fix #5693

2020-07-14 12:01:45 +02:00
595 changed files with 10415 additions and 31384 deletions
@@ -1,66 +1,4 @@
-version: 2.1
-orbs:
-    gcp-gke: circleci/gcp-gke@1.0.4
-    go: circleci/go@1.3.0
-
-# TPU REFERENCES
-references:
-    checkout_ml_testing: &checkout_ml_testing
-        run:
-            name: Checkout ml-testing-accelerators
-            command: |
-                git clone https://github.com/GoogleCloudPlatform/ml-testing-accelerators.git
-                cd ml-testing-accelerators
-                git fetch origin 5e88ac24f631c27045e62f0e8d5dfcf34e425e25:stable
-                git checkout stable
-    build_push_docker: &build_push_docker
-        run:
-            name: Configure Docker
-            command: |
-                gcloud --quiet auth configure-docker
-                cd docker/transformers-pytorch-tpu
-                if [ -z "$CIRCLE_PR_NUMBER" ]; then docker build --tag "$GCR_IMAGE_PATH:$CIRCLE_WORKFLOW_JOB_ID" -f Dockerfile --build-arg "TEST_IMAGE=1" . ; else docker build --tag "$GCR_IMAGE_PATH:$CIRCLE_WORKFLOW_JOB_ID" -f Dockerfile --build-arg "TEST_IMAGE=1" --build-arg "GITHUB_REF=pull/$CIRCLE_PR_NUMBER/head" . ; fi
-                docker push "$GCR_IMAGE_PATH:$CIRCLE_WORKFLOW_JOB_ID"
-    deploy_cluster: &deploy_cluster
-        run:
-            name: Deploy the job on the kubernetes cluster
-            command: |
-                go get github.com/google/go-jsonnet/cmd/jsonnet && \
-                export PATH=$PATH:$HOME/go/bin && \
-                kubectl create -f docker/transformers-pytorch-tpu/dataset.yaml || true && \
-                job_name=$(jsonnet -J ml-testing-accelerators/ docker/transformers-pytorch-tpu/bert-base-cased.jsonnet --ext-str image=$GCR_IMAGE_PATH --ext-str image-tag=$CIRCLE_WORKFLOW_JOB_ID | kubectl create -f -) && \
-                job_name=${job_name#job.batch/} && \
-                job_name=${job_name% created} && \
-                echo "Waiting on kubernetes job: $job_name" && \
-                i=0 && \
-                # 30 checks spaced 30s apart = 900s total.
-                max_checks=30 && \
-                status_code=2 && \
-                # Check on the job periodically. Set the status code depending on what
-                # happened to the job in Kubernetes. If we try max_checks times and
-                # still the job hasn't finished, give up and return the starting
-                # non-zero status code.
-                while [ $i -lt $max_checks ]; do ((i++)); if kubectl get jobs $job_name -o jsonpath='Failed:{.status.failed}' | grep "Failed:1"; then status_code=1 && break; elif kubectl get jobs $job_name -o jsonpath='Succeeded:{.status.succeeded}' | grep "Succeeded:1" ; then status_code=0 && break; else echo "Job not finished yet"; fi; sleep 30; done && \
-                echo "Done waiting. Job status code: $status_code" && \
-                pod_name=$(kubectl get po -l controller-uid=`kubectl get job $job_name -o "jsonpath={.metadata.labels.controller-uid}"` | awk 'match($0,!/NAME/) {print $1}') && \
-                echo "GKE pod name: $pod_name" && \
-                kubectl logs -f $pod_name --container=train
-                echo "Done with log retrieval attempt." && \
-                gcloud container images delete "$GCR_IMAGE_PATH:$CIRCLE_WORKFLOW_JOB_ID" --force-delete-tags && \
-                exit $status_code
-    delete_gke_jobs: &delete_gke_jobs
-        run:
-            name: Delete GKE Jobs
-            command: |
-                # Match jobs whose age matches patterns like '1h' or '1d', i.e. any job
-                # that has been around longer than 1hr. First print all columns for
-                # matches, then execute the delete.
-                kubectl get job | awk 'match($4,/[0-9]+[dh]/) {print $0}'
-                kubectl delete job $(kubectl get job | awk 'match($4,/[0-9]+[dh]/) {print $1}')
-
-
-
-
+version: 2
 jobs:
    run_tests_torch_and_tf:
        working_directory: ~/transformers
@@ -72,17 +10,8 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.3-torch_and_tf-{{ checksum "setup.py" }}
-                      - v0.3-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,torch,testing]
-            - run: pip install codecov pytest-cov
-            - save_cache:
-                key: v0.3-{{ checksum "setup.py" }}
-                paths:
-                    - '~/.cache/pip'
+            - run: sudo pip install .[sklearn,tf-cpu,torch,testing]
+            - run: sudo pip install codecov pytest-cov
            - run: python -m pytest -n 8 --dist=loadfile -s ./tests/ --cov  | tee output.txt
            - run: codecov
            - store_artifacts:
@@ -98,20 +27,12 @@ jobs:
        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 .[sklearn,torch,testing]
-            - save_cache:
-                  key: v0.3-torch-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
+            - run: sudo pip install .[sklearn,torch,testing]
            - run: python -m pytest -n 8 --dist=loadfile -s ./tests/ | tee output.txt
            - store_artifacts:
                  path: ~/transformers/output.txt
                  destination: test_output.txt
+
    run_tests_tf:
        working_directory: ~/transformers
        docker:
@@ -122,16 +43,7 @@ jobs:
        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 .[sklearn,tf-cpu,testing]
-            - save_cache:
-                  key: v0.3-tf-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
+            - run: sudo pip install .[sklearn,tf-cpu,testing]
            - run: python -m pytest -n 8 --dist=loadfile -s ./tests/ | tee output.txt
            - store_artifacts:
               path: ~/transformers/output.txt
@@ -144,17 +56,7 @@ jobs:
            RUN_CUSTOM_TOKENIZERS: yes
        steps:
            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.3-custom_tokenizers-{{ checksum "setup.py" }}
-                      - v0.3-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[ja,testing]
-            - run: python -m unidic download
-            - save_cache:
-                  key: v0.3-custom_tokenizers-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
+            - run: sudo pip install .[mecab,testing]
            - run: python -m pytest -s ./tests/test_tokenization_bert_japanese.py | tee output.txt
            - store_artifacts:
                path: ~/transformers/output.txt
@@ -169,18 +71,9 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.3-torch_examples-{{ checksum "setup.py" }}
-                      - v0.3-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,torch,testing]
-            - run: pip install -r examples/requirements.txt
-            - save_cache:
-                  key: v0.3-torch_examples-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -rA -s ./examples/ | tee output.txt
+            - run: sudo pip install .[sklearn,torch,testing]
+            - run: sudo pip install -r examples/requirements.txt
+            - run: python -m pytest -n 8 --dist=loadfile -s ./examples/ | tee output.txt
            - store_artifacts:
                  path: ~/transformers/output.txt
                  destination: test_output.txt
@@ -190,16 +83,7 @@ jobs:
            - image: circleci/python:3.6
        steps:
            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.3-build_doc-{{ checksum "setup.py" }}
-                      - v0.3-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[tf,torch,docs]
-            - save_cache:
-                  key: v0.3-build_doc-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
+            - run: sudo pip install .[tf,torch,docs]
            - run: cd docs && make html SPHINXOPTS="-W"
            - store_artifacts:
                path: ./docs/_build
@@ -212,15 +96,7 @@ jobs:
                fingerprints:
                    - "5b:7a:95:18:07:8c:aa:76:4c:60:35:88:ad:60:56:71"
            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.3-deploy_doc-{{ checksum "setup.py" }}
-                      - v0.3-{{ checksum "setup.py" }}
-            - run: pip install .[tf,torch,docs]
-            - save_cache:
-                  key: v0.3-deploy_doc-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
+            - run: sudo pip install .[tf,torch,docs]
            - run: ./.circleci/deploy.sh
    check_code_quality:
        working_directory: ~/transformers
@@ -230,22 +106,12 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.3-code_quality-{{ checksum "setup.py" }}
-                      - v0.3-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
            # we need a version of isort with https://github.com/timothycrosley/isort/pull/1000
-            - run: pip install git+git://github.com/timothycrosley/isort.git@e63ae06ec7d70b06df9e528357650281a3d3ec22#egg=isort
-            - run: pip install .[tf,torch,quality]
-            - save_cache:
-                  key: v0.3-code_quality-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
+            - run: sudo pip install git+git://github.com/timothycrosley/isort.git@e63ae06ec7d70b06df9e528357650281a3d3ec22#egg=isort
+            - run: sudo pip install .[tf,torch,quality]
            - run: black --check --line-length 119 --target-version py35 examples templates tests src utils
            - run: isort --check-only --recursive examples templates tests src utils
            - run: flake8 examples templates tests src utils
-            - run: python utils/check_repo.py
    check_repository_consistency:
        working_directory: ~/transformers
        docker:
@@ -254,37 +120,8 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - run: pip install requests
+            - run: sudo pip install requests
            - run: python ./utils/link_tester.py
-
-# TPU JOBS
-    run_examples_tpu:
-        docker:
-            - image: circleci/python:3.6
-        environment:
-            OMP_NUM_THREADS: 1
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - go/install
-            - *checkout_ml_testing
-            - gcp-gke/install
-            - gcp-gke/update-kubeconfig-with-credentials:
-                  cluster: $GKE_CLUSTER
-                  perform-login: true
-            - setup_remote_docker
-            - *build_push_docker
-            - *deploy_cluster
-    cleanup-gke-jobs:
-        docker:
-            - image: circleci/python:3.6
-        steps:
-            - gcp-gke/install
-            - gcp-gke/update-kubeconfig-with-credentials:
-                  cluster: $GKE_CLUSTER
-                  perform-login: true
-            - *delete_gke_jobs
 workflow_filters: &workflow_filters
    filters:
        branches:
@@ -303,15 +140,3 @@ workflows:
            - run_tests_tf
            - build_doc
            - deploy_doc: *workflow_filters
-    tpu_testing_jobs:
-        triggers:
-            - schedule:
-                # Set to run at the first minute of every hour.
-                cron: "0 8 * * *"
-                filters:
-                    branches:
-                        only:
-                            - master
-        jobs:
-            - cleanup-gke-jobs
-            - run_examples_tpu
@@ -47,4 +47,4 @@ deploy_doc "e7cfc1a" v2.9.0
 deploy_doc "7cb203f" v2.9.1
 deploy_doc "10d7239" v2.10.0 
 deploy_doc "b42586e" v2.11.0
-deploy_doc "7fb8bdf" #v3.0.2 Latest stable release
+deploy_doc "1158e56" #v3.0.2 Latest stable release
@@ -7,53 +7,14 @@ assignees: ''

 ---

-
-## Environment info
-<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
-     Don't forget to fill out the missing fields in that output! -->
-     
- `transformers` version:
- Platform:
- Python version:
- PyTorch version (GPU?):
- Tensorflow version (GPU?):
- Using GPU in script?:
- Using distributed or parallel set-up in script?:
-
-### Who can help
-<!-- Your issue will be replied to more quickly if you can figure out the right person to tag with @
- 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, GPT2, XLM: @LysandreJik 
- tokenizers: @mfuntowicz
- Trainer: @sgugger
- 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: @TevenLeScao
- blenderbot: @mariamabarham
- Bart: @sshleifer
- Marian: @sshleifer
- T5: @patrickvonplaten
- Longformer/Reformer: @patrickvonplaten
- TransfoXL/XLNet: @TevenLeScao 
- examples/seq2seq: @sshleifer
- examples/bert-loses-patience: @JetRunner
- tensorflow: @jplu
- examples/token-classification: @stefan-it
- documentation: @sgugger
- -->
+# 🐛 Bug

 ## Information

 Model I am using (Bert, XLNet ...):

+Language I am using the model on (English, Chinese ...):
+
 The problem arises when using:
 * [ ] the official example scripts: (give details below)
 * [ ] my own modified scripts: (give details below)
@@ -77,3 +38,15 @@ Steps to reproduce the behavior:
 ## Expected behavior

 <!-- A clear and concise description of what you would expect to happen. -->
+
+## Environment info
+<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
+     Don't forget to fill out the missing fields in that output! -->
+     
+- `transformers` version:
+- Platform:
+- Python version:
+- PyTorch version (GPU?):
+- Tensorflow version (GPU?):
+- Using GPU in script?:
+- Using distributed or parallel set-up in script?:
@@ -24,4 +24,4 @@ assignees: ''

 <!-- You should first ask your question on the forum or SO, and only if
     you didn't get an answer ask it here on GitHub. -->
-**A link to original question on the forum/Stack Overflow**:
+**A link to original question on Stack Overflow**:
@@ -0,0 +1,19 @@
+name: GitHub-hosted runner
+
+on: push
+
+jobs:
+  check_code_quality:
+    runs-on: ubuntu-18.04
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: 3.7
+    # - name: Install dependencies
+    #   run: |
+    #     pip install .[tf,torch,quality]
+
+
+
@@ -18,17 +18,8 @@ jobs:
      uses: actions/setup-python@v1
      with:
        python-version: 3.7
-
-    - name: Loading cache
-      uses: actions/cache@v2
-      id: cache
-      with:
-        path: ~/.cache/pip
-        key: v0-torch_hub-${{ hashFiles('setup.py') }}
-
    - name: Install dependencies
      run: |
-        pip install --upgrade pip
        pip install torch
        pip install numpy tokenizers filelock requests tqdm regex sentencepiece sacremoses packaging

@@ -25,14 +25,6 @@ jobs:
    - name: Current dir
      run: pwd
    - run: nvidia-smi
-
-    - name: Loading cache.
-      uses: actions/cache@v2
-      id: cache
-      with:
-        path: .env
-        key: v0-tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
-
    - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
      run: |
        python -m venv .env
@@ -43,8 +35,7 @@ jobs:
    - name: Install dependencies
      run: |
        source .env/bin/activate
-        pip install --upgrade pip
-        pip install torch!=1.6.0
+        pip install torch
        pip install .[sklearn,testing]

    - name: Are GPUs recognized by our DL frameworks
@@ -13,14 +13,6 @@ jobs:
    runs-on: self-hosted
    steps:
    - uses: actions/checkout@v2
-
-    - name: Loading cache.
-      uses: actions/cache@v2
-      id: cache
-      with:
-        path: .env
-        key: v0-slow_tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
-
    - name: Python version
      run: |
        which python
@@ -30,7 +22,6 @@ jobs:
      run: pwd
    - run: nvidia-smi
    - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-      if: steps.cache.outputs.cache-hit != 'true'
      run: |
        python -m venv .env
        source .env/bin/activate
@@ -40,9 +31,7 @@ jobs:
    - name: Install dependencies
      run: |
        source .env/bin/activate
-        pip install --upgrade pip
-        pip install torch!=1.6.0
-        pip install .[sklearn,testing]
+        pip install .[sklearn,torch,testing]

    - name: Are GPUs recognized by our DL frameworks
      run: |
@@ -58,13 +47,3 @@ jobs:
      run: |
        source .env/bin/activate
        python -m pytest -n 1 --dist=loadfile -s ./tests/
-    - name: Run examples tests on GPU
-      env:
-        TF_FORCE_GPU_ALLOW_GROWTH: "true"
-        OMP_NUM_THREADS: 1
-        RUN_SLOW: yes
-        USE_CUDA: yes
-      run: |
-        source .env/bin/activate
-        pip install -r examples/requirements.txt
-        python -m pytest -n 1 --dist=loadfile -s examples
@@ -65,7 +65,7 @@ Awesome! Please provide the following information:
 If you are willing to contribute the model yourself, let us know so we can best
 guide you.

-We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them
+We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them 
 in the [`templates`](https://github.com/huggingface/transformers/tree/master/templates) folder.

