return cls
 
     @slow
-    @require_torch_gpu
+    @require_torch_and_cuda
     def test_hub_configs(self):
-        """I put require_torch_gpu cause I only want this to run with self-scheduled."""
+        """I put require_torch_and_cuda cause I only want this to run with self-scheduled."""
 
         model_list = HfApi().model_list()
         org = "sshleifer"
@@ -143,6 +144,17 @@ class TestSummarizationDistiller(TestCasePlus):
                 failures.append(m)
         assert not failures, f"The following models could not be loaded through AutoConfig: {failures}"
 
+    @require_multigpu
+    @unittest.skip("Broken at the moment")
+    def test_multigpu(self):
+        updates = dict(
+            no_teacher=True,
+            freeze_encoder=True,
+            gpus=2,
+            sortish_sampler=True,
+        )
+        self._test_distiller_cli(updates, check_contents=False)
+
     def test_distill_no_teacher(self):
         updates = dict(student_encoder_layers=2, student_decoder_layers=1, no_teacher=True)
         self._test_distiller_cli(updates)
@@ -162,12 +174,13 @@ class TestSummarizationDistiller(TestCasePlus):
         self.assertEqual(1, len(ckpts))
         transformer_ckpts = list(Path(model.output_dir).glob("**/*.bin"))
         self.assertEqual(len(transformer_ckpts), 2)
-        examples = lmap(str.strip, Path(model.hparams.data_dir).joinpath("test.source").open().readlines())
+        examples = lmap(str.strip, model.hparams.data_dir.joinpath("test.source").open().readlines())
-        out_path = tempfile.mktemp()  # XXX: not being cleaned up
+        out_path = tempfile.mktemp()
         generate_summaries_or_translations(examples, out_path, str(model.output_dir / "best_tfmr"))
         self.assertTrue(Path(out_path).exists())
 
-        out_path_new = self.get_auto_remove_tmp_dir()
+        evaluate_checkpoint(ckpts[0], dest_dir=Path(tempfile.mkdtemp()))
+        out_path_new = tempfile.mkdtemp()
         convert_pl_to_hf(ckpts[0], transformer_ckpts[0].parent, out_path_new)
         assert os.path.exists(os.path.join(out_path_new, "pytorch_model.bin"))
 
@@ -215,6 +228,9 @@ class TestSummarizationDistiller(TestCasePlus):
         assert len(all_files) > 2
         self.assertEqual(len(transformer_ckpts), 2)
 
+        evaluate_checkpoint(ckpts[0], dest_dir=Path(tempfile.mkdtemp()))
+
+    @unittest.skip("T5 distillation is broken at the moment")
     def test_distill_t5(self):
         updates = dict(
             student_encoder_layers=1,
@@ -239,11 +255,12 @@ class TestSummarizationDistiller(TestCasePlus):
             model_name_or_path="sshleifer/tinier_bart",
             teacher=CHEAP_ARGS["model_name_or_path"],
             val_check_interval=0.5,
+            alpha_encoder_loss=0.4,
         )
         default_updates.update(updates)
         args_d: dict = CHEAP_ARGS.copy()
-        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
+        tmp_dir = make_test_data_dir()
-        output_dir = self.get_auto_remove_tmp_dir()
+        output_dir = tempfile.mkdtemp(prefix="output_")
 
         args_d.update(data_dir=tmp_dir, output_dir=output_dir, **default_updates)
         model = distill_main(argparse.Namespace(**args_d))
@@ -268,253 +285,252 @@ class TestSummarizationDistiller(TestCasePlus):
         return model
 
 
-class TestTheRest(TestCasePlus):
+def run_eval_tester(model):
-    def run_eval_tester(self, model):
+    input_file_name = Path(tempfile.mkdtemp()) / "utest_input.source"
-        input_file_name = Path(self.get_auto_remove_tmp_dir()) / "utest_input.source"
+    output_file_name = input_file_name.parent / "utest_output.txt"
-        output_file_name = input_file_name.parent / "utest_output.txt"
+    assert not output_file_name.exists()
-        assert not output_file_name.exists()
+    articles = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
-        articles = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
+    _dump_articles(input_file_name, articles)
-        _dump_articles(input_file_name, articles)
+    score_path = str(Path(tempfile.mkdtemp()) / "scores.json")
+    task = "translation_en_to_de" if model == T5_TINY else "summarization"
+    testargs = f"""
+        run_eval_search.py
+        {model}
+        {input_file_name}
+        {output_file_name}
+        --score_path {score_path}
+        --task {task}
+        --num_beams 2
+        --length_penalty 2.0
+        """.split()
 
-        score_path = str(Path(self.get_auto_remove_tmp_dir()) / "scores.json")
+    with patch.object(sys, "argv", testargs):
-        task = "translation_en_to_de" if model == T5_TINY else "summarization"
+        run_generate()
-        testargs = f"""
+        assert Path(output_file_name).exists()
-            run_eval_search.py
+        os.remove(Path(output_file_name))
-            {model}
-            {input_file_name}
-            {output_file_name}
-            --score_path {score_path}
-            --task {task}
-            --num_beams 2
-            --length_penalty 2.0
-            """.split()
 
-        with patch.object(sys, "argv", testargs):
-            run_generate()
-            assert Path(output_file_name).exists()
-            # os.remove(Path(output_file_name))
 
-    # test one model to quickly (no-@slow) catch simple problems and do an
+# test one model to quickly (no-@slow) catch simple problems and do an
-    # extensive testing of functionality with multiple models as @slow separately
+# extensive testing of functionality with multiple models as @slow separately
-    def test_run_eval(self):
+def test_run_eval():
-        self.run_eval_tester(T5_TINY)
+    run_eval_tester(T5_TINY)
 
-    # any extra models should go into the list here - can be slow
-    @parameterized.expand([BART_TINY, MBART_TINY])
-    @slow
-    def test_run_eval_slow(self, model):
-        self.run_eval_tester(model)
 
-    # testing with 2 models to validate: 1. translation (t5) 2. summarization (mbart)
+# any extra models should go into the list here - can be slow
-    @parameterized.expand([T5_TINY, MBART_TINY])
+@slow
-    @slow
+@pytest.mark.parametrize("model", [BART_TINY, MBART_TINY])
-    def test_run_eval_search(self, model):
+def test_run_eval_slow(model):
-        input_file_name = Path(self.get_auto_remove_tmp_dir()) / "utest_input.source"
+    run_eval_tester(model)
-        output_file_name = input_file_name.parent / "utest_output.txt"
-        assert not output_file_name.exists()
 
-        text = {
-            "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
-            "de": [
-                "Maschinelles Lernen ist großartig, oder?",
-                "Ich esse gerne Bananen",
-                "Morgen ist wieder ein toller Tag!",
-            ],
-        }
 
-        tmp_dir = Path(self.get_auto_remove_tmp_dir())
+# testing with 2 models to validate: 1. translation (t5) 2. summarization (mbart)
-        score_path = str(tmp_dir / "scores.json")
+@slow
-        reference_path = str(tmp_dir / "val.target")
+@pytest.mark.parametrize("model", [T5_TINY, MBART_TINY])
-        _dump_articles(input_file_name, text["en"])
+def test_run_eval_search(model):
-        _dump_articles(reference_path, text["de"])
+    input_file_name = Path(tempfile.mkdtemp()) / "utest_input.source"
-        task = "translation_en_to_de" if model == T5_TINY else "summarization"
+    output_file_name = input_file_name.parent / "utest_output.txt"
-        testargs = f"""
+    assert not output_file_name.exists()
-            run_eval_search.py
-            {model}
-            {str(input_file_name)}
-            {str(output_file_name)}
-            --score_path {score_path}
-            --reference_path {reference_path}
-            --task {task}
-            """.split()
-        testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"])
 
-        with patch.object(sys, "argv", testargs):
+    text = {
-            with CaptureStdout() as cs:
+        "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
-                run_search()
+        "de": [
-            expected_strings = [" num_beams | length_penalty", model, "Best score args"]
+            "Maschinelles Lernen ist großartig, oder?",
-            un_expected_strings = ["Info"]
+            "Ich esse gerne Bananen",
-            if "translation" in task:
+            "Morgen ist wieder ein toller Tag!",
-                expected_strings.append("bleu")
+        ],
-            else:
+    }
-                expected_strings.extend(ROUGE_KEYS)
-            for w in expected_strings:
-                assert w in cs.out
-            for w in un_expected_strings:
-                assert w not in cs.out
-            assert Path(output_file_name).exists()
-            os.remove(Path(output_file_name))
 
-    @parameterized.expand(
+    tmp_dir = Path(tempfile.mkdtemp())
-        [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY],
+    score_path = str(tmp_dir / "scores.json")
-    )
+    reference_path = str(tmp_dir / "val.target")
-    def test_finetune(self, model):
+    _dump_articles(input_file_name, text["en"])
-        args_d: dict = CHEAP_ARGS.copy()
+    _dump_articles(reference_path, text["de"])
-        task = "translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
+    task = "translation_en_to_de" if model == T5_TINY else "summarization"
-        args_d["label_smoothing"] = 0.1 if task == "translation" else 0
+    testargs = f"""
+        run_eval_search.py
+        {model}
+        {str(input_file_name)}
+        {str(output_file_name)}
+        --score_path {score_path}
+        --reference_path {reference_path}
+        --task {task}
+        """.split()
+    testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"])
 
-        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
+    with patch.object(sys, "argv", testargs):
-        output_dir = self.get_auto_remove_tmp_dir()
+        with CaptureStdout() as cs:
-        args_d.update(
+            run_search()
-            data_dir=tmp_dir,
+        expected_strings = [" num_beams | length_penalty", model, "Best score args"]
-            model_name_or_path=model,
+        un_expected_strings = ["Info"]
-            tokenizer_name=None,
+        if "translation" in task:
-            train_batch_size=2,
+            expected_strings.append("bleu")
-            eval_batch_size=2,
-            output_dir=output_dir,
-            do_predict=True,
-            task=task,
-            src_lang="en_XX",
-            tgt_lang="ro_RO",
-            freeze_encoder=True,
-            freeze_embeds=True,
-        )
-        assert "n_train" in args_d
-        args = argparse.Namespace(**args_d)
-        module = main(args)
-
-        input_embeds = module.model.get_input_embeddings()
-        assert not input_embeds.weight.requires_grad
-        if model == T5_TINY:
-            lm_head = module.model.lm_head
-            assert not lm_head.weight.requires_grad
-            assert (lm_head.weight == input_embeds.weight).all().item()
-        elif model == FSMT_TINY:
-            fsmt = module.model.model
-            embed_pos = fsmt.decoder.embed_positions
-            assert not embed_pos.weight.requires_grad
-            assert not fsmt.decoder.embed_tokens.weight.requires_grad
-            # check that embeds are not the same
-            assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
         else:
-            bart = module.model.model
+            expected_strings.extend(ROUGE_KEYS)
-            embed_pos = bart.decoder.embed_positions
+        for w in expected_strings:
-            assert not embed_pos.weight.requires_grad
+            assert w in cs.out
-            assert not bart.shared.weight.requires_grad
+        for w in un_expected_strings:
-            # check that embeds are the same
+            assert w not in cs.out
-            assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
+        assert Path(output_file_name).exists()
-            assert bart.decoder.embed_tokens == bart.shared
+        os.remove(Path(output_file_name))
 
-        example_batch = load_json(module.output_dir / "text_batch.json")
-        assert isinstance(example_batch, dict)
-        assert len(example_batch) >= 4
 
-    def test_finetune_extra_model_args(self):
+@pytest.mark.parametrize(
-        args_d: dict = CHEAP_ARGS.copy()
+    "model",
+    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY],
+)
+def test_finetune(model):
+    args_d: dict = CHEAP_ARGS.copy()
+    task = "translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
+    args_d["label_smoothing"] = 0.1 if task == "translation" else 0
 
-        task = "summarization"
+    tmp_dir = make_test_data_dir()
-        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
+    output_dir = tempfile.mkdtemp(prefix="output_")
+    args_d.update(
+        data_dir=tmp_dir,
+        model_name_or_path=model,
+        tokenizer_name=None,
+        train_batch_size=2,
+        eval_batch_size=2,
+        output_dir=output_dir,
+        do_predict=True,
+        task=task,
+        src_lang="en_XX",
+        tgt_lang="ro_RO",
+        freeze_encoder=True,
+        freeze_embeds=True,
+    )
+    assert "n_train" in args_d
+    args = argparse.Namespace(**args_d)
+    module = main(args)
 
-        args_d.update(
+    input_embeds = module.model.get_input_embeddings()
-            data_dir=tmp_dir,
+    assert not input_embeds.weight.requires_grad
-            tokenizer_name=None,
+    if model == T5_TINY:
-            train_batch_size=2,
+        lm_head = module.model.lm_head
-            eval_batch_size=2,
+        assert not lm_head.weight.requires_grad
-            do_predict=False,
+        assert (lm_head.weight == input_embeds.weight).all().item()
-            task=task,
+    elif model == FSMT_TINY:
-            src_lang="en_XX",
+        fsmt = module.model.model
-            tgt_lang="ro_RO",
+        embed_pos = fsmt.decoder.embed_positions
-            freeze_encoder=True,
+        assert not embed_pos.weight.requires_grad
-            freeze_embeds=True,
+        assert not fsmt.decoder.embed_tokens.weight.requires_grad
-        )
+        # check that embeds are not the same
+        assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
+    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
 