 ### Do you want a new feature (that is not a model)?
@@ -87,8 +87,8 @@ A world-class feature request addresses the following points:
 If your issue is well written we're already 80% of the way there by the time you
 post it.

-We have added **templates** to guide you in the process of adding a new example script for training or testing the
-models in the library. You can find them in the [`templates`](https://github.com/huggingface/transformers/tree/master/templates)
+We have added **templates** to guide you in the process of adding a new example script for training or testing the 
+models in the library. You can find them in the [`templates`](https://github.com/huggingface/transformers/tree/master/templates) 
 folder.

 ## Start contributing! (Pull Requests)
@@ -149,14 +149,6 @@ Follow these steps to start contributing:
   $ make test
   ```

-   Note, that this command uses `-n auto` pytest flag, therefore, it will start as many parallel `pytest` processes as the number of your computer's CPU-cores, and if you have lots of those and a few GPUs and not a great amount of RAM, it's likely to overload your computer. Therefore, to run the test suite, you may want to consider using this command instead:
-
-   ```bash
-   $ python -m pytest -n 3 --dist=loadfile -s -v ./tests/
-   ```
-
-   Adjust the value of `-n` to fit the load your hardware can support.
-
   `transformers` relies on `black` and `isort` to format its source code
   consistently. After you make changes, format them with:

@@ -216,21 +208,21 @@ Follow these steps to start contributing:
   are useful to avoid duplicated work, and to differentiate it from PRs ready
   to be merged;
 4. Make sure existing tests pass;
-5. Add high-coverage tests. No quality testing = no merge.
-   - If you are adding a new model, make sure that you use
+5. Add high-coverage tests. No quality testing = no merge. 
+   - If you are adding a new model, make sure that you use 
     `ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)`, which triggers the common tests.
-   - If you are adding new `@slow` tests, make sure they pass using
-     `RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`.
-   - If you are adding a new tokenizer, write tests, and make sure
+   - If you are adding new `@slow` tests, make sure they pass using 
+     `RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`. 
+   - If you are adding a new tokenizer, write tests, and make sure 
     `RUN_SLOW=1 python -m pytest tests/test_tokenization_{your_model_name}.py` passes.
-   CircleCI does not run the slow tests, but github actions does every night!
-6. All public methods must have informative docstrings that work nicely with sphinx. See `modeling_ctrl.py` for an
+   CircleCI does not run the slow tests. 
+6. All public methods must have informative docstrings that work nicely with sphinx. See `modeling_ctrl.py` for an 
   example.

 ### Tests

-An extensive test suite is included to test the library behavior and several examples. Library tests can be found in
-the [tests folder](https://github.com/huggingface/transformers/tree/master/tests) and examples tests in the
+An extensive test suite is included to test the library behavior and several examples. Library tests can be found in 
+the [tests folder](https://github.com/huggingface/transformers/tree/master/tests) and examples tests in the 
 [examples folder](https://github.com/huggingface/transformers/tree/master/examples).

 We like `pytest` and `pytest-xdist` because it's faster. From the root of the
@@ -246,7 +238,8 @@ and for the examples:
 $ pip install -r examples/requirements.txt  # only needed the first time
 $ python -m pytest -n auto --dist=loadfile -s -v ./examples/
 ```
-In fact, that's how `make test` and `make test-examples` are implemented (sans the `pip install` line)!
+
+In fact, that's how `make test` and `make test-examples` are implemented!

 You can specify a smaller set of tests in order to test only the feature
 you're working on.
@@ -6,7 +6,6 @@ quality:
 	black --check --line-length 119 --target-version py35 examples templates tests src utils
 	isort --check-only --recursive examples templates tests src utils
 	flake8 examples templates tests src utils
-	python utils/check_repo.py

 # Format source code automatically

@@ -167,31 +167,19 @@ At some point in the future, you'll be able to seamlessly move from pre-training
 19. **[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.
 20. **[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.
 21. **[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.
-22. **[DPR](https://github.com/facebookresearch/DPR)** (from Facebook) released with the paper [Dense Passage Retrieval
-for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon
-Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
-23. **[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.
-24. **[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.  
-25. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
-26. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.
+22. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
+23. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.

 These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations (e.g. ~93 F1 on SQuAD for BERT Whole-Word-Masking, ~88 F1 on RocStories for OpenAI GPT, ~18.3 perplexity on WikiText 103 for Transformer-XL, ~0.916 Pearson R coefficient on STS-B for XLNet). You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/transformers/examples.html).

 ## Online demo

-You can test our inference API on most model pages from the model hub: https://huggingface.co/models
-
-For example: 
- [Masked word completion with BERT](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
- [NER with Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
- [Text generation with GPT-2](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
- [NLI with RoBERTa](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
- [Summarization with BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
- [Question answering with DistilBERT](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
- [Translation with T5](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
-
-
 **[Write With Transformer](https://transformer.huggingface.co)**, built by the Hugging Face team at transformer.huggingface.co, is the official demo of this repo’s text generation capabilities.
+You can use it to experiment with completions generated by `GPT2Model`, `TransfoXLModel`, and `XLNetModel`.
+
+> “🦄 Write with transformer is to writing what calculators are to calculus.”
+
+![write_with_transformer](https://transformer.huggingface.co/front/assets/thumbnail-large.png)

 ## Quick tour

@@ -4,7 +4,3 @@ coverage:
      default:
        informational: true
    patch: off
-comment:
-  require_changes: true    # only comment if there was change in coverage
-  require_head: yes        # don't report if there is no head coverage report
-  require_base: yes        # don't report if there is no base coverage report
@@ -0,0 +1,23 @@
+cd docs
+
+function deploy_doc(){
+	echo "Creating doc at commit $1 and pushing to folder $2"
+	git checkout $1
+	if [ ! -z "$2" ] 
+	then
+		echo "Pushing version" $2
+		make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html $doc:$dir/$2
+	else
+		echo "Pushing master"
+		make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir
+	fi
+}
+
+deploy_doc "master" 
+deploy_doc "b33a385" v1.0.0
+deploy_doc "fe02e45" v1.1.0
+deploy_doc "89fd345" v1.2.0
+deploy_doc "fc9faa8" v2.0.0
+deploy_doc "3ddce1d" v2.1.1
+deploy_doc "f2f3294" v2.2.0
+deploy_doc "d0f8b9a" v2.3.0
@@ -1,65 +0,0 @@
-FROM google/cloud-sdk:slim
-
-# Build args.
-ARG GITHUB_REF=refs/heads/master
-
-# TODO: This Dockerfile installs pytorch/xla 3.6 wheels. There are also 3.7
-# wheels available; see below.
-ENV PYTHON_VERSION=3.6
-
-RUN apt-get update && apt-get install -y --no-install-recommends \
-         build-essential \
-         cmake \
-         git \
-         curl \
-         ca-certificates
-
-# Install conda and python.
-# NOTE new Conda does not forward the exit status... https://github.com/conda/conda/issues/8385
-RUN curl -o ~/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-4.7.12-Linux-x86_64.sh  && \
-    chmod +x ~/miniconda.sh && \
-    ~/miniconda.sh -b && \
-    rm ~/miniconda.sh
-
-ENV PATH=/root/miniconda3/bin:$PATH
-
-RUN conda create -y --name container python=$PYTHON_VERSION
-
-# Run the rest of commands within the new conda env.
-# Use absolute path to appease Codefactor.
-SHELL ["/root/miniconda3/bin/conda", "run", "-n", "container", "/bin/bash", "-c"]
-RUN conda install -y python=$PYTHON_VERSION mkl
-
-RUN pip uninstall -y torch && \
-    # Python 3.7 wheels are available. Replace cp36-cp36m with cp37-cp37m
-    gsutil cp 'gs://tpu-pytorch/wheels/torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \
-    gsutil cp 'gs://tpu-pytorch/wheels/torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \
-    gsutil cp 'gs://tpu-pytorch/wheels/torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \
-    pip install 'torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
-    pip install 'torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
-    pip install 'torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
-    rm 'torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
-    rm 'torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
-    rm 'torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
-    apt-get install -y libomp5
-
-ENV LD_LIBRARY_PATH=root/miniconda3/envs/container/lib
-
-
-# Install huggingface/transformers at the current PR, plus dependencies.
-RUN git clone https://github.com/huggingface/transformers.git && \
-    cd transformers && \
-    git fetch origin $GITHUB_REF:CI && \
-    git checkout CI && \
-    cd .. && \
-    pip install ./transformers && \
-    pip install -r ./transformers/examples/requirements.txt && \
-    pip install pytest
-
-RUN python -c "import torch_xla; print(torch_xla.__version__)"
-RUN python -c "import transformers as trf; print(trf.__version__)"
-RUN conda init bash
-COPY docker-entrypoint.sh /usr/local/bin/
-RUN chmod +x /usr/local/bin/docker-entrypoint.sh
-ENTRYPOINT ["/usr/local/bin/docker-entrypoint.sh"]
-CMD ["bash"]
@@ -1,38 +0,0 @@
-local base = import 'templates/base.libsonnet';
-local tpus = import 'templates/tpus.libsonnet';
-local utils = import "templates/utils.libsonnet";
-local volumes = import "templates/volumes.libsonnet";
-
-local bertBaseCased = base.BaseTest {
-  frameworkPrefix: "hf",
-  modelName: "bert-base-cased",
-  mode: "example",
-  configMaps: [],
-
-  timeout: 3600, # 1 hour, in seconds
-
-  image: std.extVar('image'),
-  imageTag: std.extVar('image-tag'),
-
-  tpuSettings+: {
-    softwareVersion: "pytorch-nightly",
-  },
-  accelerator: tpus.v3_8,
-
-  volumeMap+: {
-    datasets: volumes.PersistentVolumeSpec {
-      name: "huggingface-cluster-disk",
-      mountPath: "/datasets",
-    },
-  },
-  command: utils.scriptCommand(
-    |||
-      python -m pytest -s transformers/examples/test_xla_examples.py -v
-      test_exit_code=$?
-      echo "\nFinished running commands.\n"
-      test $test_exit_code -eq 0
-    |||
-  ),
-};
-
-bertBaseCased.oneshotJob
@@ -1,32 +0,0 @@
-apiVersion: v1
-kind: PersistentVolume
-metadata:
-  name: huggingface-cluster-disk
-spec:
-  storageClassName: ""
-  capacity:
-    storage: 500Gi
-  accessModes:
-    - ReadOnlyMany
-  claimRef:
-    namespace: default
-    name: huggingface-cluster-disk-claim
-  gcePersistentDisk:
-    pdName: huggingface-cluster-disk
-    fsType: ext4
-    readOnly: true
---
-apiVersion: v1
-kind: PersistentVolumeClaim
-metadata:
-  name: huggingface-cluster-disk-claim
-spec:
-  # Specify "" as the storageClassName so it matches the PersistentVolume's StorageClass.
-  # A nil storageClassName value uses the default StorageClass. For details, see
-  # https://kubernetes.io/docs/concepts/storage/persistent-volumes/#class-1
-  storageClassName: ""
-  accessModes:
-    - ReadOnlyMany
-  resources:
-    requests:
-      storage: 1Ki
@@ -1,8 +0,0 @@
-#!/bin/bash
-source ~/.bashrc
-echo "running docker-entrypoint.sh"
-conda activate container
-echo $KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS
-echo "printed TPU info"
-export XRT_TPU_CONFIG="tpu_worker;0;${KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS:7}"
-exec "$@"#!/bin/bash
@@ -1,36 +1,5 @@
 /* Our DOM objects */

-/* Colab dropdown */
-
-.colab-dropdown {
-    position: relative;
-    display: inline-block;
-}
-  
-.colab-dropdown-content {
-    display: none;
-    position: absolute;
-    background-color: #f9f9f9;
-    min-width: 117px;
-    box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
-    z-index: 1;
-}
-  
-.colab-dropdown-content button {
-    color: #6670FF;
-    background-color: #f9f9f9;
-    font-size: 12px;
-    border: none;
-    min-width: 117px;
-    padding: 5px 5px;
-    text-decoration: none;
-    display: block;
-}
-  
-.colab-dropdown-content button:hover {background-color: #eee;}
-  
-.colab-dropdown:hover .colab-dropdown-content {display: block;}
-
 /* Version control */

 .version-button {
@@ -21,18 +21,6 @@ const versionMapping = {
    "v1.1.0": "v1.1.0",
    "v1.0.0": "v1.0.0"
 }
-// The page that have a notebook and therefore should have the open in colab badge.
-const hasNotebook = [
-    "benchmarks",
-    "custom_datasets",
-    "multilingual",
-    "perplexity",
-    "preprocessing",
-    "quicktour",
-    "task_summary",
-    "tokenizer_summary",
-    "training"
-];

 function addIcon() {
    const huggingFaceLogo = "https://huggingface.co/landing/assets/transformers-docs/huggingface_logo.svg";
@@ -94,26 +82,6 @@ function addGithubButton() {
    document.querySelector(".wy-side-nav-search .icon-home").insertAdjacentHTML('afterend', div);
 }

-function addColabLink() {
-    const parts = location.toString().split('/');
-    const pageName = parts[parts.length - 1].split(".")[0];
-
-    if (hasNotebook.includes(pageName)) {
-        const baseURL = "https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/"
-        const linksColab = `
-        <div class="colab-dropdown">
-            <img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg">
-            <div class="colab-dropdown-content">
-                <button onclick=" window.open('${baseURL}${pageName}.ipynb')">Mixed</button>
-                <button onclick=" window.open('${baseURL}pytorch/${pageName}.ipynb')">PyTorch</button>
-                <button onclick=" window.open('${baseURL}tensorflow/${pageName}.ipynb')">TensorFlow</button>
-            </div>
-        </div>`
-        const leftMenu = document.querySelector(".wy-breadcrumbs-aside")
-        leftMenu.innerHTML = linksColab + '\n' + leftMenu.innerHTML
-    }
-}
-
 function addVersionControl() {
    // To grab the version currently in view, we parse the url
    const parts = location.toString().split('/');
@@ -287,7 +255,6 @@ function onLoad() {
    addGithubButton();
    parseGithubButtons();
    addHfMenu();
-    addColabLink();
    platformToggle();
 }

@@ -40,12 +40,12 @@ There are many more parameters that can be configured via the benchmark argument
 ``src/transformers/benchmark/benchmark_args_utils.py``, ``src/transformers/benchmark/benchmark_args.py`` (for PyTorch) and ``src/transformers/benchmark/benchmark_args_tf.py`` (for Tensorflow). 
 Alternatively, running the following shell commands from root will print out a descriptive list of all configurable parameters for PyTorch and Tensorflow respectively.

-.. code-block:: bash
+.. code-block::

-    ## PYTORCH CODE
+    >>> ## PYTORCH CODE
    python examples/benchmarking/run_benchmark.py --help

-    ## TENSORFLOW CODE
+    >>> ## TENSORFLOW CODE
    python examples/benchmarking/run_benchmark_tf.py --help


@@ -76,8 +76,7 @@ exclude_patterns = [u'_build', 'Thumbs.db', '.DS_Store']
 pygments_style = None

 # Remove the prompt when copying examples
-copybutton_prompt_text = r">>> |\.\.\. "
-copybutton_prompt_is_regexp = True
+copybutton_prompt_text = ">>> "

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

@@ -1,715 +0,0 @@
-Fine-tuning with custom datasets
-================================
-
-.. note::
-
-    The datasets used in this tutorial are available and can be more easily accessed using the
-    `🤗 NLP library <https://github.com/huggingface/nlp>`_. We do not use this library to access the datasets here
-    since this tutorial meant to illustrate how to work with your own data. A brief of introduction can be found
-    at the end of the tutorial in the section ":ref:`nlplib`".
-
-This tutorial will take you through several examples of using 🤗 Transformers models with your own datasets. The
-guide shows one of many valid workflows for using these models and is meant to be illustrative rather than
-definitive. We show examples of reading in several data formats, preprocessing the data for several types of tasks,
-and then preparing the data into PyTorch/TensorFlow ``Dataset`` objects which can easily be used either with
-:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` or with native PyTorch/TensorFlow.
-
-We include several examples, each of which demonstrates a different type of common downstream task:
-
-  - :ref:`seq_imdb`
-  - :ref:`tok_ner`
-  - :ref:`qa_squad`
-  - :ref:`resources`
-
-.. _seq_imdb:
-
-Sequence Classification with IMDb Reviews
-----------------------------------------
-
-.. note::
-
-    This dataset can be explored in the Hugging Face model hub (`IMDb <https://huggingface.co/datasets/imdb>`_), and can
-    be alternatively downloaded with the 🤗 NLP library with ``load_dataset("imdb")``.
-
-In this example, we'll show how to download, tokenize, and train a model on the IMDb reviews dataset. This task
-takes the text of a review and requires the model to predict whether the sentiment of the review is positive or
-negative. Let's start by downloading the dataset from the
-`Large Movie Review Dataset <http://ai.stanford.edu/~amaas/data/sentiment/>`_ webpage.
-
-.. code-block:: bash
-
-    wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
-    tar -xf aclImdb_v1.tar.gz
-
-This data is organized into ``pos`` and ``neg`` folders with one text file per example. Let's write a function that can
-read this in.
-
-.. code-block:: python
-
-    from pathlib import Path
-
-    def read_imdb_split(split_dir):
-        split_dir = Path(split_dir)
-        texts = []
-        labels = []
-        for label_dir in ["pos", "neg"]:
-            for text_file in (split_dir/label_dir).iterdir():
-                texts.append(text_file.read_text())
-                labels.append(0 if label_dir is "neg" else 1)
-
-        return texts, labels
-
-    train_texts, train_labels = read_imdb_split('aclImdb/train')
-    test_texts, test_labels = read_imdb_split('aclImdb/test')
-
-We now have a train and test dataset, but let's also also create a validation set which we can use for for
-evaluation and tuning without training our test set results. Sklearn has a convenient utility for creating such
-splits:
-
-.. code-block:: python
-
-    from sklearn.model_selection import train_test_split
-    train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)
-
-Alright, we've read in our dataset. Now let's tackle tokenization. We'll eventually train a classifier using
-pre-trained DistilBert, so let's use the DistilBert tokenizer.
-
-.. code-block:: python
-
-    from transformers import DistilBertTokenizerFast
-    tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
-
-Now we can simply pass our texts to the tokenizer. We'll pass ``truncation=True`` and ``padding=True``, which will
-ensure that all of our sequences are padded to the same length and are truncated to be no longer model's maximum
-input length. This will allow us to feed batches of sequences into the model at the same time.
-
-.. code-block:: python
-
-    train_encodings = tokenizer(train_texts, truncation=True, padding=True)
-    val_encodings = tokenizer(val_texts, truncation=True, padding=True)
-    test_encodings = tokenizer(test_texts, truncation=True, padding=True)
-
-Now, let's turn our labels and encodings into a Dataset object. In PyTorch, this is done by subclassing a
-``torch.utils.data.Dataset`` object and implementing ``__len__`` and ``__getitem__``. In TensorFlow, we pass our input encodings and
-labels to the ``from_tensor_slices`` constructor method. We put the data in this format so that the data can be
-easily batched such that each key in the batch encoding corresponds to a named parameter of the
-:meth:`~transformers.DistilBertForSequenceClassification.forward` method of the model we will train.
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    import torch
-
-    class IMDbDataset(torch.utils.data.Dataset):
-        def __init__(self, encodings, labels):
-            self.encodings = encodings
-            self.labels = labels
-
-        def __getitem__(self, idx):
-            item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
-            item['labels'] = torch.tensor(self.labels[idx])
-            return item
-
-        def __len__(self):
-            return len(self.labels)
-
-    train_dataset = IMDbDataset(train_encodings, train_labels)
-    val_dataset = IMDbDataset(val_encodings, val_labels)
-    test_dataset = IMDbDataset(test_encodings, test_labels)
-    ## TENSORFLOW CODE
-    import tensorflow as tf
-
-    train_dataset = tf.data.Dataset.from_tensor_slices((
-        dict(train_encodings),
-        train_labels
-    ))
-    val_dataset = tf.data.Dataset.from_tensor_slices((
-        dict(val_encodings),
-        val_labels
-    ))
-    test_dataset = tf.data.Dataset.from_tensor_slices((
-        dict(test_encodings),
-        test_labels
-    ))
-
-Now that our datasets our ready, we can fine-tune a model either with the 🤗
-:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` or with native PyTorch/TensorFlow. See
-:doc:`training <training>`.
-
-.. _ft_trainer:
-
-Fine-tuning with Trainer
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-The steps above prepared the datasets in the way that the trainer is expected. Now all we need to do is create a
-model to fine-tune, define the :class:`~transformers.TrainingArguments`/:class:`~transformers.TFTrainingArguments`
-and instantiate a :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer`.
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    from transformers import DistilBertForSequenceClassification, Trainer, TrainingArguments
-
-    training_args = TrainingArguments(
-        output_dir='./results',          # output directory
-        num_train_epochs=3,              # total number of training epochs
-        per_device_train_batch_size=16,  # batch size per device during training
-        per_device_eval_batch_size=64,   # batch size for evaluation
-        warmup_steps=500,                # number of warmup steps for learning rate scheduler
-        weight_decay=0.01,               # strength of weight decay
-        logging_dir='./logs',            # directory for storing logs
-        logging_steps=10,
-    )
-
-    model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
-
-    trainer = Trainer(
-        model=model,                         # the instantiated 🤗 Transformers model to be trained
-        args=training_args,                  # training arguments, defined above
-        train_dataset=train_dataset,         # training dataset
-        eval_dataset=val_dataset             # evaluation dataset
-    )
-
-    trainer.train()
-    ## TENSORFLOW CODE
-    from transformers import TFDistilBertForSequenceClassification, TFTrainer, TFTrainingArguments
-
-    training_args = TFTrainingArguments(
-        output_dir='./results',          # output directory
-        num_train_epochs=3,              # total number of training epochs
-        per_device_train_batch_size=16,  # batch size per device during training
-        per_device_eval_batch_size=64,   # batch size for evaluation
-        warmup_steps=500,                # number of warmup steps for learning rate scheduler
-        weight_decay=0.01,               # strength of weight decay
-        logging_dir='./logs',            # directory for storing logs
-        logging_steps=10,
-    )
-
-    with training_args.strategy.scope():
-        model = TFDistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
-
-    trainer = TFTrainer(
-        model=model,                         # the instantiated 🤗 Transformers model to be trained
-        args=training_args,                  # training arguments, defined above
-        train_dataset=train_dataset,         # training dataset
-        eval_dataset=val_dataset             # evaluation dataset
-    )
-
-    trainer.train()
-
-.. _ft_native:
-
-Fine-tuning with native PyTorch/TensorFlow
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-We can also train use native PyTorch or TensorFlow:
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    from torch.utils.data import DataLoader
-    from transformers import DistilBertForSequenceClassification, AdamW
-
-    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-
-    model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
-    model.to(device)
-    model.train()
-
-    train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
-
-    optim = AdamW(model.parameters(), lr=5e-5)
-
-    for epoch in range(3):
-        for batch in train_loader:
-            optim.zero_grad()
-            input_ids = batch['input_ids'].to(device)
-            attention_mask = batch['attention_mask'].to(device)
-            labels = batch['labels'].to(device)
-            outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
-            loss = outputs[0]
-            loss.backward()
-            optim.step()
-
-    model.eval()
-    ## TENSORFLOW CODE
-    from transformers import TFDistilBertForSequenceClassification
-
-    model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
-
-    optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
-    model.compile(optimizer=optimizer, loss=model.compute_loss) # can also use any keras loss fn
-    model.fit(train_dataset.shuffle(1000).batch(16), epochs=3, batch_size=16)
-
-.. _tok_ner:
-
-Token Classification with W-NUT Emerging Entities
-------------------------------------------------
-
-.. note::
-
-    This dataset can be explored in the Hugging Face model hub (`WNUT-17 <https://huggingface.co/datasets/wnut_17>`_), and can
-    be alternatively downloaded with the 🤗 NLP library with ``load_dataset("wnut_17")``.
-
-Next we will look at token classification. Rather than classifying an entire sequence, this task classifies token by
-token. We'll demonstrate how to do this with 
-`Named Entity Recognition <http://nlpprogress.com/english/named_entity_recognition.html>`_, which involves
-identifying tokens which correspond to a predefined set of "entities". Specifically, we'll use the
-`W-NUT Emerging and Rare entities <http://noisy-text.github.io/2017/emerging-rare-entities.html>`_ corpus. The data
-is given as a collection of pre-tokenized documents where each token is assigned a tag.
-
-Let's start by downloading the data.
-
-.. code-block:: bash
-
-    wget http://noisy-text.github.io/2017/files/wnut17train.conll
-
-In this case, we'll just download the train set, which is a single text file. Each line of the file contains either
-(1) a word and tag separated by a tab, or (2) a blank line indicating the end of a document. Let's write a
-function to read this in. We'll take in the file path and return ``token_docs`` which is a list of lists of token
-strings, and ``token_tags`` which is a list of lists of tag strings.
-
-.. code-block:: python
-
-    from pathlib import Path
-    import re
-
-    def read_wnut(file_path):
-        file_path = Path(file_path)
-
-        raw_text = file_path.read_text().strip()
-        raw_docs = re.split(r'\n\t?\n', raw_text)
-        token_docs = []
-        tag_docs = []
-        for doc in raw_docs:
-            tokens = []
-            tags = []
-            for line in doc.split('\n'):
-                token, tag = line.split('\t')
-                tokens.append(token)
-                tags.append(tag)
-            token_docs.append(tokens)
-            tag_docs.append(tags)
-        
-        return token_docs, tag_docs
-    
-    texts, tags = read_wnut('wnut17train.conll')
-    
-Just to see what this data looks like, let's take a look at a segment of the first document.
-
-.. code-block:: python
-
-    >>> print(texts[0][10:17], tags[0][10:17], sep='\n')
-    ['for', 'two', 'weeks', '.', 'Empire', 'State', 'Building']
-    ['O', 'O', 'O', 'O', 'B-location', 'I-location', 'I-location']
-
-``location`` is an entity type, ``B-`` indicates the beginning of an entity, and ``I-`` indicates consecutive positions of
-the same entity ("Empire State Building" is considered one entity). ``O`` indicates the token does not correspond to
-any entity.
-
-Now that we've read the data in, let's create a train/validation split:
-
-.. code-block:: python
-
-    from sklearn.model_selection import train_test_split
-    train_texts, val_texts, train_tags, val_tags = train_test_split(texts, tags, test_size=.2)
-
-Next, let's create encodings for our tokens and tags. For the tags, we can start by just create a simple mapping
-which we'll use in a moment:
-
-.. code-block:: python
-
-    unique_tags = set(tag for doc in tags for tag in doc)
-    tag2id = {tag: id for id, tag in enumerate(unique_tags)}
-    id2tag = {id: tag for tag, id in tag2id.items()}
-
-To encode the tokens, we'll use a pre-trained DistilBert tokenizer. We can tell the tokenizer that we're dealing
-with ready-split tokens rather than full sentence strings by passing ``is_pretokenized=True``. We'll also pass
-``padding=True`` and ``truncation=True`` to pad the sequences to be the same length. Lastly, we can tell the model
-to return information about the tokens which are split by the wordpiece tokenization process, which we will need in
-a moment.
-
-.. code-block:: python
-
-    from transformers import DistilBertTokenizerFast
-    tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-cased')
-    train_encodings = tokenizer(train_texts, is_pretokenized=True, return_offsets_mapping=True, padding=True, truncation=True)
-    val_encodings = tokenizer(val_texts, is_pretokenized=True, return_offsets_mapping=True, padding=True, truncation=True)
-
-Great, so now our tokens are nicely encoded in the format that they need to be in to feed them into our DistilBert
-model below.
-
-Now we arrive at a common obstacle with using pre-trained models for token-level classification: many of the tokens
-in the W-NUT corpus are not in DistilBert's vocabulary. Bert and many models like it use a method called WordPiece
-Tokenization, meaning that single words are split into multiple tokens such that each token is likely to be in
-the vocabulary. For example, DistilBert's tokenizer would split the Twitter handle ``@huggingface`` into the tokens
-``['@', 'hugging', '##face']``. This is a problem for us because we have exactly one tag per token. If the tokenizer
-splits a token into multiple sub-tokens, then we will end up with a mismatch between our tokens and our labels.
-
-One way to handle this is to only train on the tag labels for the first subtoken of a split token. We can do this in
-🤗 Transformers by setting the labels we wish to ignore to ``-100``. In the example above, if the label for
-``@HuggingFace`` is ``3`` (indexing ``B-corporation``), we would set the labels of ``['@', 'hugging', '##face']`` to
-``[3, -100, -100]``.
-
-Let's write a function to do this. This is where we will use the ``offset_mapping`` from the tokenizer as mentioned
-above. For each sub-token returned by the tokenizer, the offset mapping gives us a tuple indicating the sub-token's
-start position and end position relative to the original token it was split from. That means that if the first
-position in the tuple is anything other than ``0``, we will set its corresponding label to ``-100``. While we're at
-it, we can also set labels to ``-100`` if the second position of the offset mapping is ``0``, since this means it must
-be a special token like ``[PAD]`` or ``[CLS]``.
-
-.. note:: 
-
-    Due to a recently fixed bug, -1 must be used instead of -100 when using TensorFlow in 🤗 Transformers <= 3.02.
-
-.. code-block:: python
-
-    import numpy as np
-
-    def encode_tags(tags, encodings):
-        labels = [[tag2id[tag] for tag in doc] for doc in tags]
-        encoded_labels = []
-        for doc_labels, doc_offset in zip(labels, encodings.offset_mapping):
-            # create an empty array of -100
-            doc_enc_labels = np.ones(len(doc_offset),dtype=int) * -100
-            arr_offset = np.array(doc_offset)
-
-            # set labels whose first offset position is 0 and the second is not 0
-            doc_enc_labels[(arr_offset[:,0] == 0) & (arr_offset[:,1] != 0)] = doc_labels
-            encoded_labels.append(doc_enc_labels.tolist())
-
-        return encoded_labels
-    
-    train_labels = encode_tags(train_tags, train_encodings)
-    val_labels = encode_tags(val_tags, val_encodings)
-
-The hard part is now done. Just as in the sequence classification example above, we can create a dataset object:
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    import torch
-
-    class WNUTDataset(torch.utils.data.Dataset):
-        def __init__(self, encodings, labels):
-            self.encodings = encodings
-            self.labels = labels
-
-        def __getitem__(self, idx):
-            item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
-            item['labels'] = torch.tensor(self.labels[idx])
-            return item
-
-        def __len__(self):
-            return len(self.labels)
-
-    train_encodings.pop("offset_mapping") # we don't want to pass this to the model
-    val_encodings.pop("offset_mapping")
-    train_dataset = WNUTDataset(train_encodings, train_labels)
-    val_dataset = WNUTDataset(val_encodings, val_labels)
-    ## TENSORFLOW CODE
-    import tensorflow as tf
-
-    train_encodings.pop("offset_mapping") # we don't want to pass this to the model
-    val_encodings.pop("offset_mapping")
-
-    train_dataset = tf.data.Dataset.from_tensor_slices((
-        dict(train_encodings),
-        train_labels
-    ))
-    val_dataset = tf.data.Dataset.from_tensor_slices((
-        dict(val_encodings),
-        val_labels
-    ))
-
-Now load in a token classification model and specify the number of labels:
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    from transformers import DistilBertForTokenClassification
-    model = DistilBertForTokenClassification.from_pretrained('distilbert-base-cased', num_labels=len(unique_tags))
-    ## TENSORFLOW CODE
-    from transformers import TFDistilBertForTokenClassification
-    model = TFDistilBertForTokenClassification.from_pretrained('distilbert-base-cased', num_labels=len(unique_tags))
-
-The data and model are both ready to go. You can train the model either with
-:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` or with native PyTorch/TensorFlow, exactly as in the
-sequence classification example above.
-
-  - :ref:`ft_trainer`
-  - :ref:`ft_native`
-
-.. _qa_squad:
-
-Question Answering with SQuAD 2.0
---------------------------------
-
-.. note::
-
-    This dataset can be explored in the Hugging Face model hub (`SQuAD V2 <https://huggingface.co/datasets/squad_v2>`_), and can
-    be alternatively downloaded with the 🤗 NLP library with ``load_dataset("squad_v2")``.
-
-Question answering comes in many forms. In this example, we'll look at the particular type of extractive QA that
-involves answering a question about a passage by highlighting the segment of the passage that answers the question.
-This involves fine-tuning a model which predicts a start position and an end position in the passage. We will use the
-`Stanford Question Answering Dataset (SQuAD) 2.0 <https://rajpurkar.github.io/SQuAD-explorer/>`_.
-
-We will start by downloading the data:
-
-.. code-block:: bash
-
-    mkdir squad
-    wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json -O squad/train-v2.0.json
-    wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json -O squad/dev-v2.0.json
-
-Each split is in a structured json file with a number of questions and answers for each passage (or context). We'll
-take this apart into parallel lists of contexts, questions, and answers (note that the contexts here are repeated
-since there are multiple questions per context):
-
-.. code-block:: python
-
-    import json
-    from pathlib import Path
-
-    def read_squad(path):
-        path = Path(path)
-        with open(path, 'rb') as f:
-            squad_dict = json.load(f)
-
-        contexts = []
-        questions = []
-        answers = []
-        for group in squad_dict['data']:
-            for passage in group['paragraphs']:
-                context = passage['context']
-                for qa in passage['qas']:
-                    question = qa['question']
-                    for answer in qa['answers']:
-                        contexts.append(context)
-                        questions.append(question)
-                        answers.append(answer)
-
-        return contexts, questions, answers
-    
-    train_contexts, train_questions, train_answers = read_squad('squad/train-v2.0.json')
-    val_contexts, val_questions, val_answers = read_squad('squad/dev-v2.0.json')
-
-The contexts and questions are just strings. The answers are dicts containing the subsequence of the passage with
-the correct answer as well as an integer indicating the character at which the answer begins. In order to train a
-model on this data we need (1) the tokenized context/question pairs, and (2) integers indicating at which *token*
-positions the answer begins and ends.
-
-First, let's get the *character* position at which the answer ends in the passage (we are given the starting
-position). Sometimes SQuAD answers are off by one or two characters, so we will also adjust for that.
-
-.. code-block:: python
-
-    def add_end_idx(answers, contexts):
-        for answer, context in zip(answers, contexts):
-            gold_text = answer['text']
-            start_idx = answer['answer_start']
-            end_idx = start_idx + len(gold_text)
-            
-            # sometimes squad answers are off by a character or two – fix this
-            if context[start_idx:end_idx] == gold_text:
-                answer['answer_end'] = end_idx
-            elif context[start_idx-1:end_idx-1] == gold_text:
-                answer['answer_start'] = start_idx - 1
-                answer['answer_end'] = end_idx - 1     # When the gold label is off by one character
-            elif context[start_idx-2:end_idx-2] == gold_text:
-                answer['answer_start'] = start_idx - 2
-                answer['answer_end'] = end_idx - 2     # When the gold label is off by two characters
-
-    add_end_idx(train_answers, train_contexts)
-    add_end_idx(val_answers, val_contexts)
-
-Now ``train_answers`` and ``val_answers`` include the character end positions and the corrected start positions.
-Next, let's tokenize our context/question pairs. 🤗 Tokenizers can accept parallel lists of sequences and encode
-them together as sequence pairs.
-
-.. code-block:: python
-
-    from transformers import DistilBertTokenizerFast
-    tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
-
-    train_encodings = tokenizer(train_contexts, train_questions, truncation=True, padding=True)
-    val_encodings = tokenizer(val_contexts, val_questions, truncation=True, padding=True)
-
-Next we need to convert our character start/end positions to token start/end positions. When using 🤗 Fast
-Tokenizers, we can use the built in :func:`~transformers.BatchEncoding.char_to_token` method.
-
-.. code-block:: python
-
-    def add_token_positions(encodings, answers):
-        start_positions = []
-        end_positions = []
-        for i in range(len(answers)):
-            start_positions.append(encodings.char_to_token(i, answers[i]['answer_start']))
-            end_positions.append(encodings.char_to_token(i, answers[i]['answer_end'] - 1))
-            # if None, the answer passage has been truncated
-            if start_positions[-1] is None:
-                start_positions[-1] = tokenizer.model_max_length
-            if end_positions[-1] is None:
-                end_positions[-1] = tokenizer.model_max_length
-        encodings.update({'start_positions': start_positions, 'end_positions': end_positions})
-
-    add_token_positions(train_encodings, train_answers)
-    add_token_positions(val_encodings, val_answers)
-
-Our data is ready. Let's just put it in a PyTorch/TensorFlow dataset so that we can easily use it for
-training. In PyTorch, we define a custom ``Dataset`` class. In TensorFlow, we pass a tuple of
-``(inputs_dict, labels_dict)`` to the ``from_tensor_slices`` method.
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    import torch
-
-    class SquadDataset(torch.utils.data.Dataset):
-        def __init__(self, encodings):
-            self.encodings = encodings
-
-        def __getitem__(self, idx):
-            return {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
-
-        def __len__(self):
-            return len(self.encodings.input_ids)
-        
-    train_dataset = SquadDataset(train_encodings)
-    val_dataset = SquadDataset(val_encodings)
-    ## TENSORFLOW CODE
-    import tensorflow as tf
-
-    train_dataset = tf.data.Dataset.from_tensor_slices((
-        {key: train_encodings[key] for key in ['input_ids', 'attention_mask']},
-        {key: train_encodings[key] for key in ['start_positions', 'end_positions']}
-    ))
-    val_dataset = tf.data.Dataset.from_tensor_slices((
-        {key: val_encodings[key] for key in ['input_ids', 'attention_mask']},
-        {key: val_encodings[key] for key in ['start_positions', 'end_positions']}
-    ))
-
-Now we can use a DistilBert model with a QA head for training:
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    from transformers import DistilBertForQuestionAnswering
-    model = DistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
-    ## TENSORFLOW CODE
-    from transformers import TFDistilBertForQuestionAnswering
-    model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
-
-
-The data and model are both ready to go. You can train the model with
-:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` exactly as in the sequence classification example
-above. If using native PyTorch, replace ``labels`` with ``start_positions`` and ``end_positions`` in the training
-example. If using Keras's ``fit``, we need to make a minor modification to handle this example since it involves
-multiple model outputs.
-
-  - :ref:`ft_trainer`
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    from torch.utils.data import DataLoader
-    from transformers import AdamW
-
-    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-
-    model.to(device)
-    model.train()
-
-    train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
-
-    optim = AdamW(model.parameters(), lr=5e-5)
-
-    for epoch in range(3):
-        for batch in train_loader:
-            optim.zero_grad()
-            input_ids = batch['input_ids'].to(device)
-            attention_mask = batch['attention_mask'].to(device)
-            start_positions = batch['start_positions'].to(device)
-            end_positions = batch['end_positions'].to(device)
-            outputs = model(input_ids, attention_mask=attention_mask, start_positions=start_positions, end_positions=end_positions)
-            loss = outputs[0]
-            loss.backward()
-            optim.step()
-
-    model.eval()
-    ## TENSORFLOW CODE
-    # Keras will expect a tuple when dealing with labels
-    train_dataset = train_dataset.map(lambda x, y: (x, (y['start_positions'], y['end_positions'])))
-
-    # Keras will assign a separate loss for each output and add them together. So we'll just use the standard CE loss
-    # instead of using the built-in model.compute_loss, which expects a dict of outputs and averages the two terms.
-    # Note that this means the loss will be 2x of when using TFTrainer since we're adding instead of averaging them.
-    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-    model.distilbert.return_dict = False # if using 🤗 Transformers >3.02, make sure outputs are tuples
-
-    optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
-    model.compile(optimizer=optimizer, loss=loss) # can also use any keras loss fn
-    model.fit(train_dataset.shuffle(1000).batch(16), epochs=3, batch_size=16)
-
-.. _resources:
-
-Additional Resources
--------------------
-
-  - `How to train a new language model from scratch using Transformers and Tokenizers
-    <https://huggingface.co/blog/how-to-train>`_. Blog post showing the steps to load in Esperanto data and train a
-    masked language model from scratch.
-  - :doc:`Preprocessing <preprocessing>`. Docs page on data preprocessing.
-  - :doc:`Training <training>`. Docs page on training and fine-tuning.
-
-.. _nlplib:
-
-Using the 🤗 NLP Datasets & Metrics library
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-This tutorial demonstrates how to read in datasets from various raw text formats and prepare them for training with
-🤗 Transformers so that you can do the same thing with your own custom datasets. However, we recommend users use the
-`🤗 NLP library <https://github.com/huggingface/nlp>`_ for working with the 150+ datasets included in the
-`hub <https://huggingface.co/datasets>`_, including the three datasets used in this tutorial. As a very brief overview,
-we will show how to use the NLP library to download and prepare the IMDb dataset from the first example,
-:ref:`seq_imdb`.
-
-Start by downloading the dataset:
-
-.. code-block:: python
-
-    from nlp import load_dataset
-    train = load_dataset("imdb", split="train")
-
-Each dataset has multiple columns corresponding to different features. Let's see what our columns are.
-
-.. code-block:: python
-
-    >>> print(train.column_names)
-    ['label', 'text']
-
-Great. Now let's tokenize the text. We can do this using the ``map`` method. We'll also rename the ``label`` column
-to ``labels`` to match the model's input arguments.
-
-.. code-block:: python
-
-    train = train.map(lambda batch: tokenizer(batch["text"], truncation=True, padding=True), batched=True)
-    train.rename_column_("label", "labels")
-
-Lastly, we can use the ``set_format`` method to determine which columns and in what data format we want to access
-dataset elements.
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    >>> train.set_format("torch", columns=["input_ids", "attention_mask", "labels"])
-    >>> {key: val.shape for key, val in train[0].items()})
-    {'labels': torch.Size([]), 'input_ids': torch.Size([512]), 'attention_mask': torch.Size([512])}
-    ## TENSORFLOW CODE
-    >>> train.set_format("tensorflow", columns=["input_ids", "attention_mask", "labels"])
-    >>> {key: val.shape for key, val in train[0].items()})
-    {'labels': TensorShape([]), 'input_ids': TensorShape([512]), 'attention_mask': TensorShape([512])}
-
-We now have a fully-prepared dataset. Check out `the 🤗 NLP docs <https://huggingface.co/nlp/processing.html>`_ for
-a more thorough introduction.
@@ -7,7 +7,7 @@ General terms
 - autoencoding models: see MLM
 - autoregressive models: see CLM
 - CLM: causal language modeling, a pretraining task where the model reads the texts in order and has to predict the
-  next word. It's usually done by reading the whole sentence but using a mask inside the model to hide the future
+  next word. It's usually done by reading the whole sentence but using a mask inside the model to hide the future 
  tokens at a certain timestep.
 - MLM: masked language modeling, a pretraining task where the model sees a corrupted version of the texts, usually done
  by masking some tokens randomly, and has to predict the original text.
@@ -18,7 +18,7 @@ General terms
 - NLU: natural language understanding, all tasks related to understanding what is in a text (for instance classifying
  the whole text, individual words)
 - pretrained model: a model that has been pretrained on some data (for instance all of Wikipedia). Pretraining methods
-  involve a self-supervised objective, which can be reading the text and trying to predict the next word (see CLM) or
+  involve a self-supervised objective, which can be reading the text and trying to predict the next word (see CLM) or 
  masking some words and trying to predict them (see MLM).
 - RNN: recurrent neural network, a type of model that uses a loop over a layer to process texts.
 - seq2seq or sequence-to-sequence: models that generate a new sequence from an input, like translation models, or
@@ -57,7 +57,7 @@ The tokenizer takes care of splitting the sequence into tokens available in the
    >>> tokenized_sequence = tokenizer.tokenize(sequence)