-        # test models whose config includes the extra_model_args
+
-        model = BART_TINY
+def test_finetune_extra_model_args():
-        output_dir = self.get_auto_remove_tmp_dir()
+    args_d: dict = CHEAP_ARGS.copy()
-        args_d1 = args_d.copy()
+
-        args_d1.update(
+    task = "summarization"
-            model_name_or_path=model,
+    tmp_dir = make_test_data_dir()
-            output_dir=output_dir,
+
-        )
+    args_d.update(
-        extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
+        data_dir=tmp_dir,
-        for p in extra_model_params:
+        tokenizer_name=None,
-            args_d1[p] = 0.5
+        train_batch_size=2,
-        args = argparse.Namespace(**args_d1)
+        eval_batch_size=2,
+        do_predict=False,
+        task=task,
+        src_lang="en_XX",
+        tgt_lang="ro_RO",
+        freeze_encoder=True,
+        freeze_embeds=True,
+    )
+
+    # test models whose config includes the extra_model_args
+    model = BART_TINY
+    output_dir = tempfile.mkdtemp(prefix="output_1_")
+    args_d1 = args_d.copy()
+    args_d1.update(
+        model_name_or_path=model,
+        output_dir=output_dir,
+    )
+    extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
+    for p in extra_model_params:
+        args_d1[p] = 0.5
+    args = argparse.Namespace(**args_d1)
+    model = main(args)
+    for p in extra_model_params:
+        assert getattr(model.config, p) == 0.5, f"failed to override the model config for param {p}"
+
+    # test models whose config doesn't include the extra_model_args
+    model = T5_TINY
+    output_dir = tempfile.mkdtemp(prefix="output_2_")
+    args_d2 = args_d.copy()
+    args_d2.update(
+        model_name_or_path=model,
+        output_dir=output_dir,
+    )
+    unsupported_param = "encoder_layerdrop"
+    args_d2[unsupported_param] = 0.5
+    args = argparse.Namespace(**args_d2)
+    with pytest.raises(Exception) as excinfo:
         model = main(args)
-        for p in extra_model_params:
+    assert str(excinfo.value) == f"model config doesn't have a `{unsupported_param}` attribute"
-            assert getattr(model.config, p) == 0.5, f"failed to override the model config for param {p}"
 
-        # test models whose config doesn't include the extra_model_args
-        model = T5_TINY
-        output_dir = self.get_auto_remove_tmp_dir()
-        args_d2 = args_d.copy()
-        args_d2.update(
-            model_name_or_path=model,
-            output_dir=output_dir,
-        )
-        unsupported_param = "encoder_layerdrop"
-        args_d2[unsupported_param] = 0.5
-        args = argparse.Namespace(**args_d2)
-        with pytest.raises(Exception) as excinfo:
-            model = main(args)
-        assert str(excinfo.value) == f"model config doesn't have a `{unsupported_param}` attribute"
 
-    def test_finetune_lr_schedulers(self):
+def test_finetune_lr_schedulers():
-        args_d: dict = CHEAP_ARGS.copy()
+    args_d: dict = CHEAP_ARGS.copy()
 
-        task = "summarization"
+    task = "summarization"
-        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
+    tmp_dir = make_test_data_dir()
 
-        model = BART_TINY
+    model = BART_TINY
-        output_dir = self.get_auto_remove_tmp_dir()
+    output_dir = tempfile.mkdtemp(prefix="output_1_")
 
-        args_d.update(
+    args_d.update(
-            data_dir=tmp_dir,
+        data_dir=tmp_dir,
-            model_name_or_path=model,
+        model_name_or_path=model,
-            output_dir=output_dir,
+        output_dir=output_dir,
-            tokenizer_name=None,
+        tokenizer_name=None,
-            train_batch_size=2,
+        train_batch_size=2,
-            eval_batch_size=2,
+        eval_batch_size=2,
-            do_predict=False,
+        do_predict=False,
-            task=task,
+        task=task,
-            src_lang="en_XX",
+        src_lang="en_XX",
-            tgt_lang="ro_RO",
+        tgt_lang="ro_RO",
-            freeze_encoder=True,
+        freeze_encoder=True,
-            freeze_embeds=True,
+        freeze_embeds=True,
-        )
+    )
 
-        # emulate finetune.py
+    # emulate finetune.py
-        parser = argparse.ArgumentParser()
+    parser = argparse.ArgumentParser()
-        parser = pl.Trainer.add_argparse_args(parser)
+    parser = pl.Trainer.add_argparse_args(parser)
-        parser = SummarizationModule.add_model_specific_args(parser, os.getcwd())
+    parser = SummarizationModule.add_model_specific_args(parser, os.getcwd())
-        args = {"--help": True}
+    args = {"--help": True}
 
-        # --help test
+    # --help test
-        with pytest.raises(SystemExit) as excinfo:
+    with pytest.raises(SystemExit) as excinfo:
-            with CaptureStdout() as cs:
+        with CaptureStdout() as cs:
-                args = parser.parse_args(args)
+            args = parser.parse_args(args)
-            assert False, "--help is expected to sys.exit"
+        assert False, "--help is expected to sys.exit"
-        assert excinfo.type == SystemExit
+    assert excinfo.type == SystemExit
-        expected = lightning_base.arg_to_scheduler_metavar
+    expected = lightning_base.arg_to_scheduler_metavar
-        assert expected in cs.out, "--help is expected to list the supported schedulers"
+    assert expected in cs.out, "--help is expected to list the supported schedulers"
 
-        # --lr_scheduler=non_existing_scheduler test
+    # --lr_scheduler=non_existing_scheduler test
-        unsupported_param = "non_existing_scheduler"
+    unsupported_param = "non_existing_scheduler"
-        args = {f"--lr_scheduler={unsupported_param}"}
+    args = {f"--lr_scheduler={unsupported_param}"}
-        with pytest.raises(SystemExit) as excinfo:
+    with pytest.raises(SystemExit) as excinfo:
-            with CaptureStderr() as cs:
+        with CaptureStderr() as cs:
-                args = parser.parse_args(args)
+            args = parser.parse_args(args)
-            assert False, "invalid argument is expected to sys.exit"
+        assert False, "invalid argument is expected to sys.exit"
-        assert excinfo.type == SystemExit
+    assert excinfo.type == SystemExit
-        expected = f"invalid choice: '{unsupported_param}'"
+    expected = f"invalid choice: '{unsupported_param}'"
-        assert expected in cs.err, f"should have bailed on invalid choice of scheduler {unsupported_param}"
+    assert expected in cs.err, f"should have bailed on invalid choice of scheduler {unsupported_param}"
 
-        # --lr_scheduler=existing_scheduler test
+    # --lr_scheduler=existing_scheduler test
-        supported_param = "cosine"
+    supported_param = "cosine"
-        args_d1 = args_d.copy()
+    args_d1 = args_d.copy()
-        args_d1["lr_scheduler"] = supported_param
+    args_d1["lr_scheduler"] = supported_param
-        args = argparse.Namespace(**args_d1)
+    args = argparse.Namespace(**args_d1)
-        model = main(args)
+    model = main(args)
-        assert (
+    assert getattr(model.hparams, "lr_scheduler") == supported_param, f"lr_scheduler={supported_param} shouldn't fail"
-            getattr(model.hparams, "lr_scheduler") == supported_param
-        ), f"lr_scheduler={supported_param} shouldn't fail"

examples/seq2seq/test_seq2seq_examples_multi_gpu.py

@@ -1,199 +0,0 @@
-# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.
-
-import logging
-import os
-import sys
-from pathlib import Path
-
-import pytest
-
-from transformers import is_torch_available
-from transformers.testing_utils import TestCasePlus, require_torch_multigpu
-
-from .utils import execute_async_std, load_json
-
-
-if is_torch_available():
-    import torch
-
-
-logging.basicConfig(level=logging.DEBUG)
-
-logger = logging.getLogger()
-CUDA_AVAILABLE = torch.cuda.is_available()
-CHEAP_ARGS = {
-    "max_tokens_per_batch": None,
-    "supervise_forward": True,
-    "normalize_hidden": True,
-    "label_smoothing": 0.2,
-    "eval_max_gen_length": None,
-    "eval_beams": 1,
-    "val_metric": "loss",
-    "save_top_k": 1,
-    "adafactor": True,
-    "early_stopping_patience": 2,
-    "logger_name": "default",
-    "length_penalty": 0.5,
-    "cache_dir": "",
-    "task": "summarization",
-    "num_workers": 2,
-    "alpha_hid": 0,
-    "freeze_embeds": True,
-    "enc_only": False,
-    "tgt_suffix": "",
-    "resume_from_checkpoint": None,
-    "sortish_sampler": True,
-    "student_decoder_layers": 1,
-    "val_check_interval": 1.0,
-    "output_dir": "",
-    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
-    "no_teacher": False,
-    "fp16_opt_level": "O1",
-    "gpus": 1 if CUDA_AVAILABLE else 0,
-    "n_tpu_cores": 0,
-    "max_grad_norm": 1.0,
-    "do_train": True,
-    "do_predict": True,
-    "accumulate_grad_batches": 1,
-    "server_ip": "",
-    "server_port": "",
-    "seed": 42,
-    "model_name_or_path": "sshleifer/bart-tiny-random",
-    "config_name": "",
-    "tokenizer_name": "facebook/bart-large",
-    "do_lower_case": False,
-    "learning_rate": 0.3,
-    "lr_scheduler": "linear",
-    "weight_decay": 0.0,
-    "adam_epsilon": 1e-08,
-    "warmup_steps": 0,
-    "max_epochs": 1,
-    "train_batch_size": 2,
-    "eval_batch_size": 2,
-    "max_source_length": 12,
-    "max_target_length": 12,
-    "val_max_target_length": 12,
-    "test_max_target_length": 12,
-    "fast_dev_run": False,
-    "no_cache": False,
-    "n_train": -1,
-    "n_val": -1,
-    "n_test": -1,
-    "student_encoder_layers": 1,
-    "freeze_encoder": False,
-    "auto_scale_batch_size": False,
-}
-
-
-def _dump_articles(path: Path, articles: list):
-    content = "\n".join(articles)
-    Path(path).open("w").writelines(content)
-
-
-ARTICLES = [" Sam ate lunch today.", "Sams lunch ingredients."]
-SUMMARIES = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
-T5_TINY = "patrickvonplaten/t5-tiny-random"
-BART_TINY = "sshleifer/bart-tiny-random"
-MBART_TINY = "sshleifer/tiny-mbart"
-MARIAN_TINY = "sshleifer/tiny-marian-en-de"
-
-
-stream_handler = logging.StreamHandler(sys.stdout)
-logger.addHandler(stream_handler)
-logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
-
-
-def make_test_data_dir(tmp_dir):
-    for split in ["train", "val", "test"]:
-        _dump_articles(os.path.join(tmp_dir, f"{split}.source"), ARTICLES)
-        _dump_articles(os.path.join(tmp_dir, f"{split}.target"), SUMMARIES)
-    return tmp_dir
-
-
-class TestSummarizationDistillerMultiGPU(TestCasePlus):
-    @classmethod
-    def setUpClass(cls):
-        logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
-        return cls
-
-    @require_torch_multigpu
-    def test_multigpu(self):
-
-        updates = dict(
-            no_teacher=True,
-            freeze_encoder=True,
-            gpus=2,
-            overwrite_output_dir=True,
-            sortish_sampler=True,
-        )
-        self._test_distiller_cli_fork(updates, check_contents=False)
-
-    def _test_distiller_cli_fork(self, updates, check_contents=True):
-        default_updates = dict(
-            label_smoothing=0.0,
-            early_stopping_patience=-1,
-            train_batch_size=1,
-            eval_batch_size=2,
-            max_epochs=2,
-            alpha_mlm=0.2,
-            alpha_ce=0.8,
-            do_predict=True,
-            model_name_or_path="sshleifer/tinier_bart",
-            teacher=CHEAP_ARGS["model_name_or_path"],
-            val_check_interval=0.5,
-        )
-        default_updates.update(updates)
-        args_d: dict = CHEAP_ARGS.copy()
-        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
-        output_dir = self.get_auto_remove_tmp_dir()
-        args_d.update(data_dir=tmp_dir, output_dir=output_dir, **default_updates)
-
-        def convert(k, v):
-            if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
-                return ""
-            if v is False or v is None:
-                return ""
-            if v is True:  # or len(str(v))==0:
-                return f"--{k}"
-            return f"--{k}={v}"
-
-        path = Path(__file__).resolve()
-        cur_path = path.parents[0]
-        examples_path = path.parents[1]
-        src_path = f"{path.parents[2]}/src"
-        env = os.environ.copy()
-        env["PYTHONPATH"] = f"{examples_path}:{src_path}:{env.get('PYTHONPATH', '')}"
-
-        cli_args = [x for x in (convert(k, v) for k, v in args_d.items()) if len(x)]
-        cmd = [sys.executable, f"{cur_path}/distillation.py"] + cli_args
-
-        print("\nRunning: ", " ".join(cmd))
-
-        result = execute_async_std(cmd, env=env, stdin=None, timeout=180, quiet=False, echo=False)
-
-        assert result.stdout, "produced no output"
-        if result.returncode > 0:
-            pytest.fail(f"failed with returncode {result.returncode}")
-
-        contents = os.listdir(output_dir)
-        contents = {os.path.basename(p) for p in contents}
-        ckpt_files = [p for p in contents if p.endswith("ckpt")]
-        assert len(ckpt_files) > 0
-
-        self.assertIn("test_generations.txt", contents)
-        self.assertIn("test_results.txt", contents)
-
-        # get the following from the module, (we don't have access to `model` here)
-        metrics_save_path = os.path.join(output_dir, "metrics.json")
-        val_metric = "rouge2"
-
-        metrics = load_json(metrics_save_path)
-        # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
-        print(metrics)
-        last_step_stats = metrics["val"][-1]
-        self.assertGreaterEqual(last_step_stats["val_avg_gen_time"], 0.01)
-        self.assertGreaterEqual(1.0, last_step_stats["val_avg_gen_time"])
-        self.assertIsInstance(last_step_stats[f"val_avg_{val_metric}"], float)
-        self.assertEqual(len(metrics["test"]), 1)
-        desired_n_evals = int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1)
-        self.assertEqual(len(metrics["val"]), desired_n_evals)