 The tokens are either words or subwords. Here for instance, "VRAM" wasn't in the model vocabulary, so it's been split
-in "V", "RA" and "M". To indicate those tokens are not separate words but parts of the same word, a double-hash prefix is
+in "V", "RA" and "M". To indicate those tokens are not separate words but parts of the same word, a double-dash is
 added for "RA" and "M":

 ::
@@ -71,27 +71,24 @@ the sentence to the tokenizer, which leverages the Rust implementation of

 ::

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

 The tokenizer returns a dictionary with all the arguments necessary for its corresponding model to work properly. The
 token indices are under the key "input_ids":

 ::

-    >>> encoded_sequence = inputs["input_ids"]
    >>> print(encoded_sequence)
    [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102]

-Note that the tokenizer automatically adds "special tokens" (if the associated model relies on them) which are special
-IDs the model sometimes uses.
-
-If we decode the previous sequence of ids,
+Note that the tokenizer automatically adds "special tokens" (if the associated model rely on them) which are special
+IDs the model sometimes uses. If we decode the previous sequence of ids,

 ::

    >>> decoded_sequence = tokenizer.decode(encoded_sequence)

-we will see
+we will see 

 ::

@@ -147,7 +144,7 @@ We can see that 0s have been added on the right of the first sentence to make it

 This can then be converted into a tensor in PyTorch or TensorFlow. The attention mask is a binary tensor indicating
 the position of the padded indices so that the model does not attend to them. For the
-:class:`~transformers.BertTokenizer`, :obj:`1` indicates a value that should be attended to, while :obj:`0` indicates
+:class:`~transformers.BertTokenizer`, :obj:`1` indicate a value that should be attended to while :obj:`0` indicate
 a padded value. This attention mask is in the dictionary returned by the tokenizer under the key "attention_mask":

 ::
@@ -161,15 +158,15 @@ Token Type IDs
 ~~~~~~~~~~~~~~

 Some models' purpose is to do sequence classification or question answering. These require two different sequences to
-be joined in a single "input_ids" entry, which usually is performed with the help of special tokens, such as the classifier (``[CLS]``) and separator (``[SEP]``)
+be encoded in the same input IDs. They are usually separated by special tokens, such as the classifier and separator
 tokens. For example, the BERT model builds its two sequence input as such:

 ::

   >>> # [CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]

-We can use our tokenizer to automatically generate such a sentence by passing the two sequences to ``tokenizer`` as two arguments (and
-not a list, like before) like this:
+We can use our tokenizer to automatically generate such a sentence by passing the two sequences as two arguments (and
+not a list like before) like this:

 ::

@@ -188,31 +185,31 @@ which will return:
    >>> print(decoded)
    [CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]

-This is enough for some models to understand where one sequence ends and where another begins. However, other models,
-such as BERT, also deploy token type IDs (also called segment IDs). They are represented as a binary
-mask identifying the two types of sequence in the model.
+This is enough for some models to understand where one sequence ends and where another begins. However, other models
+such as BERT have an additional mechanism, which are the token type IDs (also called segment IDs). They are a binary
+mask identifying the different sequences in the model.

-The tokenizer returns this mask as the "token_type_ids" entry:
+The tokenizer returns in the dictionary under the key "token_type_ids":

 ::

    >>> encoded_dict['token_type_ids']
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]

-The first sequence, the "context" used for the question, has all its tokens represented by a :obj:`0`, whereas the
-second sequence, corresponding to the "question", has all its tokens represented by a :obj:`1`.
-
-Some models, like :class:`~transformers.XLNetModel` use an additional token represented by a :obj:`2`.
+The first sequence, the "context" used for the question, has all its tokens represented by :obj:`0`, whereas the
+question has all its tokens represented by :obj:`1`. Some models, like :class:`~transformers.XLNetModel` use an
+additional token represented by a :obj:`2`.

 .. _position-ids:

 Position IDs
 ~~~~~~~~~~~~

-Contrary to RNNs that have the position of each token embedded within them,
-transformers are unaware of the position of each token. Therefore, the position IDs (``position_ids``) are used by the model to identify each token's position in the list of tokens.
+The position IDs are used by the model to identify which token is at which position. Contrary to RNNs that have the
+position of each token embedded within them, transformers are unaware of the position of each token. The position
+IDs are created for this purpose.

-They are an optional parameter. If no ``position_ids`` is passed to the model, the IDs are automatically created as absolute
+They are an optional parameter. If no position IDs are passed to the model, they are automatically created as absolute
 positional embeddings.

 Absolute positional embeddings are selected in the range ``[0, config.max_position_embeddings - 1]``. Some models
@@ -223,15 +220,15 @@ use other types of positional embeddings, such as sinusoidal position embeddings
 Feed Forward Chunking
 ~~~~~~~~~~~~~~~~~~~~~

-In each residual attention block in transformers the self-attention layer is usually followed by 2 feed forward layers.
+In transformers two feed forward layers usually follows the self attention layer in each residual attention block.
 The intermediate embedding size of the feed forward layers is often bigger than the hidden size of the model (e.g.,
-for ``bert-base-uncased``).
+for ``bert-base-uncased``). 

 For an input of size ``[batch_size, sequence_length]``, the memory required to store the intermediate feed forward
 embeddings ``[batch_size, sequence_length, config.intermediate_size]`` can account for a large fraction of the memory
 use. The authors of `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_ noticed that since the
 computation is independent of the ``sequence_length`` dimension, it is mathematically equivalent to compute the output
-embeddings of both feed forward layers ``[batch_size, config.hidden_size]_0, ..., [batch_size, config.hidden_size]_n``
+embeddings of both feed forward layers ``[batch_size, config.hidden_size]_0, ..., [batch_size, config.hidden_size]_n`` 
 individually and concat them afterward to ``[batch_size, sequence_length, config.hidden_size]`` with
 ``n = sequence_length``, which trades increased computation time against reduced memory use, but yields a
 mathematically **equivalent** result.
@@ -124,11 +124,7 @@ conversion utilities for the following models:
 22. `DPR <https://github.com/facebookresearch/DPR>`_ (from Facebook) released with the paper `Dense Passage Retrieval
    for Open-Domain Question Answering <https://arxiv.org/abs/2004.04906>`_ by Vladimir Karpukhin, Barlas Oğuz, Sewon
    Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
-23. `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.
-24. `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.  
-25. `Other community models <https://huggingface.co/models>`_, contributed by the `community
+23. `Other community models <https://huggingface.co/models>`_, contributed by the `community
    <https://huggingface.co/users>`_.

 .. toctree::
@@ -158,12 +154,11 @@ conversion utilities for the following models:

    pretrained_models
    examples
-    custom_datasets
    notebooks
    converting_tensorflow_models
    migration
+    torchscript
    contributing
-    serialization

 .. toctree::
    :maxdepth: 2
@@ -182,9 +177,9 @@ conversion utilities for the following models:
    main_classes/model
    main_classes/tokenizer
    main_classes/pipelines
-    main_classes/trainer
    main_classes/optimizer_schedules
    main_classes/processors
+    main_classes/trainer
    model_doc/auto
    model_doc/encoderdecoder
    model_doc/bert
@@ -210,8 +205,3 @@ conversion utilities for the following models:
    model_doc/retribert
    model_doc/mobilebert
    model_doc/dpr
-    model_doc/pegasus
-    model_doc/mbart
-    internal/modeling_utils
-    internal/tokenization_utils
-    internal/pipelines_utils
@@ -22,13 +22,13 @@ When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be
 pip install transformers
 ```

-Alternatively, for CPU-support only, you can install 🤗 Transformers and PyTorch in one line with:
+Alternatively, for CPU-support only, you can install 🤗 Transformers and PyTorch in one line with

 ```bash
 pip install transformers[torch]
 ```

-or 🤗 Transformers and TensorFlow 2.0 in one line with:
+or 🤗 Transformers and TensorFlow 2.0 in one line with

 ```bash
 pip install transformers[tf-cpu]
@@ -73,8 +73,8 @@ This library provides pretrained models that will be downloaded and cached local
 folder given by the shell environment variable ``TRANSFORMERS_CACHE``. The default value for it will be the PyTorch
 cache home followed by ``/transformers/`` (even if you don't have PyTorch installed). This is (by order of priority):

-  * shell environment variable ``TORCH_HOME``
-  * shell environment variable ``XDG_CACHE_HOME`` + ``/torch/``
+  * shell environment variable ``ENV_TORCH_HOME``
+  * shell environment variable ``ENV_XDG_CACHE_HOME`` + ``/torch/``
  * default: ``~/.cache/torch/``

 So if you don't have any specific environment variable set, the cache directory will be at
@@ -1,88 +0,0 @@
-Custom Layers and Utilities
---------------------------
-
-This page lists all the custom layers used by the library, as well as the utility functions it provides for modeling.
-
-Most of those are only useful if you are studying the code of the models in the library.
-
-
-``Pytorch custom modules``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_utils.Conv1D
-
-.. autoclass:: transformers.modeling_utils.PoolerStartLogits
-    :members: forward
-
-.. autoclass:: transformers.modeling_utils.PoolerEndLogits
-    :members: forward
-
-.. autoclass:: transformers.modeling_utils.PoolerAnswerClass
-    :members: forward
-
-.. autoclass:: transformers.modeling_utils.SquadHeadOutput
-
-.. autoclass:: transformers.modeling_utils.SQuADHead
-    :members: forward
-
-.. autoclass:: transformers.modeling_utils.SequenceSummary
-    :members: forward
-
-
-``PyTorch Helper Functions``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: transformers.apply_chunking_to_forward
-
-.. autofunction:: transformers.modeling_utils.find_pruneable_heads_and_indices
-
-.. autofunction:: transformers.modeling_utils.prune_layer
-
-.. autofunction:: transformers.modeling_utils.prune_conv1d_layer
-
-.. autofunction:: transformers.modeling_utils.prune_linear_layer
-
-``TensorFlow custom layers``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_utils.TFConv1D
-
-.. autoclass:: transformers.modeling_tf_utils.TFSharedEmbeddings
-    :members: call
-
-.. autoclass:: transformers.modeling_tf_utils.TFSequenceSummary
-    :members: call
-
-
-``TensorFlow loss functions``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_utils.TFCausalLanguageModelingLoss
-    :members:
-
-.. autoclass:: transformers.modeling_tf_utils.TFMaskedLanguageModelingLoss
-    :members:
-
-.. autoclass:: transformers.modeling_tf_utils.TFMultipleChoiceLoss
-    :members:
-
-.. autoclass:: transformers.modeling_tf_utils.TFQuestionAnsweringLoss
-    :members:
-
-.. autoclass:: transformers.modeling_tf_utils.TFSequenceClassificationLoss
-    :members:
-
-.. autoclass:: transformers.modeling_tf_utils.TFTokenClassificationLoss
-    :members:
-
-
-``TensorFlow Helper Functions``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: transformers.modeling_tf_utils.cast_bool_to_primitive
-
-.. autofunction:: transformers.modeling_tf_utils.get_initializer
-
-.. autofunction:: transformers.modeling_tf_utils.keras_serializable
-
-.. autofunction:: transformers.modeling_tf_utils.shape_list
@@ -1,40 +0,0 @@
-Utilities for pipelines
-----------------------
-
-This page lists all the utility functions the library provides for pipelines.
-
-Most of those are only useful if you are studying the code of the models in the library.
-
-
-Argument handling
-~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.pipelines.ArgumentHandler
-
-.. autoclass:: transformers.pipelines.ZeroShotClassificationArgumentHandler
-
-.. autoclass:: transformers.pipelines.QuestionAnsweringArgumentHandler
-
-
-Data format
-~~~~~~~~~~~
-
-.. autoclass:: transformers.pipelines.PipelineDataFormat
-    :members:
-
-.. autoclass:: transformers.pipelines.CsvPipelineDataFormat
-    :members:
-
-.. autoclass:: transformers.pipelines.JsonPipelineDataFormat
-    :members:
-
-.. autoclass:: transformers.pipelines.PipedPipelineDataFormat
-    :members:
-
-
-Utilities
-~~~~~~~~~
-
-.. autofunction:: transformers.pipelines.get_framework
-
-.. autoclass:: transformers.pipelines.PipelineException
@@ -1,38 +0,0 @@
-Utilities for Tokenizers
------------------------
-
-This page lists all the utility functions used by the tokenizers, mainly the class
-:class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase` that implements the common methods between
-:class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast` and the mixin
-:class:`~transformers.tokenization_utils_base.SpecialTokensMixin`.
-
-Most of those are only useful if you are studying the code of the tokenizers in the library.
-
-``PreTrainedTokenizerBase``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.tokenization_utils_base.PreTrainedTokenizerBase
-    :special-members: __call__
-    :members:
-
-
-``SpecialTokensMixin``
-~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.tokenization_utils_base.SpecialTokensMixin
-    :members:
-
-
-Enums and namedtuples
-~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.tokenization_utils_base.ExplicitEnum
-
-.. autoclass:: transformers.tokenization_utils_base.PaddingStrategy
-
-.. autoclass:: transformers.tokenization_utils_base.TensorType
-
-.. autoclass:: transformers.tokenization_utils_base.TruncationStrategy
-
-.. autoclass:: transformers.tokenization_utils_base.CharSpan
-
-.. autoclass:: transformers.tokenization_utils_base.TokenSpan
@@ -1,34 +1,23 @@
 Models
 ----------------------------------------------------

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

-:class:`~transformers.PreTrainedModel` and :class:`~transformers.TFPreTrainedModel` also implement a few methods which
-are common among all the models to:
+``PreTrainedModel`` also implements a few methods which are common among all the models to:

 - resize the input token embeddings when new tokens are added to the vocabulary
 - prune the attention heads of the model.

-The other methods that are common to each model are defined in :class:`~transformers.modeling_utils.ModuleUtilsMixin`
-(for the PyTorch models) and :class:`~transformers.modeling_tf_utils.TFModuleUtilsMixin` (for the TensorFlow models) or
-for text generation, :class:`~transformers.generation_utils.GenerationMixin` (for the PyTorch models) and
-:class:`~transformers.generation_tf_utils.TFGenerationMixin` (for the TensorFlow models)
-
-
 ``PreTrainedModel``
 ~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.PreTrainedModel
    :members:

+``Helper Functions``
+~~~~~~~~~~~~~~~~~~~~~

-``ModuleUtilsMixin``
-~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_utils.ModuleUtilsMixin
-    :members:
+.. autofunction:: transformers.apply_chunking_to_forward


 ``TFPreTrainedModel``
@@ -36,20 +25,3 @@ for text generation, :class:`~transformers.generation_utils.GenerationMixin` (fo

 .. autoclass:: transformers.TFPreTrainedModel
    :members:
-
-
-``TFModelUtilsMixin``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_utils.TFModelUtilsMixin
-    :members:
-
-
-Generative models
-~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.generation_utils.GenerationMixin
-    :members:
-
-.. autoclass:: transformers.generation_tf_utils.TFGenerationMixin
-    :members:
@@ -3,24 +3,13 @@ Pipelines

 The pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most
 of the complex code from the library, offering a simple API dedicated to several tasks, including Named Entity
-Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction and Question Answering. See the
-:doc:`task summary <../task_summary>` for examples of use.
+Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction and Question Answering.

 There are two categories of pipeline abstractions to be aware about:

- The :func:`~transformers.pipeline` which is the most powerful object encapsulating all other pipelines.
- The other task-specific pipelines:
-
-    - :class:`~transformers.ConversationalPipeline`
-    - :class:`~transformers.FeatureExtractionPipeline`
-    - :class:`~transformers.FillMaskPipeline`
-    - :class:`~transformers.QuestionAnsweringPipeline`
-    - :class:`~transformers.SummarizationPipeline`
-    - :class:`~transformers.TextClassificationPipeline`
-    - :class:`~transformers.TextGenerationPipeline`
-    - :class:`~transformers.TokenClassificationPipeline`
-    - :class:`~transformers.TranslationPipeline`
-    - :class:`~transformers.ZeroShotClassificationPipeline`
+- The :func:`~transformers.pipeline` which is the most powerful object encapsulating all other pipelines
+- The other task-specific pipelines, such as :class:`~transformers.TokenClassificationPipeline`
+  or :class:`~transformers.QuestionAnsweringPipeline`

 The pipeline abstraction
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -32,82 +21,53 @@ other pipeline but requires an additional argument which is the `task`.


 The task specific pipelines
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-ConversationalPipeline
-==========================================
+Parent class: Pipeline
+=========================================

-.. autoclass:: transformers.Conversation
-
-.. autoclass:: transformers.ConversationalPipeline
-    :special-members: __call__
-    :members:
-
-FeatureExtractionPipeline
-==========================================
-
-.. autoclass:: transformers.FeatureExtractionPipeline
-    :special-members: __call__
-    :members:
-
-FillMaskPipeline
-==========================================
-
-.. autoclass:: transformers.FillMaskPipeline
-    :special-members: __call__
-    :members:
-
-NerPipeline
-==========================================
-
-This class is an alias of the :class:`~transformers.TokenClassificationPipeline` defined below. Please refer to that
-pipeline for documentation and usage examples.
-
-QuestionAnsweringPipeline
-==========================================
-
-.. autoclass:: transformers.QuestionAnsweringPipeline
-    :special-members: __call__
-    :members:
-
-SummarizationPipeline
-==========================================
-
-.. autoclass:: transformers.SummarizationPipeline
-    :special-members: __call__
-    :members:
-
-TextClassificationPipeline
-==========================================
-
-.. autoclass:: transformers.TextClassificationPipeline
-    :special-members: __call__
-    :members:
-
-TextGenerationPipeline
-==========================================
-
-.. autoclass:: transformers.TextGenerationPipeline
-    :special-members: __call__
-    :members:
+.. autoclass:: transformers.Pipeline
+    :members: predict, transform, save_pretrained

 TokenClassificationPipeline
 ==========================================

 .. autoclass:: transformers.TokenClassificationPipeline
-    :special-members: __call__
-    :members:

-ZeroShotClassificationPipeline
+NerPipeline
 ==========================================

-.. autoclass:: transformers.ZeroShotClassificationPipeline
-    :special-members: __call__
-    :members:
+This class is an alias of the :class:`~transformers.TokenClassificationPipeline` defined above. Please refer to that pipeline for
+documentation and usage examples.
+
+FillMaskPipeline
+==========================================
+
+.. autoclass:: transformers.FillMaskPipeline
+
+FeatureExtractionPipeline
+==========================================
+
+.. autoclass:: transformers.FeatureExtractionPipeline
+
+TextClassificationPipeline
+==========================================
+
+.. autoclass:: transformers.TextClassificationPipeline
+
+QuestionAnsweringPipeline
+==========================================
+
+.. autoclass:: transformers.QuestionAnsweringPipeline


-Parent class: :obj:`Pipeline`
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+SummarizationPipeline
+==========================================

-.. autoclass:: transformers.Pipeline
-    :members:
+.. autoclass:: transformers.SummarizationPipeline
+
+
+TextGenerationPipeline
+==========================================
+
+.. autoclass:: transformers.TextGenerationPipeline
@@ -1,40 +1,17 @@
 Tokenizer
 ----------------------------------------------------

-A tokenizer is in charge of preparing the inputs for a model. The library contains tokenizers for all the models. Most
-of the tokenizers are available in two flavors: a full python implementation and a "Fast" implementation based on the
-Rust library `tokenizers <https://github.com/huggingface/tokenizers>`__. The "Fast" implementations allows:
+A tokenizer is in charge of preparing the inputs for a model. The library comprise tokenizers for all the models. Most of the tokenizers are available in two flavors: a full python implementation and a "Fast" implementation based on the Rust library `tokenizers`. The "Fast" implementations allows (1) a significant speed-up in particular when doing batched tokenization and (2) additional methods to map between the original string (character and words) and the token space (e.g. getting the index of the token comprising a given character or the span of characters corresponding to a given token). Currently no "Fast" implementation is available for the SentencePiece-based tokenizers (for T5, ALBERT, CamemBERT, XLMRoBERTa and XLNet models).

-1. a significant speed-up in particular when doing batched tokenization and
-2. additional methods to map between the original string (character and words) and the token space (e.g. getting the
-   index of the token comprising a given character or the span of characters corresponding to a given token). Currently
-   no "Fast" implementation is available for the SentencePiece-based tokenizers (for T5, ALBERT, CamemBERT, XLMRoBERTa
-   and XLNet models).
+The base classes ``PreTrainedTokenizer`` and ``PreTrainedTokenizerFast`` implements the common methods for encoding string inputs in model inputs (see below) and instantiating/saving python and "Fast" tokenizers either from a local file or directory or from a pretrained tokenizer provided by the library (downloaded from HuggingFace's AWS S3 repository).

-The base classes :class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast`
-implement the common methods for encoding string inputs in model inputs (see below) and instantiating/saving python and
-"Fast" tokenizers either from a local file or directory or from a pretrained tokenizer provided by the library
-(downloaded from HuggingFace's AWS S3 repository). They both rely on
-:class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase` that contains the common methods, and
-:class:`~transformers.tokenization_utils_base.SpecialTokensMixin`.
+``PreTrainedTokenizer`` and ``PreTrainedTokenizerFast`` thus implements the main methods for using all the tokenizers:

-:class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast` thus implement the main
-methods for using all the tokenizers:
-
- Tokenizing (splitting strings in sub-word token strings), converting tokens strings to ids and back, and
-  encoding/decoding (i.e., tokenizing and converting to integers).
- Adding new tokens to the vocabulary in a way that is independent of the underlying structure (BPE, SentencePiece...).
- Managing special tokens (like mask, beginning-of-sentence, etc.): adding them, assigning them to attributes in the
-  tokenizer for easy access and making sure they are not split during tokenization.
-
-:class:`~transformers.BatchEncoding` holds the output of the tokenizer's encoding methods (``__call__``,
-``encode_plus`` and ``batch_encode_plus``) and is derived from a Python dictionary. When the tokenizer is a pure python
-tokenizer, this class behaves just like a standard python dictionary and holds the various model inputs computed by these
-methods (``input_ids``, ``attention_mask``...). When the tokenizer is a "Fast" tokenizer (i.e., backed by HuggingFace
-`tokenizers library <https://github.com/huggingface/tokenizers>`__), this class provides in addition several advanced
-alignment methods which can be used to map between the original string (character and words) and the token space (e.g.,
-getting the index of the token comprising a given character or the span of characters corresponding to a given token).
+- tokenizing (spliting strings in sub-word token strings), converting tokens strings to ids and back, and encoding/decoding (i.e. tokenizing + convert to integers),
+- adding new tokens to the vocabulary in a way that is independant of the underlying structure (BPE, SentencePiece...),
+- managing special tokens like mask, beginning-of-sentence, etc tokens (adding them, assigning them to attributes in the tokenizer for easy access and making sure they are not split during tokenization)

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

 ``PreTrainedTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~
@@ -43,7 +20,6 @@ getting the index of the token comprising a given character or the span of chara
    :special-members: __call__
    :members:

-
 ``PreTrainedTokenizerFast``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -51,9 +27,14 @@ getting the index of the token comprising a given character or the span of chara
    :special-members: __call__
    :members:

-
 ``BatchEncoding``
 ~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BatchEncoding
    :members:
+
+``SpecialTokensMixin``
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.SpecialTokensMixin
+    :members:
@@ -11,23 +11,6 @@ customization during training.
 The API supports distributed training on multiple GPUs/TPUs, mixed precision through `NVIDIA Apex
 <https://github.com/NVIDIA/apex>`__ for PyTorch and :obj:`tf.keras.mixed_precision` for TensorFlow.

-Both :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` contain the basic training loop supporting the
-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 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.
- **training_step** -- Performs a training step.
- **prediction_step** -- Performs an evaluation/test step.
- **run_model** (TensorFlow only) -- Basic pass through the model.
- **evaluate** -- Runs an evaluation loop and returns metrics.
- **predict** -- Returns predictions (with metrics if labels are available) on a test set.
-
-
 ``Trainer`` 
 ~~~~~~~~~~~