examples/seq2seq/test_tatoeba_conversion.py

@@ -1,22 +0,0 @@
-import tempfile
-import unittest
-
-from transformers.convert_marian_tatoeba_to_pytorch import TatoebaConverter
-from transformers.file_utils import cached_property
-from transformers.testing_utils import slow
-
-
-class TatoebaConversionTester(unittest.TestCase):
-    @cached_property
-    def resolver(self):
-        tmp_dir = tempfile.mkdtemp()
-        return TatoebaConverter(save_dir=tmp_dir)
-
-    @slow
-    def test_resolver(self):
-        self.resolver.convert_models(["heb-eng"])
-
-    @slow
-    def test_model_card(self):
-        content, mmeta = self.resolver.write_model_card("opus-mt-he-en", dry_run=True)
-        assert mmeta["long_pair"] == "heb-eng"

examples/seq2seq/utils.py

@@ -5,7 +5,6 @@ import math
 import os
 import pickle
 import socket
-import sys
 from logging import getLogger
 from pathlib import Path
 from typing import Callable, Dict, Iterable, List, Tuple, Union
@@ -458,22 +457,14 @@ def load_json(path):
 
 
 def get_git_info():
-    try:
+    repo = git.Repo(search_parent_directories=True)
-        repo = git.Repo(search_parent_directories=True)
+    repo_infos = {
-        repo_infos = {
+        "repo_id": str(repo),
-            "repo_id": str(repo),
+        "repo_sha": str(repo.head.object.hexsha),
-            "repo_sha": str(repo.head.object.hexsha),
+        "repo_branch": str(repo.active_branch),
-            "repo_branch": str(repo.active_branch),
+        "hostname": str(socket.gethostname()),
-            "hostname": str(socket.gethostname()),
+    }
-        }
+    return repo_infos
-        return repo_infos
-    except TypeError:
-        return {
-            "repo_id": None,
-            "repo_sha": None,
-            "repo_branch": None,
-            "hostname": None,
-        }
 
 
 ROUGE_KEYS = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
@@ -620,95 +611,3 @@ def chunks(lst, n):
     """Yield successive n-sized chunks from lst."""
     for i in range(0, len(lst), n):
         yield lst[i : i + n]
-
-
-def check_output_dir(args, expected_items=0):
-    """
-    Checks whether to bail out if output_dir already exists and has more than expected_items in it
-
-    `args`: needs to have the following attributes of `args`:
-      - output_dir
-      - do_train
-      - overwrite_output_dir
-
-    `expected_items`: normally 0 (default) - i.e. empty dir, but in some cases a few files are expected (e.g. recovery from OOM)
-    """
-    if (
-        os.path.exists(args.output_dir)
-        and len(os.listdir(args.output_dir)) > expected_items
-        and args.do_train
-        and not args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({args.output_dir}) already exists and "
-            "has {len(os.listdir(args.output_dir))} items in it (expected {expected_items} items). "
-            "Use --overwrite_output_dir to overcome."
-        )
-
-
-# the following code deals with async io between processes
-
-# adapted from https://stackoverflow.com/a/59041913/9201239
-import asyncio  # noqa
-
-
-class _RunOutput:
-    def __init__(self, returncode, stdout, stderr):
-        self.returncode = returncode
-        self.stdout = stdout
-        self.stderr = stderr
-
-
-async def _read_stream(stream, callback):
-    while True:
-        line = await stream.readline()
-        if line:
-            callback(line)
-        else:
-            break
-
-
-async def _stream_subprocess(cmd, env=None, stdin=None, timeout=None, quiet=False, echo=False) -> _RunOutput:
-    if echo:
-        print(cmd)
-
-    p = await asyncio.create_subprocess_exec(
-        cmd[0],
-        *cmd[1:],
-        stdin=stdin,
-        stdout=asyncio.subprocess.PIPE,
-        stderr=asyncio.subprocess.PIPE,
-        env=env,
-    )
-    out = []
-    err = []
-
-    def tee(line, sink, pipe, label=""):
-        line = line.decode("utf-8").rstrip()
-        sink.append(line)
-        if not quiet:
-            print(label, line, file=pipe)
-
-    await asyncio.wait(
-        [
-            _read_stream(p.stdout, lambda l: tee(l, out, sys.stdout)),
-            _read_stream(p.stderr, lambda l: tee(l, err, sys.stderr, label="stderr:")),
-        ],
-        timeout=timeout,
-    )
-
-    # XXX: warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
-    # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
-    #
-    # If it starts hanging, will need to switch s/wait/communicate/ - so perhaps for debug we will enable
-    # `wait` as it's easier to see in real time, but for normal runs use `communicate`
-    return _RunOutput(await p.wait(), out, err)
-
-
-def execute_async_std(cmd, env=None, stdin=None, timeout=None, quiet=False, echo=False) -> _RunOutput:
-    loop = asyncio.get_event_loop()
-    result = loop.run_until_complete(
-        _stream_subprocess(cmd, env=env, stdin=stdin, timeout=timeout, quiet=quiet, echo=echo)
-    )
-
-    return result

examples/test_examples.py

@@ -67,10 +67,10 @@ class ExamplesTests(TestCasePlus):
         testargs = f"""
             run_glue.py
             --model_name_or_path distilbert-base-uncased
+            --data_dir ./tests/fixtures/tests_samples/MRPC/
             --output_dir {tmp_dir}
             --overwrite_output_dir
-            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
+            --task_name mrpc
-            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
             --do_train
             --do_eval
             --per_device_train_batch_size=2
@@ -116,8 +116,8 @@ class ExamplesTests(TestCasePlus):
             testargs.append("--fp16")
 
         with patch.object(sys, "argv", testargs):
-            result = run_pl_glue.main()[0]
+            result = run_pl_glue.main()
-            # for now just testing that the script can run to completion
+            # for now just testing that the script can run to a completion
             self.assertGreater(result["acc"], 0.25)
             #
             # TODO: this fails on CI - doesn't get acc/f1>=0.75:

examples/test_xla_examples.py

@@ -44,7 +44,8 @@ class TorchXLAExamplesTests(unittest.TestCase):
             transformers/examples/text-classification/run_glue.py
             --do_train
             --do_eval
-            --task_name=mrpc
+            --task_name=MRPC
+            --data_dir=/datasets/glue_data/MRPC
             --cache_dir=./cache_dir
             --num_train_epochs=1
             --max_seq_length=128
@@ -58,7 +59,7 @@ class TorchXLAExamplesTests(unittest.TestCase):
             --model_name_or_path=bert-base-cased
             --per_device_train_batch_size=64
             --per_device_eval_batch_size=64
-            --evaluation_strategy steps
+            --evaluate_during_training
             --overwrite_cache
             """.split()
         with patch.object(sys, "argv", testargs):
@@ -79,15 +80,4 @@ class TorchXLAExamplesTests(unittest.TestCase):
                 self.assertGreaterEqual(value, 0.70)
 
             # Assert that the script takes less than 300 seconds to make sure it doesn't hang.
-            self.assertLess(end - start, 500)
+            self.assertLess(end - start, 300)
-
-    def test_trainer_tpu(self):
-        import xla_spawn
-
-        testargs = """
-            transformers/tests/test_trainer_tpu.py
-            --num_cores=8
-            transformers/tests/test_trainer_tpu.py
-            """.split()
-        with patch.object(sys, "argv", testargs):
-            xla_spawn.main()

examples/text-classification/README.md

@@ -43,7 +43,7 @@ python run_tf_text_classification.py \
   --do_eval \
   --do_predict \
   --logging_steps 10 \
-  --evaluation_strategy steps \
+  --evaluate_during_training \
   --save_steps 10 \
   --overwrite_output_dir \
   --max_seq_length 128
@@ -74,10 +74,18 @@ between different runs. We report the median on 5 runs (with different seeds) fo
 | WNLI  | Accuracy                     | 45.07       |
 
 Some of these results are significantly different from the ones reported on the test set
-of GLUE benchmark on the website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the
+of GLUE benchmark on the website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the webite.
-website.
+
+Before running any one of these GLUE tasks you should download the
+[GLUE data](https://gluebenchmark.com/tasks) by running the following lines at the root of the repo
+```
+python utils/download_glue_data.py --data_dir /path/to/glue --tasks all
+```
+
+after replacing *path/to/glue* with a value that you like. Then you can run
 
 ```bash
+export GLUE_DIR=/path/to/glue
 export TASK_NAME=MRPC
 
 python run_glue.py \
@@ -85,6 +93,7 @@ python run_glue.py \
   --task_name $TASK_NAME \
   --do_train \
   --do_eval \
+  --data_dir $GLUE_DIR/$TASK_NAME \
   --max_seq_length 128 \
   --per_device_train_batch_size 32 \
   --learning_rate 2e-5 \
@@ -105,33 +114,69 @@ since the data processor for each task inherits from the base class DataProcesso
 
 ## Running on TPUs in PyTorch
 
-Even when running PyTorch, you can accelerate your workloads on Google's TPUs, using `pytorch/xla`. For information on
+**Update**: read the more up-to-date [Running on TPUs](../README.md#running-on-tpus) in the main README.md instead.
-how to setup your TPU environment refer to the
+
+Even when running PyTorch, you can accelerate your workloads on Google's TPUs, using `pytorch/xla`. For information on how to setup your TPU environment refer to the
 [pytorch/xla README](https://github.com/pytorch/xla/blob/master/README.md).
 
-For running your GLUE task on MNLI dataset you can run something like the following form the root of the transformers
+The following are some examples of running the `*_tpu.py` finetuning scripts on TPUs. All steps for data preparation are
-repo:
+identical to your normal GPU + Huggingface setup.
+
+For running your GLUE task on MNLI dataset you can run something like the following:
 
 ```
-python examples/xla_spawn.py \
+export XRT_TPU_CONFIG="tpu_worker;0;$TPU_IP_ADDRESS:8470"
-  --num_cores=8 \
+export GLUE_DIR=/path/to/glue
-  transformers/examples/text-classification/run_glue.py \
+export TASK_NAME=MNLI
+
+python run_glue_tpu.py \
+  --model_name_or_path bert-base-cased \
+  --task_name $TASK_NAME \
   --do_train \
   --do_eval \
-  --task_name=mrpc \
+  --data_dir $GLUE_DIR/$TASK_NAME \
-  --num_train_epochs=3 \
+  --max_seq_length 128 \
-  --max_seq_length=128 \
+  --train_batch_size 32 \
-  --learning_rate=5e-5 \
+  --learning_rate 3e-5 \
-  --output_dir=/tmp/mrpc \
+  --num_train_epochs 3.0 \
+  --output_dir /tmp/$TASK_NAME \
   --overwrite_output_dir \
-  --logging_steps=5 \
+  --logging_steps 50 \
-  --save_steps=5 \
+  --save_steps 200 \
-  --tpu_metrics_debug \
+  --num_cores=8
-  --model_name_or_path=bert-base-cased \
-  --per_device_train_batch_size=64 \
-  --per_device_eval_batch_size=64
 ```
 
+### MRPC
+
+#### Fine-tuning example
+
+The following examples fine-tune BERT on the Microsoft Research Paraphrase Corpus (MRPC) corpus and runs in less
+than 10 minutes on a single K-80 and in 27 seconds (!) on single tesla V100 16GB with apex installed.
+
+Before running any one of these GLUE tasks you should download the
+[GLUE data](https://gluebenchmark.com/tasks) by running
+[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
+and unpack it to some directory `$GLUE_DIR`.
+
+```bash
+export GLUE_DIR=/path/to/glue
+
+python run_glue.py \
+  --model_name_or_path bert-base-cased \
+  --task_name MRPC \
+  --do_train \
+  --do_eval \
+  --data_dir $GLUE_DIR/MRPC/ \
+  --max_seq_length 128 \
+  --per_device_train_batch_size 32 \
+  --learning_rate 2e-5 \
+  --num_train_epochs 3.0 \
+  --output_dir /tmp/mrpc_output/
+```
+
+Our test ran on a few seeds with [the original implementation hyper-
+parameters](https://github.com/google-research/bert#sentence-and-sentence-pair-classification-tasks) gave evaluation
+results between 84% and 88%.
 
 #### Using Apex and mixed-precision
 
@@ -139,12 +184,14 @@ Using Apex and 16 bit precision, the fine-tuning on MRPC only takes 27 seconds.
 [apex](https://github.com/NVIDIA/apex), then run the following example:
 
 ```bash
+export GLUE_DIR=/path/to/glue
 
 python run_glue.py \
   --model_name_or_path bert-base-cased \
   --task_name MRPC \
   --do_train \
   --do_eval \
+  --data_dir $GLUE_DIR/MRPC/ \
   --max_seq_length 128 \
   --per_device_train_batch_size 32 \
   --learning_rate 2e-5 \
@@ -159,13 +206,15 @@ Here is an example using distributed training on 8 V100 GPUs. The model used is
 reaches F1 > 92 on MRPC.
 
 ```bash
+export GLUE_DIR=/path/to/glue
 
 python -m torch.distributed.launch \
     --nproc_per_node 8 run_glue.py \
     --model_name_or_path bert-base-cased \
-    --task_name mrpc \
+    --task_name MRPC \
     --do_train \
     --do_eval \
+    --data_dir $GLUE_DIR/MRPC/ \
     --max_seq_length 128 \
     --per_device_train_batch_size 8 \
     --learning_rate 2e-5 \
@@ -197,6 +246,7 @@ python -m torch.distributed.launch \
     --task_name mnli \
     --do_train \
     --do_eval \
+    --data_dir $GLUE_DIR/MNLI/ \
     --max_seq_length 128 \
     --per_device_train_batch_size 8 \
     --learning_rate 2e-5 \
@@ -222,9 +272,7 @@ The results  are the following:
 
 # Run PyTorch version using PyTorch-Lightning
 
-Run `bash run_pl.sh` from the `glue` directory. This will also install `pytorch-lightning` and the requirements in
+Run `bash run_pl.sh` from the `glue` directory. This will also install `pytorch-lightning` and the requirements in `examples/requirements.txt`. It is a shell pipeline that will automatically download, pre-process the data and run the specified models. Logs are saved in `lightning_logs` directory.
-`examples/requirements.txt`. It is a shell pipeline that will automatically download, preprocess the data and run the
-specified models. Logs are saved in `lightning_logs` directory.
 
 Pass `--gpus` flag to change the number of GPUs. Default uses 1. At the end, the expected results are:
 

examples/text-classification/run_glue.py

@@ -14,101 +14,33 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ Finetuning the library models for sequence classification on GLUE."""
-# You can also adapt this script on your own text classification task. Pointers for this are left as comments.
 
+
+import dataclasses
 import logging
 import os
-import random
 import sys
 from dataclasses import dataclass, field
-from typing import Optional
+from typing import Callable, Dict, Optional
 
 import numpy as np
-from datasets import load_dataset, load_metric
 
-import transformers
+from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, EvalPrediction, GlueDataset
+from transformers import GlueDataTrainingArguments as DataTrainingArguments
 from transformers import (
-    AutoConfig,
-    AutoModelForSequenceClassification,
-    AutoTokenizer,
-    EvalPrediction,
     HfArgumentParser,
-    PretrainedConfig,
     Trainer,
     TrainingArguments,
-    default_data_collator,
+    glue_compute_metrics,
+    glue_output_modes,
+    glue_tasks_num_labels,
     set_seed,
 )
-from transformers.trainer_utils import is_main_process
 
 
-task_to_keys = {
-    "cola": ("sentence", None),
-    "mnli": ("premise", "hypothesis"),
-    "mrpc": ("sentence1", "sentence2"),
-    "qnli": ("question", "sentence"),
-    "qqp": ("question1", "question2"),
-    "rte": ("sentence1", "sentence2"),
-    "sst2": ("sentence", None),
-    "stsb": ("sentence1", "sentence2"),
-    "wnli": ("sentence1", "sentence2"),
-}
-
 logger = logging.getLogger(__name__)
 
 
-@dataclass
-class DataTrainingArguments:
-    """
-    Arguments pertaining to what data we are going to input our model for training and eval.
-
-    Using `HfArgumentParser` we can turn this class
-    into argparse arguments to be able to specify them on
-    the command line.
-    """
-
-    task_name: Optional[str] = field(
-        default=None,
-        metadata={"help": "The name of the task to train on: " + ", ".join(task_to_keys.keys())},
-    )
-    max_seq_length: int = field(
-        default=128,
-        metadata={
-            "help": "The maximum total input sequence length after tokenization. Sequences longer "
-            "than this will be truncated, sequences shorter will be padded."
-        },
-    )
-    overwrite_cache: bool = field(
-        default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
-    )
-    pad_to_max_length: bool = field(
-        default=True,
-        metadata={
-            "help": "Whether to pad all samples to `max_seq_length`. "
-            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
-        },
-    )
-    train_file: Optional[str] = field(
-        default=None, metadata={"help": "A csv or a json file containing the training data."}
-    )
-    validation_file: Optional[str] = field(
-        default=None, metadata={"help": "A csv or a json file containing the validation data."}
-    )
-
-    def __post_init__(self):
-        if self.task_name is not None:
-            self.task_name = self.task_name.lower()
-            if self.task_name not in task_to_keys.keys():
-                raise ValueError("Unknown task, you should pick one in " + ",".join(task_to_keys.keys()))
-        elif self.train_file is None or self.validation_file is None:
-            raise ValueError("Need either a GLUE task or a training/validation file.")
-        else:
-            extension = self.train_file.split(".")[-1]
-            assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
-            extension = self.validation_file.split(".")[-1]
-            assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
-
-
 @dataclass
 class ModelArguments:
     """
@@ -127,10 +59,6 @@ class ModelArguments:
     cache_dir: Optional[str] = field(
         default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
     )
-    use_fast_tokenizer: bool = field(
-        default=True,
-        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
-    )
 
 
 def main():
@@ -139,6 +67,7 @@ def main():
     # 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.
@@ -153,82 +82,40 @@ def main():
         and not training_args.overwrite_output_dir
     ):
         raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
-            "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 is_main_process(training_args.local_rank) else logging.WARN,
+        level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
     )
-
-    # Log on each process the small summary:
     logger.warning(
-        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
-        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+        training_args.local_rank,
+        training_args.device,
+        training_args.n_gpu,
+        bool(training_args.local_rank != -1),
+        training_args.fp16,
     )
-    # Set the verbosity to info of the Transformers logger (on main process only):
+    logger.info("Training/evaluation parameters %s", training_args)
-    if is_main_process(training_args.local_rank):
-        transformers.utils.logging.set_verbosity_info()
-    logger.info(f"Training/evaluation parameters {training_args}")
 
-    # Set seed before initializing model.
+    # Set seed
     set_seed(training_args.seed)
 
-    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
+    try:
-    # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub
+        num_labels = glue_tasks_num_labels[data_args.task_name]
-    #
+        output_mode = glue_output_modes[data_args.task_name]
-    # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
+    except KeyError:
-    # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
+        raise ValueError("Task not found: %s" % (data_args.task_name))
-    # label if at least two columns are provided.
-    #
-    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
-    # single column. You can easily tweak this behavior (see below)
-    #
-    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
-    # download the dataset.
-    if data_args.task_name is not None:
-        # Downloading and loading a dataset from the hub.
-        datasets = load_dataset("glue", data_args.task_name)
-    elif data_args.train_file.endswith(".csv"):
-        # Loading a dataset from local csv files
-        datasets = load_dataset(
-            "csv", data_files={"train": data_args.train_file, "validation": data_args.validation_file}
-        )
-    else:
-        # Loading a dataset from local json files
-        datasets = load_dataset(
-            "json", data_files={"train": data_args.train_file, "validation": data_args.validation_file}
-        )
-    # See more about loading any type of standard or custom dataset at
-    # https://huggingface.co/docs/datasets/loading_datasets.html.
-
-    # Labels
-    if data_args.task_name is not None:
-        is_regression = data_args.task_name == "stsb"
-        if not is_regression:
-            label_list = datasets["train"].features["label"].names
-            num_labels = len(label_list)
-        else:
-            num_labels = 1
-    else:
-        # Trying to have good defaults here, don't hesitate to tweak to your needs.
-        is_regression = datasets["train"].features["label"].dtype in ["float32", "float64"]
-        if is_regression:
-            num_labels = 1
-        else:
-            # A useful fast method:
-            # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
-            label_list = datasets["train"].unique("label")
-            label_list.sort()  # Let's sort it for determinism
-            num_labels = len(label_list)
 
     # Load pretrained model and tokenizer
     #
-    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
+
     config = AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path,
         num_labels=num_labels,
@@ -238,7 +125,6 @@ def main():
     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,
-        use_fast=model_args.use_fast_tokenizer,
     )
     model = AutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path,
@@ -247,103 +133,39 @@ def main():
         cache_dir=model_args.cache_dir,
     )
 
-    # Preprocessing the datasets
+    # Get datasets
-    if data_args.task_name is not None:
+    train_dataset = (
-        sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
+        GlueDataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None
-    else:
+    )
-        # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
+    eval_dataset = (
-        non_label_column_names = [name for name in datasets["train"].column_names if name != "label"]
+        GlueDataset(data_args, tokenizer=tokenizer, mode="dev", cache_dir=model_args.cache_dir)
-        if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
+        if training_args.do_eval
-            sentence1_key, sentence2_key = "sentence1", "sentence2"
+        else None
-        else:
+    )
-            if len(non_label_column_names) >= 2:
+    test_dataset = (
-                sentence1_key, sentence2_key = non_label_column_names[:2]
+        GlueDataset(data_args, tokenizer=tokenizer, mode="test", cache_dir=model_args.cache_dir)
-            else:
+        if training_args.do_predict
-                sentence1_key, sentence2_key = non_label_column_names[0], None
+        else None
+    )
 
-    # Padding strategy
+    def build_compute_metrics_fn(task_name: str) -> Callable[[EvalPrediction], Dict]:
-    if data_args.pad_to_max_length:
+        def compute_metrics_fn(p: EvalPrediction):
-        padding = "max_length"
+            preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
-        max_length = data_args.max_seq_length
+            if output_mode == "classification":
-    else:
+                preds = np.argmax(preds, axis=1)
-        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
+            else:  # regression
-        padding = False
+                preds = np.squeeze(preds)
-        max_length = None
+            return glue_compute_metrics(task_name, preds, p.label_ids)
 
-    # Some models have set the order of the labels to use, so let's make sure we do use it.
+        return compute_metrics_fn
-    label_to_id = None
-    if (
-        model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
-        and data_args.task_name is not None
-        and is_regression
-    ):
-        # Some have all caps in their config, some don't.
-        label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
-        if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
-            label_to_id = {i: label_name_to_id[label_list[i]] for i in range(num_labels)}
-        else:
-            logger.warn(
-                "Your model seems to have been trained with labels, but they don't match the dataset: ",
-                f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
-                "\nIgnoring the model labels as a result.",
-            )
-    elif data_args.task_name is None:
-        label_to_id = {v: i for i, v in enumerate(label_list)}
-
-    def preprocess_function(examples):
-        # Tokenize the texts
-        args = (
-            (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
-        )
-        result = tokenizer(*args, padding=padding, max_length=max_length, truncation=True)
-
-        # Map labels to IDs (not necessary for GLUE tasks)
-        if label_to_id is not None and "label" in examples:
-            result["label"] = [label_to_id[l] for l in examples["label"]]
-        return result
-
-    datasets = datasets.map(preprocess_function, batched=True, load_from_cache_file=not data_args.overwrite_cache)
-
-    train_dataset = datasets["train"]
-    eval_dataset = datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
-    if data_args.task_name is not None:
-        test_dataset = datasets["test_matched" if data_args.task_name == "mnli" else "test"]
-
-    # Log a few random samples from the training set:
-    for index in random.sample(range(len(train_dataset)), 3):
-        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
-
-    # Get the metric function
-    if data_args.task_name is not None:
-        metric = load_metric("glue", data_args.task_name)
-    # TODO: When datasets metrics include regular accuracy, make an else here and remove special branch from
-    # compute_metrics
-
-    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
-    # predictions and label_ids field) and has to return a dictionary string to float.
-    def compute_metrics(p: EvalPrediction):
-        preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
-        preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
-        if data_args.task_name is not None:
-            result = metric.compute(predictions=preds, references=p.label_ids)
-            if len(result) > 1:
-                result["combined_score"] = np.mean(list(result.values())).item()
-            return result
-        elif is_regression:
-            return {"mse": ((preds - p.label_ids) ** 2).mean().item()}