@@ -50,10 +50,7 @@ AlbertTokenizer
 Albert specific outputs
 ~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.modeling_albert.AlbertForPreTrainingOutput
-    :members:
-
-.. autoclass:: transformers.modeling_tf_albert.TFAlbertForPreTrainingOutput
+.. autoclass:: transformers.modeling_albert.AlbertForPretrainingOutput
    :members:


@@ -64,13 +61,6 @@ AlbertModel
    :members:


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

@@ -113,13 +103,6 @@ TFAlbertModel
    :members:


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

@@ -17,23 +17,13 @@ According to the abstract,
 The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_


-Implementation Notes
-~~~~~~~~~~~~~~~~~~~~
+Implementation Notes:

 - Bart doesn't use :obj:`token_type_ids` for sequence classification. Use BartTokenizer.encode to get the proper splitting.
 - The forward pass of ``BartModel`` will create decoder inputs (using the helper function ``transformers.modeling_bart._prepare_bart_decoder_inputs``)  if they are not passed. This is different than some other modeling APIs.
 - Model predictions are intended to be identical to the original implementation. This only works, however, if the string you pass to ``fairseq.encode`` starts with a space.
 - ``BartForConditionalGeneration.generate`` should be used for conditional generation tasks like summarization, see the example in that docstrings
 - Models that load the ``"facebook/bart-large-cnn"`` weights will not have a ``mask_token_id``, or be able to perform mask filling tasks.
- for training/forward passes that don't involve beam search, pass ``use_cache=False``
-
-
-BartForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BartForConditionalGeneration
-    :members: generate, forward
-

 BartConfig
 ~~~~~~~~~~~~~~~~~~~~~
@@ -49,6 +39,17 @@ BartTokenizer
    :members:


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

 BartModel
 ~~~~~~~~~~~~~
@@ -27,8 +27,13 @@ Tips:

 - BERT is a model with absolute position embeddings so it's usually advised to pad the inputs on
  the right rather than the left.
- BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. It is efficient at predicting masked
-  tokens and at NLU in general, but is not optimal for text generation.
+- BERT was trained with a masked language modeling (MLM) objective. It is therefore efficient at predicting masked
+  tokens and at NLU in general, but is not optimal for text generation. Models trained with a causal language
+  modeling (CLM) objective are better in that regard.
+- Alongside MLM, BERT was trained using a next sentence prediction (NSP) objective using the [CLS] token as a sequence
+  approximate. The user may use this token (the first token in a sequence built with special tokens) to get a sequence
+  prediction rather than a token prediction. However, averaging over the sequence may yield better results than using
+  the [CLS] token.

 The original code can be found `here <https://github.com/google-research/bert>`_.

@@ -57,10 +62,7 @@ BertTokenizerFast
 Bert specific outputs
 ~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.modeling_bert.BertForPreTrainingOutput
-    :members:
-
-.. autoclass:: transformers.modeling_tf_bert.TFBertForPreTrainingOutput
+.. autoclass:: transformers.modeling_bert.BertForPretrainingOutput
    :members:


@@ -78,13 +80,6 @@ BertForPreTraining
    :members:


-BertModelLMHeadModel
-~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BertLMHeadModel
-    :members:
-
-
 BertForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -141,13 +136,6 @@ TFBertForPreTraining
    :members:


-TFBertModelLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBertLMHeadModel
-    :members:
-
-
 TFBertForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -49,13 +49,6 @@ CamembertModel
    :members:


-CamembertForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CamembertForCausalLM
-    :members:
-
-
 CamembertForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -112,13 +105,6 @@ TFCamembertForSequenceClassification
    :members:


-TFCamembertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFCamembertForMultipleChoice
-    :members:
-
-
 TFCamembertForTokenClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -74,10 +74,7 @@ ElectraTokenizerFast
 Electra specific outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.modeling_electra.ElectraForPreTrainingOutput
-    :members:
-
-.. autoclass:: transformers.modeling_tf_electra.TFElectraForPreTrainingOutput
+.. autoclass:: transformers.modeling_electra.ElectraForPretrainingOutput
    :members:


@@ -109,13 +106,6 @@ ElectraForSequenceClassification
    :members:


-ElectraForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ElectraForMultipleChoice
-    :members:
-
-
 ElectraForTokenClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -151,20 +141,6 @@ TFElectraForMaskedLM
    :members:


-TFElectraForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFElectraForSequenceClassification
-    :members:
-
-
-TFElectraForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFElectraForMultipleChoice
-    :members:
-
-
 TFElectraForTokenClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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


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

@@ -77,9 +77,6 @@ OpenAI specific outputs
 .. autoclass:: transformers.modeling_openai.OpenAIGPTDoubleHeadsModelOutput
    :members:

-.. autoclass:: transformers.modeling_tf_openai.TFOpenAIGPTDoubleHeadsModelOutput
-    :members:
-

 OpenAIGPTModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -64,9 +64,6 @@ GPT2 specific outputs
 .. autoclass:: transformers.modeling_gpt2.GPT2DoubleHeadsModelOutput
    :members:

-.. autoclass:: transformers.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput
-    :members:
-

 GPT2Model
 ~~~~~~~~~~~~~~~~~~~~~
@@ -16,7 +16,7 @@ Longformer Self Attention
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 Longformer self attention employs self attention on both a "local" context and a "global" context.
 Most tokens only attend "locally" to each other meaning that each token attends to its :math:`\frac{1}{2} w` previous tokens and :math:`\frac{1}{2} w` succeding tokens with :math:`w` being the window length as defined in `config.attention_window`. Note that `config.attention_window` can be of type ``list`` to define a different :math:`w` for each layer. 
-A selected few tokens attend "globally" to all other tokens, as it is conventionally done for all tokens in *e.g.* `BertSelfAttention`.
+A selecetd few tokens attend "globally" to all other tokens, as it is conventionally done for all tokens in *e.g.* `BertSelfAttention`.

 Note that "locally" and "globally" attending tokens are projected by different query, key and value matrices.
 Also note that every "locally" attending token not only attends to tokens within its window :math:`w`, but also to all "globally" attending tokens so that global attention is *symmetric*.
@@ -102,25 +102,3 @@ LongformerForQuestionAnswering

 .. autoclass:: transformers.LongformerForQuestionAnswering
    :members:
-
-
-TFLongformerModel
-~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFLongformerModel
-    :members:
-
-
-TFLongformerForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFLongformerForMaskedLM
-    :members:
-
-
-TFLongformerForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFLongformerForQuestionAnswering
-    :members:
-
@@ -1,14 +1,14 @@
 MarianMT
 ----------------------------------------------------
-**Bugs:** If you see something strange,
-file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=sshleifer&labels=&template=bug-report.md&title>`__ and assign
+**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
@sshleifer. Translations should be similar, but not identical to, output in the test set linked to in each model card.

 Implementation Notes
 ~~~~~~~~~~~~~~~~~~~~
 - Each model is about 298 MB on disk, there are 1,000+ models.
 - The list of supported language pairs can be found `here <https://huggingface.co/Helsinki-NLP>`__.
- models were originally trained by `Jörg Tiedemann <https://researchportal.helsinki.fi/en/persons/j%C3%B6rg-tiedemann>`__ using the `Marian <https://marian-nmt.github.io/>`_ C++ library, which supports fast training and translation.
+- The 1,000+ models were originally trained by `Jörg Tiedemann <https://researchportal.helsinki.fi/en/persons/j%C3%B6rg-tiedemann>`__ using the `Marian <https://marian-nmt.github.io/>`_ C++ library, which supports fast training and translation.
 - All models are transformer encoder-decoders with 6 layers in each component. Each model's performance is documented in a model card.
 - The 80 opus models that require BPE preprocessing are not supported.
 - The modeling code is the same as ``BartForConditionalGeneration`` with a few minor modifications:
@@ -48,7 +48,7 @@ Example of translating english to many romance languages, using language codes:
    tokenizer = MarianTokenizer.from_pretrained(model_name)
    print(tokenizer.supported_language_codes)
    model = MarianMTModel.from_pretrained(model_name)
-    translated = model.generate(**tokenizer.prepare_seq2seq_batch(src_text))
+    translated = model.generate(**tokenizer.prepare_translation_batch(src_text))
    tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
    # ["c'est une phrase en anglais que nous voulons traduire en français",
    # 'Isto deve ir para o português.',
@@ -86,14 +86,6 @@ Code to see available pretrained models:
    suffix = [x.split('/')[1] for x in model_ids]
    multi_models = [f'{org}/{s}' for s in suffix if s != s.lower()]