-        else:
-            return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()}
 
     # Initialize our Trainer
     trainer = Trainer(
         model=model,
         args=training_args,
         train_dataset=train_dataset,
-        eval_dataset=eval_dataset if training_args.do_eval else None,
+        eval_dataset=eval_dataset,
-        compute_metrics=compute_metrics,
+        compute_metrics=build_compute_metrics_fn(data_args.task_name),
-        tokenizer=tokenizer,
-        # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
-        data_collator=default_data_collator if data_args.pad_to_max_length else None,
     )
 
     # Training
@@ -351,7 +173,11 @@ def main():
         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()  # Saves the tokenizer too for easy upload
+        trainer.save_model()
+        # For convenience, we also re-save the tokenizer to the same directory,
+        # so that you can share your model easily on huggingface.co/models =)
+        if trainer.is_world_master():
+            tokenizer.save_pretrained(training_args.output_dir)
 
     # Evaluation
     eval_results = {}
@@ -359,52 +185,56 @@ def main():
         logger.info("*** Evaluate ***")
 
         # Loop to handle MNLI double evaluation (matched, mis-matched)
-        tasks = [data_args.task_name]
         eval_datasets = [eval_dataset]
         if data_args.task_name == "mnli":
-            tasks.append("mnli-mm")
+            mnli_mm_data_args = dataclasses.replace(data_args, task_name="mnli-mm")
-            eval_datasets.append(datasets["validation_mismatched"])
+            eval_datasets.append(
+                GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, mode="dev", cache_dir=model_args.cache_dir)
+            )
 
-        for eval_dataset, task in zip(eval_datasets, tasks):
+        for eval_dataset in eval_datasets:
+            trainer.compute_metrics = build_compute_metrics_fn(eval_dataset.args.task_name)
             eval_result = trainer.evaluate(eval_dataset=eval_dataset)
 
-            output_eval_file = os.path.join(training_args.output_dir, f"eval_results_{task}.txt")
+            output_eval_file = os.path.join(
-            if trainer.is_world_process_zero():
+                training_args.output_dir, f"eval_results_{eval_dataset.args.task_name}.txt"
+            )
+            if trainer.is_world_master():
                 with open(output_eval_file, "w") as writer:
-                    logger.info(f"***** Eval results {task} *****")
+                    logger.info("***** Eval results {} *****".format(eval_dataset.args.task_name))
                     for key, value in eval_result.items():
-                        logger.info(f"  {key} = {value}")
+                        logger.info("  %s = %s", key, value)
-                        writer.write(f"{key} = {value}\n")
+                        writer.write("%s = %s\n" % (key, value))
 
             eval_results.update(eval_result)
 
     if training_args.do_predict:
-        logger.info("*** Test ***")
+        logging.info("*** Test ***")
-
-        # Loop to handle MNLI double evaluation (matched, mis-matched)
-        tasks = [data_args.task_name]
         test_datasets = [test_dataset]
         if data_args.task_name == "mnli":
-            tasks.append("mnli-mm")
+            mnli_mm_data_args = dataclasses.replace(data_args, task_name="mnli-mm")
-            test_datasets.append(datasets["test_mismatched"])
+            test_datasets.append(
+                GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, mode="test", cache_dir=model_args.cache_dir)
+            )
 
-        for test_dataset, task in zip(test_datasets, tasks):
+        for test_dataset in test_datasets:
-            # Removing the `label` columns because it contains -1 and Trainer won't like that.
-            test_dataset.remove_columns_("label")
             predictions = trainer.predict(test_dataset=test_dataset).predictions
-            predictions = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1)
+            if output_mode == "classification":
+                predictions = np.argmax(predictions, axis=1)
 
-            output_test_file = os.path.join(training_args.output_dir, f"test_results_{task}.txt")
+            output_test_file = os.path.join(
-            if trainer.is_world_process_zero():
+                training_args.output_dir, f"test_results_{test_dataset.args.task_name}.txt"
+            )
+            if trainer.is_world_master():
                 with open(output_test_file, "w") as writer:
-                    logger.info(f"***** Test results {task} *****")
+                    logger.info("***** Test results {} *****".format(test_dataset.args.task_name))
                     writer.write("index\tprediction\n")
                     for index, item in enumerate(predictions):
-                        if is_regression:
+                        if output_mode == "regression":
-                            writer.write(f"{index}\t{item:3.3f}\n")
+                            writer.write("%d\t%3.3f\n" % (index, item))
                         else:
-                            item = label_list[item]
+                            item = test_dataset.get_labels()[item]
-                            writer.write(f"{index}\t{item}\n")
+                            writer.write("%d\t%s\n" % (index, item))
     return eval_results
 
 

examples/text-classification/run_tf_text_classification.py

@@ -60,7 +60,7 @@ def get_tfds(
         for k in files.keys():
             transformed_ds[k] = ds[k].map(
                 lambda example: tokenizer.batch_encode_plus(
-                    (example[features_name[0]], example[features_name[1]]),
+                    (example[features_name[0]], features_name[1]),
                     truncation=True,
                     max_length=max_seq_length,
                     padding="max_length",
@@ -96,9 +96,6 @@ def get_tfds(
         else None
     )
 
-    if train_ds is not None:
-        train_ds = train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))
-
     val_ds = (
         tf.data.Dataset.from_generator(
             gen_val,
@@ -109,9 +106,6 @@ def get_tfds(
         else None
     )
 
-    if val_ds is not None:
-        val_ds = val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION])))
-
     test_ds = (
         tf.data.Dataset.from_generator(
             gen_test,
@@ -122,9 +116,6 @@ def get_tfds(
         else None
     )
 
-    if test_ds is not None:
-        test_ds = test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST])))
-
     return train_ds, val_ds, test_ds, label2id
 
 

model_cards/DJSammy/bert-base-danish-uncased_BotXO,ai/README.md

@@ -1,143 +0,0 @@
----
-language: da
-tags:
-- bert
-- masked-lm
-- lm-head
-license: cc-by-4.0
-datasets:
-- common_crawl
-- wikipedia
-pipeline_tag: fill-mask
-widget:
-- text: "København er [MASK] i Danmark."
----
-
-# Danish BERT (uncased) model 
-
-[BotXO.ai](https://www.botxo.ai/) developed this model. For data and training details see their [GitHub repository](https://github.com/botxo/nordic_bert).  
-
-The original model was trained in TensorFlow then I converted it to Pytorch using [transformers-cli](https://huggingface.co/transformers/converting_tensorflow_models.html?highlight=cli).
-
-For TensorFlow version download here: https://www.dropbox.com/s/19cjaoqvv2jicq9/danish_bert_uncased_v2.zip?dl=1
-
-
-## Architecture
-
-```python
-from transformers import AutoModelForPreTraining
-
-model = AutoModelForPreTraining.from_pretrained("DJSammy/bert-base-danish-uncased_BotXO,ai")
-
-params = list(model.named_parameters())
-print('danish_bert_uncased_v2 has {:} different named parameters.\n'.format(len(params)))
-
-print('==== Embedding Layer ====\n')
-for p in params[0:5]:
-    print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
-
-print('\n==== First Transformer ====\n')
-for p in params[5:21]:
-    print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
-
-print('\n==== Last Transformer ====\n')
-for p in params[181:197]:
-    print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
-
-print('\n==== Output Layer ====\n')
-for p in params[197:]:
-    print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
-
-# danish_bert_uncased_v2 has 206 different named parameters.
-
-# ==== Embedding Layer ====
-
-# bert.embeddings.word_embeddings.weight                  (32000, 768)
-# bert.embeddings.position_embeddings.weight                (512, 768)
-# bert.embeddings.token_type_embeddings.weight                (2, 768)
-# bert.embeddings.LayerNorm.weight                              (768,)
-# bert.embeddings.LayerNorm.bias                                (768,)
-
-# ==== First Transformer ====
-
-# bert.encoder.layer.0.attention.self.query.weight          (768, 768)
-# bert.encoder.layer.0.attention.self.query.bias                (768,)
-# bert.encoder.layer.0.attention.self.key.weight            (768, 768)
-# bert.encoder.layer.0.attention.self.key.bias                  (768,)
-# bert.encoder.layer.0.attention.self.value.weight          (768, 768)
-# bert.encoder.layer.0.attention.self.value.bias                (768,)
-# bert.encoder.layer.0.attention.output.dense.weight        (768, 768)
-# bert.encoder.layer.0.attention.output.dense.bias              (768,)
-# bert.encoder.layer.0.attention.output.LayerNorm.weight        (768,)
-# bert.encoder.layer.0.attention.output.LayerNorm.bias          (768,)
-# bert.encoder.layer.0.intermediate.dense.weight           (3072, 768)
-# bert.encoder.layer.0.intermediate.dense.bias                 (3072,)
-# bert.encoder.layer.0.output.dense.weight                 (768, 3072)
-# bert.encoder.layer.0.output.dense.bias                        (768,)
-# bert.encoder.layer.0.output.LayerNorm.weight                  (768,)
-# bert.encoder.layer.0.output.LayerNorm.bias                    (768,)
-
-# ==== Last Transformer ====
-
-# bert.encoder.layer.11.attention.self.query.weight         (768, 768)
-# bert.encoder.layer.11.attention.self.query.bias               (768,)
-# bert.encoder.layer.11.attention.self.key.weight           (768, 768)
-# bert.encoder.layer.11.attention.self.key.bias                 (768,)
-# bert.encoder.layer.11.attention.self.value.weight         (768, 768)
-# bert.encoder.layer.11.attention.self.value.bias               (768,)
-# bert.encoder.layer.11.attention.output.dense.weight       (768, 768)
-# bert.encoder.layer.11.attention.output.dense.bias             (768,)
-# bert.encoder.layer.11.attention.output.LayerNorm.weight       (768,)
-# bert.encoder.layer.11.attention.output.LayerNorm.bias         (768,)
-# bert.encoder.layer.11.intermediate.dense.weight          (3072, 768)
-# bert.encoder.layer.11.intermediate.dense.bias                (3072,)
-# bert.encoder.layer.11.output.dense.weight                (768, 3072)
-# bert.encoder.layer.11.output.dense.bias                       (768,)
-# bert.encoder.layer.11.output.LayerNorm.weight                 (768,)
-# bert.encoder.layer.11.output.LayerNorm.bias                   (768,)
-
-# ==== Output Layer ====
-
-# bert.pooler.dense.weight                                  (768, 768)
-# bert.pooler.dense.bias                                        (768,)
-# cls.predictions.bias                                        (32000,)
-# cls.predictions.transform.dense.weight                    (768, 768)
-# cls.predictions.transform.dense.bias                          (768,)
-# cls.predictions.transform.LayerNorm.weight                    (768,)
-# cls.predictions.transform.LayerNorm.bias                      (768,)
-# cls.seq_relationship.weight                                 (2, 768)
-# cls.seq_relationship.bias                                       (2,)
-```
-
-## Example Pipeline
-
-```python
-from transformers import pipeline
-unmasker = pipeline('fill-mask', model='DJSammy/bert-base-danish-uncased_BotXO,ai')
-
-unmasker('København er [MASK] i Danmark.')
-
-# Copenhagen is the [MASK] of Denmark.
-# =>
-
-# [{'score': 0.788068950176239,
-#  'sequence': '[CLS] københavn er hovedstad i danmark. [SEP]',
-#  'token': 12610,
-#  'token_str': 'hovedstad'},
-# {'score': 0.07606703042984009,
-#  'sequence': '[CLS] københavn er hovedstaden i danmark. [SEP]',
-#  'token': 8108,
-#  'token_str': 'hovedstaden'},
-# {'score': 0.04299738258123398,
-#  'sequence': '[CLS] københavn er metropol i danmark. [SEP]',
-#  'token': 23305,
-#  'token_str': 'metropol'},
-# {'score': 0.008163209073245525,
-#  'sequence': '[CLS] københavn er ikke i danmark. [SEP]',
-#  'token': 89,
-#  'token_str': 'ikke'},
-# {'score': 0.006238455418497324,
-#  'sequence': '[CLS] københavn er ogsa i danmark. [SEP]',
-#  'token': 25253,
-#  'token_str': 'ogsa'}]
-```

model_cards/Michau/t5-base-en-generate-headline/README.md

@@ -1,47 +0,0 @@
-## About the model
-
-The model has been trained on a collection of 500k articles with headings. Its purpose is to create a one-line heading suitable for the given article.
-
-Sample code with a WikiNews article:
-
-```python
-import torch
-from transformers import T5ForConditionalGeneration,T5Tokenizer
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-model = T5ForConditionalGeneration.from_pretrained("Michau/t5-base-en-generate-headline")
-tokenizer = T5Tokenizer.from_pretrained("Michau/t5-base-en-generate-headline")
-model = model.to(device)
-
-article = '''
-Very early yesterday morning, the United States President Donald Trump reported he and his wife First Lady Melania Trump tested positive for COVID-19. Officials said the Trumps' 14-year-old son Barron tested negative as did First Family and Senior Advisors Jared Kushner and Ivanka Trump.
-Trump took to social media, posting at 12:54 am local time (0454 UTC) on Twitter, "Tonight, [Melania] and I tested positive for COVID-19. We will begin our quarantine and recovery process immediately. We will get through this TOGETHER!" Yesterday afternoon Marine One landed on the White House's South Lawn flying Trump to Walter Reed National Military Medical Center (WRNMMC) in Bethesda, Maryland.
-Reports said both were showing "mild symptoms". Senior administration officials were tested as people were informed of the positive test. Senior advisor Hope Hicks had tested positive on Thursday.
-Presidential physician Sean Conley issued a statement saying Trump has been given zinc, vitamin D, Pepcid and a daily Aspirin. Conley also gave a single dose of the experimental polyclonal antibodies drug from Regeneron Pharmaceuticals.
-According to official statements, Trump, now operating from the WRNMMC, is to continue performing his duties as president during a 14-day quarantine. In the event of Trump becoming incapacitated, Vice President Mike Pence could take over the duties of president via the 25th Amendment of the US Constitution. The Pence family all tested negative as of yesterday and there were no changes regarding Pence's campaign events.
-'''
-
-text =  "headline: " + article
-
-max_len = 256
-
-encoding = tokenizer.encode_plus(text, return_tensors = "pt")
-input_ids = encoding["input_ids"].to(device)
-attention_masks = encoding["attention_mask"].to(device)
-
-beam_outputs = model.generate(
-    input_ids = input_ids,
-    attention_mask = attention_masks,
-    max_length = 64,
-    num_beams = 3,
-    early_stopping = True,
-)
-
-result = tokenizer.decode(beam_outputs[0])
-print(result)
-```
-
-Result:
-
-```Trump and First Lady Melania Test Positive for COVID-19```

model_cards/Rostlab/prot_t5_xl_bfd/README.md

@@ -1,125 +0,0 @@
----
-language: protein
-tags:
-- protein language model
-datasets:
-- BFD
----
-
-# ProtT5-XL-BFD model
-
-Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
-[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
-[this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino acids: it only works with capital letter amino acids.
-
-
-## Model description
-
-ProtT5-XL-BFD is based on the `t5-3b` model and was pretrained on a large corpus of protein sequences in a self-supervised fashion.
-This means it was pretrained on the raw protein sequences 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 protein sequences.
-
-One important difference between this T5 model and the original T5 version is the denosing objective.
-The original T5-3B model was pretrained using a span denosing objective, while this model was pre-trained with a Bart-like MLM denosing objective.
-The masking probability is consistent with the original T5 training by randomly masking 15% of the amino acids in the input.
-
-It has been shown that the features extracted from this self-supervised model (LM-embeddings) captured important biophysical properties governing protein shape.
-shape.
-This implied learning some of the grammar of the language of life realized in protein sequences.
-
-## Intended uses & limitations
-
-The model could be used for protein feature extraction or to be fine-tuned on downstream tasks.
-We have noticed in some tasks on can gain more accuracy by fine-tuning the model rather than using it as a feature extractor.
-We have also noticed that for feature extraction, its better to use the feature extracted from the encoder not from the decoder.
-
-### How to use
-
-Here is how to use this model to extract the features of a given protein sequence in PyTorch:
-
-```python
-from transformers import T5Tokenizer, T5Model
-import re
-import torch
-
-tokenizer = T5Tokenizer.from_pretrained('Rostlab/prot_t5_xl_bfd', do_lower_case=False)
-
-model = T5Model.from_pretrained("Rostlab/prot_t5_xl_bfd")
-
-sequences_Example = ["A E T C Z A O","S K T Z P"]
-
-sequences_Example = [re.sub(r"[UZOB]", "X", sequence) for sequence in sequences_Example]
-
-ids = tokenizer.batch_encode_plus(sequences_Example, add_special_tokens=True, padding=True)
-
-input_ids = torch.tensor(ids['input_ids'])
-attention_mask = torch.tensor(ids['attention_mask'])
-
-with torch.no_grad():
-    embedding = model(input_ids=input_ids,attention_mask=attention_mask,decoder_input_ids=None)
-
-# For feature extraction we recommend to use the encoder embedding
-encoder_embedding = embedding[2].cpu().numpy()
-decoder_embedding = embedding[0].cpu().numpy()
-```
-
-## Training data
-
-The ProtT5-XL-BFD model was pretrained on [BFD](https://bfd.mmseqs.com/), a dataset consisting of 2.1 billion protein sequences.
-
-## Training procedure
-
-### Preprocessing
-
-The protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21. The rare amino acids "U,Z,O,B" were mapped to "X".
-The inputs of the model are then of the form:
-
-```
-Protein Sequence [EOS]
-```
-
-The preprocessing step was performed on the fly, by cutting and padding the protein sequences up to 512 tokens.
-
-The details of the masking procedure for each sequence are as follows:
-- 15% of the amino acids are masked.
-- In 90% of the cases, the masked amino acids are replaced by `[MASK]` token.
-- In 10% of the cases, the masked amino acids are replaced by a random amino acid (different) from the one they replace.
-
-### Pretraining
-
-The model was trained on a single TPU Pod V3-1024 for 1.2 million steps in total, using sequence length 512 (batch size 4k).
-It has a total of approximately 3B parameters and was trained using the encoder-decoder architecture.
-The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
-
-
-## Evaluation results
-
-When the model is used for feature etraction, this model achieves the following results:
-
-Test results :
-
-| Task/Dataset | secondary structure (3-states) | secondary structure (8-states)  |  Localization | Membrane  |
-|:-----:|:-----:|:-----:|:-----:|:-----:|
-|   CASP12  | 77 | 66 |    |    |
-|   TS115   | 85 | 74 |    |    | 
-|   CB513   | 84 | 71 |    |    |
-|  DeepLoc  |    |    | 77 | 91 |
-
-### BibTeX entry and citation info
-
-```bibtex
-@article {Elnaggar2020.07.12.199554,
-	author = {Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and BHOWMIK, DEBSINDHU and Rost, Burkhard},
-	title = {ProtTrans: Towards Cracking the Language of Life{\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing},
-	elocation-id = {2020.07.12.199554},
-	year = {2020},
-	doi = {10.1101/2020.07.12.199554},
-	publisher = {Cold Spring Harbor Laboratory},
-	abstract = {Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112 times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8 states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. Availability ProtTrans: \<a href="https://github.com/agemagician/ProtTrans"\>https://github.com/agemagician/ProtTrans\</a\>Competing Interest StatementThe authors have declared no competing interest.},
-	URL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554},
-	eprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf},
-	journal = {bioRxiv}
-}
-```
-
-> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)

model_cards/T-Systems-onsite/bert-german-dbmdz-uncased-sentence-stsb/README.md

@@ -4,4 +4,44 @@ license: mit
 ---
 
 # bert-german-dbmdz-uncased-sentence-stsb
-**This model is outdated! Please use this improved version: <https://huggingface.co/T-Systems-onsite/german-roberta-sentence-transformer-v2>**
+
+## How to use
+**The usage description above - provided by Hugging Face - is wrong! Please use this:**
+
+Install the `sentence-transformers` package. See here: <https://github.com/UKPLab/sentence-transformers>
+```python
+from sentence_transformers import models
+from sentence_transformers import SentenceTransformer
+
+# load BERT model from Hugging Face
+word_embedding_model = models.Transformer(
+    'T-Systems-onsite/bert-german-dbmdz-uncased-sentence-stsb')
+
+# Apply mean pooling to get one fixed sized sentence vector
+pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),
+                               pooling_mode_mean_tokens=True,
+                               pooling_mode_cls_token=False,
+                               pooling_mode_max_tokens=False)
+
+# join BERT model and pooling to get the sentence transformer
+model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
+```
+
+## Model description
+This is a German [sentence embedding](https://github.com/UKPLab/sentence-transformers) trained on the [German STSbenchmark Dataset](https://github.com/t-systems-on-site-services-gmbh/german-STSbenchmark). It was trained from [Philip May](https://eniak.de/) and open-sourced by [T-Systems-onsite](https://www.t-systems-onsite.de/).The base language model is the [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased) from [Bayerische Staatsbibliothek ](https://huggingface.co/dbmdz).
+
+## Intended uses
+> Sentence-BERT (SBERT) is a  modification  of  the  pretrained BERT network that use siamese and triplet network structures to derive semantically mean-ingful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT.
+
+Source: [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084)
+
+## Training procedure
+We did an automatic hyperprameter optimization with [Optuna](https://github.com/optuna/optuna) and found the following hyperprameters:
+- batch_size = 5
+- num_epochs = 11
+- lr = 2.637549780860126e-05
+- eps = 5.0696075038683e-06
+- weight_decay = 0.02817210102940054
+- warmup_steps = 27.342745941760147 % of total steps
+
+The final model was trained on the combination of all three datasets: `sts_de_dev.csv`, `sts_de_test.csv` and `sts_de_train.csv`

model_cards/T-Systems-onsite/german-roberta-sentence-transformer-v2/README.md

@@ -1,66 +0,0 @@
----
-language: de
-license: mit
----
-
-# German RoBERTa for Sentence Embeddings V2
-This model is intended to [compute sentence (text embeddings)](https://www.sbert.net/docs/usage/computing_sentence_embeddings.html) for German text. These embeddings can then be compared with [cosine-similarity](https://en.wikipedia.org/wiki/Cosine_similarity) to find sentences with a similar semantic meaning. For example this can be useful for [semantic textual similarity](https://www.sbert.net/docs/usage/semantic_textual_similarity.html), [semantic search](https://www.sbert.net/docs/usage/semantic_search.html), or [paraphrase mining](https://www.sbert.net/docs/usage/paraphrase_mining.html). To do this you have to use the [Sentence Transformers Python framework](https://github.com/UKPLab/sentence-transformers).
-
-> Sentence-BERT (SBERT) is a  modification  of  the  pretrained BERT network that use siamese and triplet network structures to derive semantically mean-ingful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT.
-
-Source: [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084)
-
-This model is fine-tuned from [Philip May](https://eniak.de/) and open-sourced by [T-Systems-onsite](https://www.t-systems-onsite.de/). Special thanks to [Nils Reimers](https://www.nils-reimers.de/) for your awesome open-source work, the Sentence Transformers, the models and all your help on GitHub.
-
-## How to use
-**The usage description above - provided by Hugging Face - is wrong for sentence embeddings! Please use this:**
-
-To use this model install the `sentence-transformers` package (see here: <https://github.com/UKPLab/sentence-transformers>).
-
-```python
-from sentence_transformers import SentenceTransformer
-model = SentenceTransformer('T-Systems-onsite/german-roberta-sentence-transformer-v2')
-```
-
-For details of usage and examples see here:
-- [Computing Sentence Embeddings](https://www.sbert.net/docs/usage/computing_sentence_embeddings.html)
-- [Semantic Textual Similarity](https://www.sbert.net/docs/usage/semantic_textual_similarity.html)
-- [Paraphrase Mining](https://www.sbert.net/docs/usage/paraphrase_mining.html)
-- [Semantic Search](https://www.sbert.net/docs/usage/semantic_search.html)
-- [Cross-Encoders](https://www.sbert.net/docs/usage/cross-encoder.html)
-- [Examples on GitHub](https://github.com/UKPLab/sentence-transformers/tree/master/examples/applications)
-
-## Training
-The base model is [xlm-roberta-base](https://huggingface.co/xlm-roberta-base). This model has been further trained by [Nils Reimers](https://www.nils-reimers.de/) on a large scale paraphrase dataset for 50+ languages. [Nils Reimers](https://www.nils-reimers.de/) about this [on GitHub](https://github.com/UKPLab/sentence-transformers/issues/509#issuecomment-712243280):
-
->A paper is upcoming for the paraphrase models.
->
->These models were trained on various datasets with Millions of examples for paraphrases, mainly derived from Wikipedia edit logs, paraphrases mined from Wikipedia and SimpleWiki, paraphrases from news reports, AllNLI-entailment pairs with in-batch-negative loss etc.
->
->In internal tests, they perform much better than the NLI+STSb models as they have see more and broader type of training data. NLI+STSb has the issue that they are rather narrow in their domain and do not contain any domain specific words / sentences (like from chemistry, computer science, math etc.). The paraphrase models has seen plenty of sentences from various domains.
->
->More details with the setup, all the datasets, and a wider evaluation will follow soon.
-
-The resulting model called `xlm-r-distilroberta-base-paraphrase-v1` has been released here: <https://github.com/UKPLab/sentence-transformers/releases/tag/v0.3.8>
-
-Building on this cross language model we fine-tuned it for German language on the deepl.com dataset of our [German STSbenchmark dataset](https://github.com/t-systems-on-site-services-gmbh/german-STSbenchmark).
-
-We did an automatic hyperprameter search for 102 trials with [Optuna](https://github.com/optuna/optuna). Using crossvalidation on the deepl.com test and dev dataset we found the following best hyperprameters:
-- batch_size = 15
-- num_epochs = 4
-- lr = 2.2995320905210864e-05
-- eps = 1.8979875906303792e-06
-- weight_decay = 0.003314045812507563
-- warmup_steps_proportion = 0.46141685205829014
-
-The final model was trained with these hyperparameters on the combination of `sts_de_train.csv` and `sts_de_dev.csv`. The `sts_de_test.csv` was left for testing. The AWS dataset has not been used.
-
-# Evaluation
-The evaluation has been done on the test set of our [German STSbenchmark dataset](https://github.com/t-systems-on-site-services-gmbh/german-STSbenchmark). The code is available on [Colab](https://colab.research.google.com/drive/1aCWOqDQx953kEnQ5k4Qn7uiixokocOHv?usp=sharing). As the metric for evaluation we use the Spearman’s rank correlation between the  cosine-similarity of the sentence embeddings and STSbenchmark labels.
-
-| Model Name                           | Spearman rank correlation         |
-|--------------------------------------|-----------------------------------|
-| xlm-r-distilroberta-base-paraphrase-v1                      | 0.8079     |
-| xlm-r-100langs-bert-base-nli-stsb-mean-tokens               | 0.8194     |
-| xlm-r-bert-base-nli-stsb-mean-tokens                        | 0.8194     |
-| **T-Systems-onsite/german-roberta-sentence-transformer-v2** | **0.8529** |

model_cards/abhilash1910/french-roberta/README.md

@@ -1,131 +0,0 @@
-# Roberta Trained Model For Masked Language Model On French Corpus :robot:
-
-
-This is a Masked Language Model trained with [Roberta](https://huggingface.co/transformers/model_doc/roberta.html) on a small French News Corpus(Leipzig corpora).
-The model is built using Huggingface transformers.
-The model can be found at :[French-Roberta](https://huggingface.co/abhilash1910/french-roberta)
-
-
-## Specifications
-
-
-The corpus for training is taken from Leipzig Corpora (French News) , and is trained on a small set of the corpus (300K). 
-
-
-## Model Specification
-
-
-The model chosen for training is [Roberta](https://arxiv.org/abs/1907.11692) with the following specifications:
- 1. vocab_size=32000