-MarianMTModel
-~~~~~~~~~~~~~
-
-Pytorch version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints.
-Model API is identical to BartForConditionalGeneration.
-Available models are listed at `Model List <https://huggingface.co/models?search=Helsinki-NLP>`__
-This class inherits nearly all functionality from ``BartForConditionalGeneration``, see that page for method signatures.
-
 MarianConfig
 ~~~~~~~~~~~~~~~~~~~
 .. autoclass:: transformers.MarianConfig
@@ -104,8 +96,16 @@ MarianTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MarianTokenizer
-    :members: prepare_seq2seq_batch
+    :members: prepare_translation_batch


+MarianMTModel
+~~~~~~~~~~~~~

+Pytorch version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints.
+Model API is identical to BartForConditionalGeneration.
+Available models are listed at `Model List <https://huggingface.co/models?search=Helsinki-NLP>`__
+This class inherits all functionality from ``BartForConditionalGeneration``, see that page for method signatures.

+.. autoclass:: transformers.MarianMTModel
+    :members:
@@ -1,76 +0,0 @@
-MBart
----------------------------------------------------
-**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
-@sshleifer
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~
-The MBart model was presented in `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. According to the abstract,
-
-MBART is a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective. mBART is one of the first methods for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text.
-
-The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/mbart>`__
-
-
-Training
-~~~~~~~~~~~~~~~~~~~~~
-MBart is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation task. 
-As the model is multilingual it expects the sequences in a different format. A special language id token 
-is added in both the source and target text. The source text format is ``X [eos, src_lang_code]`` 
-where ``X`` is the source text. The target text format is ```[tgt_lang_code] X [eos]```. ```bos``` is never used.
-The ```MBartTokenizer.prepare_seq2seq_batch``` handles this automatically and should be used to encode 
-the sequences for seq-2-seq fine-tuning.
-
- Supervised training
-
-::
-
-    example_english_phrase = "UN Chief Says There Is No Military Solution in Syria"
-    expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
-    batch = tokenizer.prepare_seq2seq_batch(example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian)
-    input_ids = batch["input_ids"]
-    target_ids = batch["decoder_input_ids"]
-    decoder_input_ids = target_ids[:, :-1].contiguous()
-    labels = target_ids[:, 1:].clone()
-    model(input_ids=input_ids, decoder_input_ids=decoder_input_ids, labels=labels) #forward
-
- Generation
-
-    While generating the target text set the `decoder_start_token_id` to the target language id. 
-    The following example shows how to translate English to Romanian using the ```facebook/mbart-large-en-ro``` model.
-
-::
-
-    from transformers import MBartForConditionalGeneration, MBartTokenizer
-    model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
-    tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro")
-    article = "UN Chief Says There Is No Military Solution in Syria"
-    batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], src_lang="en_XX")
-    translated_tokens = model.generate(**batch, decoder_start_token_id=tokenizer.lang_code_to_id["ro_RO"])
-    translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
-    assert translation == "Şeful ONU declară că nu există o soluţie militară în Siria"
-
-
-MBartConfig
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MBartConfig
-    :members:
-
-
-MBartTokenizer
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MBartTokenizer
-    :members: build_inputs_with_special_tokens, prepare_seq2seq_batch
-
-
-MBartForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MBartForConditionalGeneration
-    :members: generate, forward
-
-
@@ -59,10 +59,7 @@ MobileBertTokenizerFast
 MobileBert specific outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.modeling_mobilebert.MobileBertForPreTrainingOutput
-    :members:
-
-.. autoclass:: transformers.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput
+.. autoclass:: transformers.modeling_mobilebert.MobileBertForPretrainingOutput
    :members:


@@ -1,116 +0,0 @@
-Pegasus
----------------------------------------------------
-**DISCLAIMER:** If you see something strange,
-file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=sshleifer&labels=&template=bug-report.md&title>`__ and assign
-@sshleifer.
-
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~
-
-The Pegasus model was proposed in `PEGASUS: Pre-training with Extracted Gap-sentences for
-Abstractive Summarization <https://arxiv.org/pdf/1912.08777.pdf>`_ by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019.
-According to the abstract,
-
- Pegasus' pretraining task is intentionally similar to summarization: important sentences are removed/masked from an input document and are generated together as one output sequence from the remaining sentences, similar to an extractive summary.
- Pegasus achieves SOTA summarization performance on all 12 downstream tasks, as measured by ROUGE and human eval.
-
-The Authors' code can be found `here <https://github.com/google-research/pegasus>`_.
-
-
-Checkpoints
-~~~~~~~~~~~
-All the `checkpoints <https://huggingface.co/models?search=pegasus>`_ are finetuned for summarization, besides ``pegasus-large``, whence the other checkpoints are finetuned.
- Each checkpoint is 2.2 GB on disk and 568M parameters.
- FP16 is not supported (help/ideas on this appreciated!).
- Summarizing xsum in fp32 takes about 400ms/sample, with default parameters on a v100 GPU.
- For XSUM, The paper reports rouge1,rouge2, rougeL of paper: 47.21/24.56/39.25. As of Aug 9, this port scores 46.91/24.34/39.1.
-The gap is likely because of different alpha/length_penalty implementations in beam search.
-
-
-Implementation Notes
-~~~~~~~~~~~~~~~~~~~~
-
- All models are transformer encoder-decoders with 16 layers in each component.
- The implementation is completely inherited from ``BartForConditionalGeneration``
- Some key configuration differences:
-    - static, sinusoidal position embeddings
-    - no ``layernorm_embedding`` (``PegasusConfig.normalize_embedding=False``)
-    - the model starts generating with pad_token_id (which has 0 token_embedding) as the prefix.
-    - ``num_beams=8``
- All pretrained pegasus checkpoints are the same besides three attributes: ``tokenizer.model_max_length`` (max input size),  ``max_length`` (max num tokens to generate) and ``length_penalty``
- Code to convert checkpoints trained in the author's `repo <https://github.com/google-research/pegasus>`_ can be found in ``convert_pegasus_tf_to_pytorch.py``
-
-
-Usage Example
-~~~~~~~~~~~~~~~~~~~~
-
-.. code-block:: python
-
-    from transformers import PegasusForConditionalGeneration, PegasusTokenizer
-    src_text = [
-        """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."""
-    ]
-
-    model_name = 'google/pegasus-xsum'
-    torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
-    tokenizer = PegasusTokenizer.from_pretrained(model_name)
-    model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
-    batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest').to(torch_device)
-    translated = model.generate(**batch)
-    tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
-    assert tgt_text[0] == "California's largest electricity provider has turned off power to tens of thousands of customers."
-
-PegasusForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-This class inherits all functionality from ``BartForConditionalGeneration``, see that page for method signatures.
-Available models are listed at `Model List <https://huggingface.co/models?search=pegasus>`__
-
-.. autoclass:: transformers.PegasusForConditionalGeneration
-    :members:
-
-
-PegasusConfig
-~~~~~~~~~~~~~~~~~~~
-This config fully inherits from ``BartConfig``, but pegasus uses different default values:
-Up to date parameter values can be seen in `S3 <https://s3.amazonaws.com/models.huggingface.co/bert/google/pegasus-xsum/config.json>`_.
-As of Aug 10, 2020, they are:
-
-.. code-block:: python
-
-    dict(
-    vocab_size=96103,
-    max_position_embeddings=512,
-    d_model=1024,
-    encoder_ffn_dim=4096,
-    decoder_ffn_dim=4096,
-    encoder_attention_heads=16,
-    decoder_attention_heads=16,
-    encoder_layers=16,
-    decoder_layers=16,
-    dropout=0.1,
-    attention_dropout=0.1,
-    activation_dropout=0.1,
-    pad_token_id=0,
-    eos_token_id=1,
-    is_encoder_decoder=True,
-    normalize_before=True,
-    scale_embedding=True,
-    normalize_embedding=False,
-    add_final_layer_norm=True,
-    static_position_embeddings=True,
-    num_beams=8,
-    activation_function="relu",
-    )
-
-
-PegasusTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-warning: ``add_tokens`` does not work at the moment.
-
-.. autoclass:: transformers.PegasusTokenizer
-    :members: __call__, prepare_seq2seq_batch
-
-
-
@@ -121,13 +121,6 @@ ReformerForMaskedLM
    :members:


-ReformerForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ReformerForSequenceClassification
-    :members:
-
-
 ReformerForQuestionAnswering
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -63,13 +63,6 @@ RobertaModel
    :members:


-RobertaForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RobertaForCausalLM
-    :members:
-
-
 RobertaForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -38,13 +38,13 @@ T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.

  In this setup spans of the input sequence are masked by so-called sentinel tokens (*a.k.a* unique mask tokens) 
  and the output sequence is formed as a concatenation of the same sentinel tokens and the *real* masked tokens. 
-  Each sentinel token represents a unique mask token for this sentence and should start with ``<extra_id_0>``, ``<extra_id_1>``, ... up to ``<extra_id_99>``. As a default 100 sentinel tokens are available in ``T5Tokenizer``.
+  Each sentinel token represents a unique mask token for this sentence and should start with ``<extra_id_1>``, ``<extra_id_2>``, ... up to ``<extra_id_100>``. As a default 100 sentinel tokens are available in ``T5Tokenizer``.
  *E.g.* the sentence "The cute dog walks in the park" with the masks put on "cute dog" and "the" should be processed as follows: 

 ::

-  input_ids = tokenizer.encode('The <extra_id_0> walks in <extra_id_1> park', return_tensors='pt')
-  labels = tokenizer.encode('<extra_id_0> cute dog <extra_id_1> the <extra_id_2> </s>', return_tensors='pt')
+  input_ids = tokenizer.encode('The <extra_id_1> walks in <extra_id_2> park', return_tensors='pt')
+  labels = tokenizer.encode('<extra_id_1> cute dog <extra_id_2> the <extra_id_3> </s>', return_tensors='pt')
  # the forward function automatically creates the correct decoder_input_ids
  model(input_ids=input_ids, labels=labels)

@@ -63,12 +63,6 @@ TransfoXL specific outputs
 .. autoclass:: transformers.modeling_transfo_xl.TransfoXLLMHeadModelOutput
    :members:

-.. autoclass:: transformers.modeling_tf_transfo_xl.TFTransfoXLModelOutput
-    :members:
-
-.. autoclass:: transformers.modeling_tf_transfo_xl.TFTransfoXLLMHeadModelOutput
-    :members:
-

 TransfoXLModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -75,20 +75,6 @@ XLMForSequenceClassification
    :members:


-XLMForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMForMultipleChoice
-    :members:
-
-
-XLMForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMForTokenClassification
-    :members:
-
-
 XLMForQuestionAnsweringSimple
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -74,24 +74,6 @@ XLNet specific outputs
 .. autoclass:: transformers.modeling_xlnet.XLNetForQuestionAnsweringOutput
    :members:

-.. autoclass:: transformers.modeling_tf_xlnet.TFXLNetModelOutput
-    :members:
-
-.. autoclass:: transformers.modeling_tf_xlnet.TFXLNetLMHeadModelOutput
-    :members:
-
-.. autoclass:: transformers.modeling_tf_xlnet.TFXLNetForSequenceClassificationOutput
-    :members:
-
-.. autoclass:: transformers.modeling_tf_xlnet.TFXLNetForMultipleChoiceOutput
-    :members:
-
-.. autoclass:: transformers.modeling_tf_xlnet.TFXLNetForTokenClassificationOutput
-    :members:
-
-.. autoclass:: transformers.modeling_tf_xlnet.TFXLNetForQuestionAnsweringSimpleOutput
-    :members:
-

 XLNetModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -1,11 +1,11 @@
 Summary of the models
 ================================================

-This is a summary of the models available in 🤗 Transformers. It assumes you’re familiar with the original
-`transformer model <https://arxiv.org/abs/1706.03762>`_. For a gentle introduction check the `annotated transformer
+This is a summary of the models available in 🤗 Transformers. It assumes you’re familiar with the original 
+`transformer model <https://arxiv.org/abs/1706.03762>`_. For a gentle introduction check the `annotated transformer 
 <http://nlp.seas.harvard.edu/2018/04/03/attention.html>`_. Here we focus on the high-level differences between the
-models. You can check them more in detail in their respective documentation. Also checkout the
-:doc:`pretrained model page </pretrained_models>` to see the checkpoints available for each type of model and all `the
+models. You can check them more in detail in their respective documentation. Also checkout the 
+:doc:`pretrained model page </pretrained_models>` to see the checkpoints available for each type of model and all `the 
 community models <https://huggingface.co/models>`_.

 Each one of the models in the library falls into one of the following categories:
@@ -15,37 +15,37 @@ Each one of the models in the library falls into one of the following categories
  * :ref:`seq-to-seq-models`
  * :ref:`multimodal-models`

-Autoregressive models are pretrained on the classic language modeling task: guess the next token having read all the
-previous ones. They correspond to the decoder of the original transformer model, and a mask is used on top of the full
-sentence so that the attention heads can only see what was before in the next, and not what’s after. Although those
-models can be fine-tuned and achieve great results on many tasks, the most natural application is text generation.
+Autoregressive models are pretrained on the classic language modeling task: guess the next token having read all the 
+previous ones. They correspond to the decoder of the original transformer model, and a mask is used on top of the full 
+sentence so that the attention heads can only see what was before in the next, and not what’s after. Although those 
+models can be fine-tuned and achieve great results on many tasks, the most natural application is text generation. 
 A typical example of such models is GPT.

-Autoencoding models are pretrained by corrupting the input tokens in some way and trying to reconstruct the original
-sentence. They correspond to the encoder of the original transformer model in the sense that they get access to the
-full inputs without any mask. Those models usually build a bidirectional representation of the whole sentence. They can
-be fine-tuned and achieve great results on many tasks such as text generation, but their most natural application is
+Autoencoding models are pretrained by corrupting the input tokens in some way and trying to reconstruct the original 
+sentence. They correspond to the encoder of the original transformer model in the sense that they get access to the 
+full inputs without any mask. Those models usually build a bidirectional representation of the whole sentence. They can 
+be fine-tuned and achieve great results on many tasks such as text generation, but their most natural application is 
 sentence classification or token classification. A typical example of such models is BERT.

-Note that the only difference between autoregressive models and autoencoding models is in the way the model is
+Note that the only difference between autoregressive models and autoencoding models is in the way the model is 
 pretrained. Therefore, the same architecture can be used for both autoregressive and autoencoding models. When a given
-model has been used for both types of pretraining, we have put it in the category corresponding to the article where it was first
+model has been used for both pretraining, we have put it in the category corresponding to the article it was first
 introduced.

-Sequence-to-sequence models use both the encoder and the decoder of the original transformer, either for translation
-tasks or by transforming other tasks to sequence-to-sequence problems. They can be fine-tuned to many tasks but their
-most natural applications are translation, summarization and question answering. The original transformer model is an
+Sequence-to-sequence models use both the encoder and the decoder of the original transformer, either for translation 
+tasks or by transforming other tasks to sequence-to-sequence problems. They can be fine-tuned to many tasks but their 
+most natural applications are translation, summarization and question answering. The original transformer model is an 
 example of such a model (only for translation), T5 is an example that can be fine-tuned on other tasks.

-Multimodal models mix text inputs with other kinds (e.g. images) and are more specific to a given task.
+Multimodal models mix text inputs with other kinds (like image) and are more specific to a given task.

 .. _autoregressive-models:

 Autoregressive models
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-As mentioned before, these models rely on the decoder part of the original transformer and use an attention mask so
-that at each position, the model can only look at the tokens before the attention heads.
+As mentioned before, these models rely on the decoder part of the original transformer and use an attention mask so 
+that at each position, the model can only look at the tokens before in the attention heads.

 Original GPT
 ----------------------------------------------
@@ -59,12 +59,12 @@ Original GPT
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-openai--gpt-blueviolet">
   </a>

-`Improving Language Understanding by Generative Pre-Training <https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf>`_,
+`Improving Language Understanding by Generative Pre-Training <https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf>`_, 
 Alec Radford et al.

 The first autoregressive model based on the transformer architecture, pretrained on the Book Corpus dataset.

-The library provides versions of the model for language modeling and multitask language modeling/multiple choice
+The library provides versions of the model for language modeling and multitask language modeling/multiple choice 
 classification.

 GPT-2
@@ -79,13 +79,13 @@ GPT-2
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-gpt2-blueviolet">
   </a>

-`Language Models are Unsupervised Multitask Learners <https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`_,
+`Language Models are Unsupervised Multitask Learners <https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`_, 
 Alec Radford et al.

-A bigger and better version of GPT, pretrained on WebText (web pages from outgoing links in Reddit with 3 karmas or
+A bigger and better version of GPT, pretrained on WebText (web pages from outgoing links in Reddit with 3 karmas or 
 more).

-The library provides versions of the model for language modeling and multitask language modeling/multiple choice
+The library provides versions of the model for language modeling and multitask language modeling/multiple choice 
 classification.

 CTRL
@@ -100,11 +100,11 @@ CTRL
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-ctrl-blueviolet">
   </a>

-`CTRL: A Conditional Transformer Language Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`_,
+`CTRL: A Conditional Transformer Language Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`_, 
 Nitish Shirish Keskar et al.

-Same as the GPT model but adds the idea of control codes. Text is generated from a prompt (can be empty) and one (or
-several) of those control codes which are then used to influence the text generation: generate with the style of
+Same as the GPT model but adds the idea of control codes. Text is generated from a prompt (can be empty) and one (or 
+several) of those control codes which are then used to influence the text generation: generate with the style of 
 wikipedia article, a book or a movie review.

 The library provides a version of the model for language modeling only.
@@ -121,19 +121,19 @@ Transformer-XL
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-transfo--xl-blueviolet">
   </a>

-`Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`_,
+`Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`_, 
 Zihang Dai et al.