- 2. max_position_embeddings=514
- 3. num_attention_heads=12
- 4. num_hidden_layers=6
- 5. type_vocab_size=1
-
-
-This is trained by using  RobertaConfig from transformers package.The total training parameters :68124416
-The model is trained for 100 epochs with a gpu batch size of 64 units. 
-More details for building custom models can be found at the [HuggingFace Blog](https://huggingface.co/blog/how-to-train)
-
-
-
-## Usage Specifications
-
-
-For using this model, we have to first import AutoTokenizer and AutoModelWithLMHead Modules from transformers
-After that we have to specify, the pre-trained model,which in this case is 'abhilash1910/french-roberta' for the tokenizers and the model.
-
-
-```python
-from transformers import AutoTokenizer, AutoModelWithLMHead
-
-tokenizer = AutoTokenizer.from_pretrained("abhilash1910/french-roberta")
-
-model = AutoModelWithLMHead.from_pretrained("abhilash1910/french-roberta")
-```
-
-
-After this the model will be downloaded, it will take some time to download all the model files.
-For testing the model, we have to import  pipeline module from transformers and create a masked output model for inference as follows:
-
-
-```python
-from transformers import pipeline
-model_mask = pipeline('fill-mask', model='abhilash1910/french-roberta')
-model_mask("Le tweet <mask>.")
-```
-
-
-Some of the examples are also provided with generic French sentences:
-
-Example 1:
-
-
-```python
-model_mask("À ce jour, <mask> projet a entraîné")
-```
-
-
-Output:
-
-
-```bash
-[{'sequence': '<s>À ce jour, belles projet a entraîné</s>',
-  'score': 0.18685665726661682,
-  'token': 6504,
-  'token_str': 'Ġbelles'},
- {'sequence': '<s>À ce jour,- projet a entraîné</s>',
-  'score': 0.0005200508167035878,
-  'token': 17,
-  'token_str': '-'},
- {'sequence': '<s>À ce jour, de projet a entraîné</s>',
-  'score': 0.00045729897101409733,
-  'token': 268,
-  'token_str': 'Ġde'},
- {'sequence': '<s>À ce jour, du projet a entraîné</s>',
-  'score': 0.0004307595663703978,
-  'token': 326,
-  'token_str': 'Ġdu'},
- {'sequence': '<s>À ce jour," projet a entraîné</s>',
-  'score': 0.0004219160182401538,
-  'token': 6,
-  'token_str': '"'}]
-  ```
- 
- Example 2:
- 
-```python
- model_mask("C'est un <mask>")
-```
-
-Output:
-
-```bash
-[{'sequence': "<s>C'est un belles</s>",
-  'score': 0.16440927982330322,
-  'token': 6504,
-  'token_str': 'Ġbelles'},
- {'sequence': "<s>C'est un de</s>",
-  'score': 0.0005495127406902611,
-  'token': 268,
-  'token_str': 'Ġde'},
- {'sequence': "<s>C'est un du</s>",
-  'score': 0.00044988933950662613,
-  'token': 326,
-  'token_str': 'Ġdu'},
- {'sequence': "<s>C'est un-</s>",
-  'score': 0.00044542422983795404,
-  'token': 17,
-  'token_str': '-'},
- {'sequence': "<s>C'est un\t</s>",
-  'score': 0.00037563967634923756,
-  'token': 202,
-  'token_str': 'ĉ'}]
-  ```
-  
-
-## Resources
-
-For all resources , please look into the [HuggingFace](https://huggingface.co/) Site and the [Repositories](https://github.com/huggingface).
-
-

model_cards/akhooli/xlm-r-large-arabic-toxic/README.md

@@ -1,12 +0,0 @@
----
-
-language:
-- ar
-- en
-
-license: mit
----
-### xlm-r-large-arabic-toxic (toxic/hate speech classifier) 
-Toxic (hate speech) classification (Label_0: non-toxic, Label_1: toxic) of Arabic comments by fine-tuning XLM-Roberta-Large. 
-Zero shot classification of other languages (also works in mixed languages - ex. Arabic & English).  
-Usage and further info: see last section in this [Colab notebook](https://lnkd.in/d3bCFyZ)

model_cards/aliosm/ai-soco-c++-roberta-small-clas/README.md

@@ -1,56 +0,0 @@
----
-language: "c++"
-tags:
-- exbert
-- authorship-identification
-- fire2020
-- pan2020
-- ai-soco
-- classification
-license: "mit"
-datasets:
-- ai-soco
-metrics:
-- accuracy
----
-
-# ai-soco-c++-roberta-small-clas
-
-## Model description
-
-`ai-soco-c++-roberta-small` model fine-tuned on [AI-SOCO](https://sites.google.com/view/ai-soco-2020) task.
-
-#### How to use
-
-You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
-
-#### Limitations and bias
-
-The model is limited to C++ programming language only.
-
-## Training data
-
-The model initialized from [`ai-soco-c++-roberta-small`](https://github.com/huggingface/transformers/blob/master/model_cards/aliosm/ai-soco-c++-roberta-small) model and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset to do text classification.
-
-## Training procedure
-
-The model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
-
-## Eval results
-
-The model achieved 93.19%/92.88% accuracy on AI-SOCO task and ranked in the 4th place.
-
-### BibTeX entry and citation info
-
-```bibtex
-@inproceedings{ai-soco-2020-fire,
-    title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
-    author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
-    booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
-    year = "2020"
-}
-```
-
-<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-small-clas">
-	<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
-</a>

model_cards/aliosm/ai-soco-c++-roberta-small/README.md

@@ -1,55 +0,0 @@
----
-language: "c++"
-tags:
-- exbert
-- authorship-identification
-- fire2020
-- pan2020
-- ai-soco
-license: "mit"
-datasets:
-- ai-soco
-metrics:
-- perplexity
----
-
-# ai-soco-c++-roberta-small
-
-## Model description
-
-From scratch pre-trained RoBERTa model with 6 layers and 12 attention heads using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which consists of C++ codes crawled from CodeForces website.
-
-## Intended uses & limitations
-
-The model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.
-
-#### How to use
-
-You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
-
-#### Limitations and bias
-
-The model is limited to C++ programming language only.
-
-## Training data
-
-The model initialized randomly and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which contains 100K C++ source codes.
-
-## Training procedure
-
-The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in [`run_language_modelling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) script. Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
-
-### BibTeX entry and citation info
-
-```bibtex
-@inproceedings{ai-soco-2020-fire,
-    title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
-    author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
-    booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
-    year = "2020"
-}
-```
-
-<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-small">
-	<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
-</a>

model_cards/aliosm/ai-soco-c++-roberta-tiny-96-clas/README.md

@@ -1,56 +0,0 @@
----
-language: "c++"
-tags:
-- exbert
-- authorship-identification
-- fire2020
-- pan2020
-- ai-soco
-- classification
-license: "mit"
-datasets:
-- ai-soco
-metrics:
-- accuracy
----
-
-# ai-soco-c++-roberta-tiny-96-clas
-
-## Model description
-
-`ai-soco-c++-roberta-tiny-96` model fine-tuned on [AI-SOCO](https://sites.google.com/view/ai-soco-2020) task.
-
-#### How to use
-
-You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
-
-#### Limitations and bias
-
-The model is limited to C++ programming language only.
-
-## Training data
-
-The model initialized from [`ai-soco-c++-roberta-tiny-96`](https://github.com/huggingface/transformers/blob/master/model_cards/aliosm/ai-soco-c++-roberta-tiny-96) model and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset to do text classification.
-
-## Training procedure
-
-The model trained on Google Colab platform using V100 GPU for 10 epochs, 16 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
-
-## Eval results
-
-The model achieved 91.12%/91.02% accuracy on AI-SOCO task and ranked in the 7th place.
-
-### BibTeX entry and citation info
-
-```bibtex
-@inproceedings{ai-soco-2020-fire,
-    title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
-    author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
-    booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
-    year = "2020"
-}
-```
-
-<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-tiny-96-clas">
-	<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
-</a>

model_cards/aliosm/ai-soco-c++-roberta-tiny-96/README.md

@@ -1,55 +0,0 @@
----
-language: "c++"
-tags:
-- exbert
-- authorship-identification
-- fire2020
-- pan2020
-- ai-soco
-license: "mit"
-datasets:
-- ai-soco
-metrics:
-- perplexity
----
-
-# ai-soco-c++-roberta-tiny-96
-
-## Model description
-
-From scratch pre-trained RoBERTa model with 1 layers and 96 attention heads using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which consists of C++ codes crawled from CodeForces website.
-
-## Intended uses & limitations
-
-The model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.
-
-#### How to use
-
-You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
-
-#### Limitations and bias
-
-The model is limited to C++ programming language only.
-
-## Training data
-
-The model initialized randomly and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which contains 100K C++ source codes.
-
-## Training procedure
-
-The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in [`run_language_modelling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) script. Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
-
-### BibTeX entry and citation info
-
-```bibtex
-@inproceedings{ai-soco-2020-fire,
-    title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
-    author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
-    booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
-    year = "2020"
-}
-```
-
-<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-tiny-96">
-	<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
-</a>

model_cards/aliosm/ai-soco-c++-roberta-tiny-clas/README.md

@@ -1,56 +0,0 @@
----
-language: "c++"
-tags:
-- exbert
-- authorship-identification
-- fire2020
-- pan2020
-- ai-soco
-- classification
-license: "mit"
-datasets:
-- ai-soco
-metrics:
-- accuracy
----
-
-# ai-soco-c++-roberta-tiny-clas
-
-## Model description
-
-`ai-soco-c++-roberta-tiny` model fine-tuned on [AI-SOCO](https://sites.google.com/view/ai-soco-2020) task.
-
-#### How to use
-
-You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
-
-#### Limitations and bias
-
-The model is limited to C++ programming language only.
-
-## Training data
-
-The model initialized from [`ai-soco-c++-roberta-tiny`](https://github.com/huggingface/transformers/blob/master/model_cards/aliosm/ai-soco-c++-roberta-tiny) model and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset to do text classification.
-
-## Training procedure
-
-The model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
-
-## Eval results
-
-The model achieved 87.66%/87.46% accuracy on AI-SOCO task and ranked in the 9th place.
-
-### BibTeX entry and citation info
-
-```bibtex
-@inproceedings{ai-soco-2020-fire,
-    title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
-    author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
-    booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
-    year = "2020"
-}
-```
-
-<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-tiny-clas">
-	<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
-</a>

model_cards/aliosm/ai-soco-c++-roberta-tiny/README.md

@@ -1,55 +0,0 @@
----
-language: "c++"
-tags:
-- exbert
-- authorship-identification
-- fire2020
-- pan2020
-- ai-soco
-license: "mit"
-datasets:
-- ai-soco
-metrics:
-- perplexity
----
-
-# ai-soco-c++-roberta-tiny
-
-## Model description
-
-From scratch pre-trained RoBERTa model with 1 layers and 12 attention heads using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which consists of C++ codes crawled from CodeForces website.
-
-## Intended uses & limitations
-
-The model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.
-
-#### How to use
-
-You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
-
-#### Limitations and bias
-
-The model is limited to C++ programming language only.
-
-## Training data
-
-The model initialized randomly and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which contains 100K C++ source codes.
-
-## Training procedure
-
-The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 32\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in [`run_language_modelling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) script. Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
-
-### BibTeX entry and citation info
-
-```bibtex
-@inproceedings{ai-soco-2020-fire,
-    title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
-    author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
-    booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
-    year = "2020"
-}
-```
-
-<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-tiny">
-	<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
-</a>

model_cards/allegro/herbert-base-cased/README.md

@@ -1,51 +0,0 @@
----
-language: pl
-tags:
-- herbert
-license: cc-by-sa-4.0
----
-
-# HerBERT 
-**[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** is a BERT-based Language Model trained on Polish Corpora
-using MLM and SSO objectives with dynamic masking of whole words.
-Model training and experiments were conducted with [transformers](https://github.com/huggingface/transformers) in version 2.9.
-
-## Tokenizer
-The training dataset was tokenized into subwords using ``CharBPETokenizer`` a character level byte-pair encoding with
-a vocabulary size of 50k tokens. The tokenizer itself was trained with a [tokenizers](https://github.com/huggingface/tokenizers) library. 
-We kindly encourage you to use the **Fast** version of tokenizer, namely ``HerbertTokenizerFast``.
-
-## HerBERT usage
-
-
-Example code:
-```python
-from transformers import AutoTokenizer, AutoModel
-
-tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-base-cased")
-model = AutoModel.from_pretrained("allegro/herbert-base-cased")
-
-output = model(
-    **tokenizer.batch_encode_plus(
-        [
-            (
-                "A potem szedł środkiem drogi w kurzawie, bo zamiatał nogami, ślepy dziad prowadzony przez tłustego kundla na sznurku.",
-                "A potem leciał od lasu chłopak z butelką, ale ten ujrzawszy księdza przy drodze okrążył go z dala i biegł na przełaj pól do karczmy."
-            )
-        ],
-    padding='longest',
-    add_special_tokens=True,
-    return_tensors='pt'
-    )
-)
-```
-
-
-## License
-CC BY-SA 4.0
-
-
-## Authors
-Model was trained by **Allegro Machine Learning Research** team.
-
-You can contact us at: <a href="mailto:klejbenchmark@allegro.pl">klejbenchmark@allegro.pl</a>

model_cards/allegro/herbert-large-cased/README.md

@@ -1,50 +0,0 @@
----
-language: pl
-tags:
-- herbert
-license: cc-by-sa-4.0
----
-# HerBERT 
-**[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** is a BERT-based Language Model trained on Polish Corpora
-using MLM and SSO objectives with dynamic masking of whole words.
-Model training and experiments were conducted with [transformers](https://github.com/huggingface/transformers) in version 2.9.
-
-## Tokenizer
-The training dataset was tokenized into subwords using ``CharBPETokenizer`` a character level byte-pair encoding with
-a vocabulary size of 50k tokens. The tokenizer itself was trained with a [tokenizers](https://github.com/huggingface/tokenizers) library. 
-We kindly encourage you to use the **Fast** version of tokenizer, namely ``HerbertTokenizerFast``.
-
-## HerBERT usage
-
-
-Example code:
-```python
-from transformers import AutoTokenizer, AutoModel
-
-tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-large-cased")
-model = AutoModel.from_pretrained("allegro/herbert-large-cased")
-
-output = model(
-    **tokenizer.batch_encode_plus(
-        [
-            (
-                "A potem szedł środkiem drogi w kurzawie, bo zamiatał nogami, ślepy dziad prowadzony przez tłustego kundla na sznurku.",
-                "A potem leciał od lasu chłopak z butelką, ale ten ujrzawszy księdza przy drodze okrążył go z dala i biegł na przełaj pól do karczmy."
-            )
-        ],
-    padding='longest',
-    add_special_tokens=True,
-    return_tensors='pt'
-    )
-)
-```
-
-
-## License
-CC BY-SA 4.0
-
-
-## Authors
-Model was trained by **Allegro Machine Learning Research** team.
-
-You can contact us at: <a href="mailto:klejbenchmark@allegro.pl">klejbenchmark@allegro.pl</a>

model_cards/amine/bert-base-5lang-cased/README.md

@@ -1,64 +0,0 @@
----
-language: 
-- en
-- fr
-- es
-- de
-- zh
-
-tags:
-- pytorch
-- bert
-- multilingual
-- en
-- fr
-- es
-- de
-- zh
-
-datasets: wikipedia
-
-license: apache-2.0
-
-inference: false
----
-
-# bert-base-5lang-cased
-This is a smaller version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handles only 5 languages (en, fr, es, de and zh) instead of 104.
-The model is therefore 30% smaller than the original one (124M parameters instead of 178M) but gives exactly the same representations for the above cited languages. 
-Starting from `bert-base-5lang-cased` will facilitate the deployment of your model on public cloud platforms while keeping similar results. 
-For instance, Google Cloud Platform requires that the model size on disk should be lower than 500 MB for serveless deployments (Cloud Functions / Cloud ML) which is not the case of the original `bert-base-multilingual-cased`.
-
-For more information about the models size, memory footprint and loading time please refer to the table below:
-
-|            Model             | Num parameters |   Size   |  Memory  | Loading time |
-| ---------------------------- | -------------- | -------- | -------- | ------------ |
-| bert-base-multilingual-cased |   178 million  |  714 MB  | 1400 MB  |    4.2 sec   |
-| bert-base-5lang-cased        |   124 million  |  495 MB  |  950 MB  |    3.6 sec   |
-
-These measurements have been computed on a [Google Cloud n1-standard-1 machine (1 vCPU, 3.75 GB)](https://cloud.google.com/compute/docs/machine-types\#n1_machine_type).
-
-## How to use
-
-```python
-from transformers import AutoTokenizer, AutoModel
-
-tokenizer = AutoTokenizer.from_pretrained("amine/bert-base-5lang-cased")
-model = AutoModel.from_pretrained("amine/bert-base-5lang-cased")
-
-```
-
-### How to cite
-
-```bibtex
-@inproceedings{smallermbert,
-  title={Load What You Need: Smaller Versions of Mutlilingual BERT},
-  author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
-  booktitle={SustaiNLP / EMNLP},
-  year={2020}
-}
-```
-
-## Contact 
-
-Please contact amine@geotrend.fr for any question, feedback or request.

model_cards/cambridgeltl/BioRedditBERT-uncased/README.md

@@ -1,52 +0,0 @@
----
-language: 
-- en
-tags:
-- BioNLP
-- social_media
----
-
-# BioRedditBERT
-
-## Model description
-BioRedditBERT is a BERT model initialised from BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) and further pre-trained on health-related Reddit posts. Please view our paper [COMETA: A Corpus for Medical Entity Linking in the Social Media](https://arxiv.org/pdf/2010.03295.pdf) (EMNLP 2020) for more details.
-
-
-## Training data
-
-We crawled all threads from 68 health themed subreddits such as `r/AskDocs`, `r/health` and etc. starting from the beginning of 2015 to the end of 2018, obtaining a collection of more than
-800K discussions. This collection was then pruned by removing deleted posts, comments from bots or moderators, and so on. In the end, we obtained the training corpus with ca. 300 million tokens and a vocabulary
-size of ca. 780,000 words.
-
-## Training procedure
-We use the same pre-training script in the original [google-research/bert](https://github.com/google-research/bert) repo. The model is initialised with [`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`](https://github.com/dmis-lab/biobert).
-We train with a batch size of 64, a max sequence length of 64, a learning rate of `2e-5` for 100k steps on two GeForce GTX 1080Ti (11 GB) GPUs. Other hyper-parameters are the same as default.
-
-
-## Eval results
-To show the benefit from further pre-training on the social media domain, we demonstrate results on a medical entity linking dataset also in the social media: [AskAPatient](https://zenodo.org/record/55013#.X4ncRmTYpb8) [(Limsopatham and Collier 2016)](https://www.aclweb.org/anthology/P16-1096.pdf). 
-We follow the same 10-fold cross-validation procedure for all models and report the average result without fine-tuning. `[CLS]` is used as representations for entity mentions (we also tried average of all tokens but found `[CLS]` generally performs better).
-
-Model   | Accuracy@1 | Accuracy@5
--------|---------|---------
-[BERT-base-uncased](https://huggingface.co/bert-base-uncased)  | 38.2 | 43.3
-[BioBERT v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) | 41.4 | 51.5
-[ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) | 43.9 | 54.3
-[BlueBERT](https://ftp.ncbi.nlm.nih.gov/pub/lu/Suppl/NCBI-BERT/NCBI_BERT_pubmed_mimic_uncased_L-12_H-768_A-12.zip) | 41.5 | 48.5
-[SciBERT](https://huggingface.co/allenai/scibert_scivocab_uncased) | 42.3 | 51.9 
-[PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) | 42.5 | 49.6
-BioRedditBERT | **44.3** | **56.2**
-
-
-### BibTeX entry and citation info
-
-```bibtex
-@inproceedings{basaldella-2020-cometa,
-    title = "{COMETA}: A Corpus for Medical Entity Linking in the Social Media",
-    author = "Basaldella, Marco  and Liu, Fangyu, and Shareghi, Ehsan, and Collier, Nigel",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
-    month = nov,
-    year = "2020",
-    publisher = "Association for Computational Linguistics"
-}
-```

model_cards/camembert-base-README.md

@@ -1,8 +1,7 @@
 ---
 language: fr
+
 license: mit
-datasets:
-- oscar
 ---
 
 # CamemBERT: a Tasty French Language Model

model_cards/cedpsam/chatbot_fr/README.md

@@ -13,9 +13,6 @@ trained with this notebook
 https://colab.research.google.com/drive/1pfCV3bngAmISNZVfDvBMyEhQKuYw37Rl#scrollTo=AyImj9qZYLRi&uniqifier=3
 
 config from microsoft/DialoGPT-medium
-dataset generated from 2018 opensubtitle from opus folowing these guidelines
-https://github.com/PolyAI-LDN/conversational-datasets/tree/master/opensubtitles with this notebook
-https://colab.research.google.com/drive/1uyh3vJ9nEjqOHI68VD73qxt4olJzODxi#scrollTo=deaacv4XfLMk
 ### How to use
 
 Now we are ready to try out how the model works as a chatting partner!

model_cards/cimm-kzn/rudr-bert/README.md

@@ -8,22 +8,16 @@ RuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC
 
 If you find this repository helpful, feel free to cite our publication:
 
-[1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews. 
+[1] https://arxiv.org/abs/2004.03659
-   
-   preprint: https://arxiv.org/abs/2004.03659
 ```
-@article{10.1093/bioinformatics/btaa675,
+@misc{tutubalina2020russian,
-    author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey},
+    title={The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews},
-    title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}",
+    author={Elena Tutubalina and Ilseyar Alimova and Zulfat Miftahutdinov and Andrey Sakhovskiy and Valentin Malykh and Sergey Nikolenko},
-    journal = {Bioinformatics},
+    year={2020},
-    year = {2020},
+    eprint={2004.03659},
-    month = {07},
+    archivePrefix={arXiv},
-    issn = {1367-4803},
+    primaryClass={cs.CL}
-    doi = {10.1093/bioinformatics/btaa675},
+}
-    url = {https://doi.org/10.1093/bioinformatics/btaa675},
-    note = {btaa675},
-    eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf},
-} 
 ```
 [2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.
    [link to paper](https://www.researchgate.net/profile/Elena_Tutubalina/publication/323751823_Using_semantic_analysis_of_texts_for_the_identification_of_drugs_with_similar_therapeutic_effects/links/5bf7cfc3299bf1a0202cbc1f/Using-semantic-analysis-of-texts-for-the-identification-of-drugs-with-similar-therapeutic-effects.pdf)

model_cards/cooelf/limitbert/README.md

@@ -1,59 +0,0 @@
-# LIMIT-BERT
-
-Code and model for the *EMNLP 2020 Findings* paper: 
-
-[LIMIT-BERT: Linguistic Informed Multi-task BERT](https://arxiv.org/abs/1910.14296)) 
-
-## Contents
-
-1. [Requirements](#Requirements)
-2. [Training](#Training)
-
-## Requirements
-
-* Python 3.6 or higher.
-* Cython 0.25.2 or any compatible version.
-* [PyTorch](http://pytorch.org/) 1.0.0+. 
-* [EVALB](http://nlp.cs.nyu.edu/evalb/). Before starting, run `make` inside the `EVALB/` directory to compile an `evalb` executable. This will be called from Python for evaluation. 
-* [pytorch-transformers](https://github.com/huggingface/pytorch-transformers) PyTorch 1.0.0+ or any compatible version.
-
-#### Pre-trained Models (PyTorch)
-The following pre-trained models are available for download from Google Drive:
-* [`LIMIT-BERT`](https://drive.google.com/open?id=1fm0cK2A91iLG3lCpwowCCQSALnWS2X4i): 
-  PyTorch version, same setting with BERT-Large-WWM，loading model with [pytorch-transformers](https://github.com/huggingface/pytorch-transformers).
-
-## How to use
-
-```
-from transformers import AutoTokenizer, AutoModel
-tokenizer = AutoTokenizer.from_pretrained("cooelf/limitbert")
-model = AutoModel.from_pretrained("cooelf/limitbert")
-```
-
-Please see our original repo for the training scripts.
-
-https://github.com/cooelf/LIMIT-BERT
-
-## Training
-
-To train LIMIT-BERT, simply run:
-```
-sh run_limitbert.sh
-```
-### Evaluation Instructions
-
-To test after setting model path:
-```
-sh test_bert.sh
-```
-
-## Citation
-
-```
-@article{zhou2019limit,
-  title={{LIMIT-BERT}: Linguistic informed multi-task {BERT}},
-  author={Zhou, Junru and Zhang, Zhuosheng and Zhao, Hai},
-  journal={arXiv preprint arXiv:1910.14296},
-  year={2019}
-}
-```

model_cards/distilbert-base-cased-README.md

@@ -1,3 +0,0 @@
----
-license: apache-2.0
----

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

@@ -4,5 +4,4 @@ datasets:
 - squad
 metrics:
 - squad
-license: apache-2.0
 ---

model_cards/distilbert-base-german-cased-README.md

@@ -1,5 +1,4 @@
 ---
 language: de
-license: apache-2.0
 ---
 ## distilbert-base-german-cased

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

@@ -6,5 +6,4 @@ widget:
   context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
 - text: "How many square kilometers of rainforest is covered in the basin?"
   context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
-license: apache-2.0
 ---

model_cards/distilbert-base-uncased-finetuned-sst-2-english-README.md

@@ -1,3 +0,0 @@
----
-license: apache-2.0
----

model_cards/dslim/bert-base-NER/README.md

@@ -1,114 +0,0 @@
----
-language: en
-datasets:
-- conll2003
----
-# bert-base-NER
-
-## Model description
-
-**bert-base-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). 
-
-Specifically, this model is a *bert-base-cased* model that was fine-tuned on the English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset. 
-## Intended uses & limitations
-
-#### How to use
-
-You can use this model with Transformers *pipeline* for NER.
-
-```python
-from transformers import AutoTokenizer, AutoModelForTokenClassification
-from transformers import pipeline
-
-tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
-model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
-
-nlp = pipeline("ner", model=model, tokenizer=tokenizer)
-example = "My name is Wolfgang and I live in Berlin"
-
-ner_results = nlp(example)
-print(ner_results)
-```
-
-#### Limitations and bias
-
-This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases. 
-
-## Training data
-
-This model was fine-tuned on English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset. 
-
-The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
-Abbreviation|Description
--|-
-O|Outside of a named entity
-B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
-I-MIS |Miscellaneous entity
-B-PER |Beginning of a person’s name right after another person’s name
-I-PER |Person’s name
-B-ORG |Beginning of an organisation right after another organisation
-I-ORG |Organisation
-B-LOC |Beginning of a location right after another location
-I-LOC |Location
-
-
-### CoNLL-2003 English Dataset Statistics
-This dataset was derived from the Reuters corpus which consists of Reuters news stories. You can read more about how this dataset was created in the CoNLL-2003 paper. 
-#### # of training examples per entity type
-Dataset|LOC|MISC|ORG|PER
--|-|-|-|-
-Train|7140|3438|6321|6600
-Dev|1837|922|1341|1842
-Test|1668|702|1661|1617
-#### # of articles/sentences/tokens per dataset
-Dataset |Articles |Sentences |Tokens
--|-|-|-
-Train |946 |14,987 |203,621
-Dev |216 |3,466 |51,362
-Test |231 |3,684 |46,435
-
-## Training procedure
-
-This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original BERT paper](https://arxiv.org/pdf/1810.04805) which trained & evaluated the model on CoNLL-2003 NER task. 
-
-## Eval results
-metric|dev|test
--|-|-
-f1 |95.1 |91.3
-precision |95.0 |90.7
-recall |95.3 |91.9
-
-The test metrics are a little lower than the official Google BERT results which encoded document context & experimented with CRF. More on replicating the original results [here](https://github.com/google-research/bert/issues/223).
-
-### BibTeX entry and citation info
-
-```
-@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}
-}
-```
-```
-@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
-    title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
-    author = "Tjong Kim Sang, Erik F.  and
-      De Meulder, Fien",
-    booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
-    year = "2003",
-    url = "https://www.aclweb.org/anthology/W03-0419",
-    pages = "142--147",
-}
-```