-Same as a regular GPT model, but introduces a recurrence mechanism for two consecutive segments (similar to a regular
-RNNs with two consecutive inputs). In this context, a segment is a number of consecutive tokens (for instance 512) that
+Same as a regular GPT model, but introduces a recurrence mechanism for two consecutive segments (similar to a regular 
+RNNs with two consecutive inputs). In this context, a segment is a number of consecutive tokens (for instance 512) that 
 may span across multiple documents, and segments are fed in order to the model.

-Basically, the hidden states of the previous segment are concatenated to the current input to compute the attention
-scores. This allows the model to pay attention to information that was in the previous segment as well as the current
+Basically, the hidden states of the previous segment are concatenated to the current input to compute the attention 
+scores. This allows the model to pay attention to information that was in the previous segment as well as the current 
 one. By stacking multiple attention layers, the receptive field can be increased to multiple previous segments.

-This changes the positional embeddings to positional relative embeddings (as the regular positional embeddings would
-give the same results in the current input and the current hidden state at a given position) and needs to make some
+This changes the positional embeddings to positional relative embeddings (as the regular positional embeddings would 
+give the same results in the current input and the current hidden state at a given position) and needs to make some 
 adjustments in the way attention scores are computed.

 The library provides a version of the model for language modeling only.
@@ -155,16 +155,16 @@ Reformer
 `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_,
 Nikita Kitaev et al .

-An autoregressive transformer model with lots of tricks to reduce memory footprint and compute time. Those tricks
+An autoregressive transformer model with lots of tricks to reduce memory footprint and compute time. Those tricks 
 include:

-  * Use :ref:`Axial position encoding <axial-pos-encoding>` (see below for more details). It’s a mechanism to avoid
-    having a huge positional encoding matrix (when the sequence length is very big) by factorizing it into smaller
+  * Use :ref:`Axial position encoding <axial-pos-encoding>` (see below for more details). It’s a mechanism to avoid 
+    having a huge positional encoding matrix (when the sequence length is very big) by factorizing it in smaller 
    matrices.
-  * Replace traditional attention by :ref:`LSH (local-sensitive hashing) attention <lsh-attention>` (see below for more
-    details). It's a technique to avoid computing the full product query-key in the attention layers.
-  * Avoid storing the intermediate results of each layer by using reversible transformer layers to obtain them during
-    the backward pass (subtracting the residuals from the input of the next layer gives them back) or recomputing them
+  * Replace traditional attention by :ref:`LSH (local-sensitive hashing) attention <lsh-attention>` (see below for more 
+    details). It's a technique to avoid compute the full product query-key in the attention layers.
+  * Avoid storing the intermediate results of each layer by using reversible transformer layers to obtain them during 
+    the backward pass (subtracting the residuals from the input of the next layer gives them back) or recomputing them 
    for results inside a given layer (less efficient than storing them but saves memory).
  * Compute the feedforward operations by chunks and not on the whole batch.

@@ -190,14 +190,14 @@ XLNet
 `XLNet: Generalized Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_,
 Zhilin Yang et al.

-XLNet is not a traditional autoregressive model but uses a training strategy that builds on that. It permutes the
-tokens in the sentence, then allows the model to use the last n tokens to predict the token n+1. Since this is all done
-with a mask, the sentence is actually fed in the model in the right order, but instead of masking the first n tokens
+XLNet is not a traditional autoregressive model but uses a training strategy that builds on that. It permutes the 
+tokens in the sentence, then allows the model to use the last n tokens to predict the token n+1. Since this is all done 
+with a mask, the sentence is actually fed in the model in the right order, but instead of masking the first n tokens 
 for n+1, XLNet uses a mask that hides the previous tokens in some given permutation of 1,...,sequence length.

-XLNet also uses the same recurrence mechanism as Transformer-XL to build long-term dependencies.
+XLNet also uses the same recurrence mechanism as TransformerXL to build long-term dependencies. 

-The library provides a version of the model for language modeling, token classification, sentence classification,
+The library provides a version of the model for language modeling, token classification, sentence classification, 
 multiple choice classification and question answering.

 .. _autoencoding-models:
@@ -206,7 +206,8 @@ Autoencoding models
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

 As mentioned before, these models rely on the encoder part of the original transformer and use no mask so the model can
-look at all the tokens in the attention heads. For pretraining, targets are the original sentences and inputs are their corrupted versions.
+look at all the tokens in the attention heads. For pretraining, inputs are a corrupted version of the sentence, usually 
+obtained by masking tokens, and targets are the original sentences.

 BERT
 ----------------------------------------------
@@ -223,18 +224,18 @@ BERT
 `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`_,
 Jacob Devlin et al.

-Corrupts the inputs by using random masking, more precisely, during pretraining, a given percentage of tokens (usually
-15%) is masked by:
-
+Corrupts the inputs by using random masking, more precisely, during pretraining, a given percentage of tokens (usually 
+15%) are masked by
+ 
  * a special mask token with probability 0.8
  * a random token different from the one masked with probability 0.1
  * the same token with probability 0.1

-The model must predict the original sentence, but has a second objective: inputs are two sentences A and B (with a
-separation token in between). With probability 50%, the sentences are consecutive in the corpus, in the remaining 50%
+The model must predict the original sentence, but has a second objective: inputs are two sentences A and B (with a 
+separation token in between). With probability 50%, the sentences are consecutive in the corpus, in the remaining 50% 
 they are not related. The model has to predict if the sentences are consecutive or not.

-The library provides a version of the model for language modeling (traditional or masked), next sentence prediction,
+The library provides a version of the model for language modeling (traditional or masked), next sentence prediction, 
 token classification, sentence classification, multiple choice classification and question answering.

 ALBERT
@@ -254,16 +255,16 @@ Zhenzhong Lan et al.

 Same as BERT but with a few tweaks:

-  * Embedding size E is different from hidden size H justified because the embeddings are context independent (one
-    embedding vector represents one token), whereas hidden states are context dependent (one hidden state represents a
-    sequence of tokens) so it's more logical to have H >> E. Also, the embedding matrix is large since it's V x E (V
+  * Embedding size E is different from hidden size H justified because the embeddings are context independent (one 
+    embedding vector represents one token) whereas hidden states are context dependent (one hidden state represents a 
+    sequence of tokens) so it's more logical to have H >> E. Als, the embedding matrix is large since it's V x E (V 
    being the vocab size). If E < H, it has less parameters.
  * Layers are split in groups that share parameters (to save memory).
-  * Next sentence prediction is replaced by a sentence ordering prediction: in the inputs, we have two sentences A and B
-    (that are consecutive) and we either feed A followed by B or B followed by A. The model must predict if they have
+  * Next sentence prediction is replaced by a sentence ordering prediction: in the inputs, we have two sentences A et B 
+    (that are consecutive) and we either feed A followed by B or B followed by A. The model must predict if they have 
    been swapped or not.

-The library provides a version of the model for masked language modeling, token classification, sentence
+The library provides a version of the model for masked language modeling, token classification, sentence 
 classification, multiple choice classification and question answering.

 RoBERTa
@@ -283,13 +284,13 @@ Yinhan Liu et al.

 Same as BERT with better pretraining tricks:

-  * dynamic masking: tokens are masked differently at each epoch, whereas BERT does it once and for all
-  * no NSP (next sentence prediction) loss and instead of putting just two sentences together, put a chunk of
-    contiguous texts together to reach 512 tokens (so the sentences are in an order than may span several documents)
+  * dynamic masking: tokens are masked differently at each epoch whereas BERT does it once and for all
+  * no NSP (next sentence prediction) loss and instead of putting just two sentences together, put a chunk of 
+    contiguous texts together to reach 512 tokens (so sentences in in an order than may span other several documents)
  * train with larger batches
  * use BPE with bytes as a subunit and not characters (because of unicode characters)

-The library provides a version of the model for masked language modeling, token classification, sentence
+The library provides a version of the model for masked language modeling, token classification, sentence 
 classification, multiple choice classification and question answering.

 DistilBERT
@@ -307,14 +308,14 @@ DistilBERT
 `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`_,
 Victor Sanh et al.

-Same as BERT but smaller. Trained by distillation of the pretrained BERT model, meaning it's been trained to predict
+Same as BERT but smaller. Trained by distillation of the pretrained BERT model, meaning it's been trained to predict 
 the same probabilities as the larger model. The actual objective is a combination of:

  * finding the same probabilities as the teacher model
  * predicting the masked tokens correctly (but no next-sentence objective)
  * a cosine similarity between the hidden states of the student and the teacher model

-The library provides a version of the model for masked language modeling, token classification, sentence classification
+The library provides a version of the model for masked language modeling, token classification, sentence classification 
 and question answering.

 XLM
@@ -331,24 +332,25 @@ XLM

 `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_, Guillaume Lample and Alexis Conneau

-A transformer model trained on several languages. There are three different type of training for this model and the
+A transformer model trained on several languages. There are three different type of training for this model and the 
 library provides checkpoints for all of them:

-  * Causal language modeling (CLM) which is the traditional autoregressive training (so this model could be in the
-    previous section as well). One of the languages is selected for each training sample, and the model input is a
-    sentence of 256 tokens, that may span over several documents in one of those languages.
-  * Masked language modeling (MLM) which is like RoBERTa. One of the languages is selected for each training sample,
-    and the model input is a sentence of 256 tokens, that may span over several documents in one of those languages, with
+  * Causal language modeling (CLM) which is the traditional autoregressive training (so this model could be in the 
+    previous section as well). One of the languages is selected for each training sample, and the model input is a 
+    sentence of 256 tokens that may span on several documents in one one those languages.
+  * Masked language modeling (MLM) which is like RoBERTa. One of the languages is selected for each training sample, 
+    and the model input is a sentence of 256 tokens that may span on several documents in one one those languages, with
    dynamic masking of the tokens.
-  * A combination of MLM and translation language modeling (TLM). This consists of concatenating a sentence in two
-    different languages, with random masking. To predict one of the masked tokens, the model can use both, the
-    surrounding context in language 1 and the context given by language 2.
+  * A combination of MLM and translation language modeling (TLM). This consists of concatenating a sentence in two 
+    different languages, with random masking. To predict one of the masked token, the model can use both the 
+    surrounding context in language 1 as well as the context given by language 2.

 Checkpoints refer to which method was used for pretraining by having `clm`, `mlm` or `mlm-tlm` in their names. On top
 of positional embeddings, the model has language embeddings. When training using MLM/CLM, this gives the model an
-indication of the language used, and when training using MLM+TLM, an indication of the language used for each part.
+indication of the language used, and when training using MLM+TLM, an indication of which part of the input is in which
+language.

-The library provides a version of the model for language modeling, token classification, sentence classification and
+The library provides a version of the model for language modeling, token classification, sentence classification and 
 question answering.

 XLM-RoBERTa
@@ -363,14 +365,14 @@ XLM-RoBERTa
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlm--roberta-blueviolet">
   </a>

-`Unsupervised Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`_, Alexis Conneau et
+`Unsupervised Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`_, Alexis Conneau et 
 al.

-Uses RoBERTa tricks on the XLM approach, but does not use the translation language modeling objective. It only uses
-masked language modeling on sentences coming from one language. However, the model is trained on many more languages
+Uses RoBERTa tricks on the XLM approach, but does not use the translation language modeling objective, only using 
+masked language modeling on sentences coming from one language. However, the model is trained on many more languages 
 (100) and doesn't use the language embeddings, so it's capable of detecting the input language by itself.

-The library provides a version of the model for masked language modeling, token classification, sentence
+The library provides a version of the model for masked language modeling, token classification, sentence 
 classification, multiple choice classification and question answering.

 FlauBERT
@@ -403,16 +405,16 @@ ELECTRA
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-electra-blueviolet">
   </a>

-`ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators <https://arxiv.org/abs/2003.10555>`_,
+`ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators <https://arxiv.org/abs/2003.10555>`_, 
 Kevin Clark et al.

-ELECTRA is a transformer model pretrained with the use of another (small) masked language model. The inputs are
-corrupted by that language model, which takes an input text that is randomly masked and outputs a text in which ELECTRA
-has to predict which token is an original and which one has been replaced. Like for GAN training, the small language
-model is trained for a few steps (but with the original texts as objective, not to fool the ELECTRA model like in a
+ELECTRA is a transformer model pretrained with the use of another (small) masked language model. The inputs are 
+corrupted by that language model, which takes an input text that is randomly masked and outputs a text in which ELECTRA 
+has to predict which token is an original and which one has been replaced. Like for GAN training, the small language 
+model is trained for a few steps (but with the original texts as objective, not to fool the ELECTRA model like in a 
 traditional GAN setting) then the ELECTRA model is trained for a few steps.

-The library provides a version of the model for masked language modeling, token classification and sentence
+The library provides a version of the model for masked language modeling, token classification and sentence 
 classification.

 .. _longformer:
@@ -431,9 +433,9 @@ Longformer

 `Longformer: The Long-Document Transformer <https://arxiv.org/abs/2004.05150>`_, Iz Beltagy et al.

-A transformer model replacing the attention matrices by sparse matrices to go faster. Often, the local context (e.g.,
-what are the two tokens left and right?) is enough to take action for a given token. Some preselected input tokens are
-still given global attention, but the attention matrix has way less parameters, resulting in a speed-up. See the
+A transformer model replacing the attention matrices by sparse matrices to go faster. Often, the local context (e.g., 
+what are the two tokens left and right?) is enough to take action for a given token. Some preselected input tokens are 
+still given global attention, but the attention matrix has way less parameters, resulting in a speed-up. See the 
 :ref:`local attention section <local-attention>` for more information.

 It is pretrained the same way a RoBERTa otherwise.
@@ -441,7 +443,7 @@ It is pretrained the same way a RoBERTa otherwise.
 **Note:** This model could be very well be used in an autoregressive setting, there is no checkpoint for such a
 pretraining yet, though.

-The library provides a version of the model for masked language modeling, token classification, sentence
+The library provides a version of the model for masked language modeling, token classification, sentence 
 classification, multiple choice classification and question answering.

 .. _seq-to-seq-models:
@@ -463,46 +465,21 @@ BART
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-bart-blueviolet">
   </a>

-`BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
+`BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension 
 <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, on the
+Sequence-to-sequence model with an encoder and a decoder. Encoder is fed a corrupted version of the tokens, decoder is 
+fed the tokens (but has a mask to hide the future words like a regular transformers decoder). For the encoder, on the 
 pretraining tasks, a composition of the following transformations are applied:

  * mask random tokens (like in BERT)
  * delete random tokens
  * mask a span of k tokens with a single mask token (a span of 0 tokens is an insertion of a mask token)
  * permute sentences
-  * rotate the document to make it start at a specific token
+  * rotate the document to make it start by a specific token

 The library provides a version of this model for conditional generation and sequence classification.

-Pegasus
----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=pegasus">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-pegasus-blueviolet">
-   </a>
-   <a href="model_doc/pegasus.html">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-pegasus-blueviolet">
-   </a>
-
-`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 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 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.
-
-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.
-
-The library provides a version of this model for conditional generation, which should be used for summarization.
-
-
 MarianMT
 ----------------------------------------------

@@ -533,71 +510,47 @@ T5
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-t5-blueviolet">
   </a>

-`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`_,
+`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`_, 
 Colin Raffel et al.

-Uses the traditional transformer model (with a slight change in the positional embeddings, which are learned at
-each layer). To be able to operate on all NLP tasks, it transforms them into text-to-text problems by using specific
-prefixes: “summarize: ”, “question: ”, “translate English to German: ” and so forth.
+Uses the traditional transformer model (except a slight change with the positional embeddings, which are learned at 
+each layer). To be able to operate on all NLP tasks, it transforms them in text-to-text problems by using certain 
+prefixes: “Summarize: …”, “question: …”, “translate English to German: …” and so forth.

-The pretraining includes both supervised and self-supervised training. Supervised training is conducted on downstream
-tasks provided by the GLUE and SuperGLUE benchmarks (converting them into text-to-text tasks as explained above).
+The pretraining includes both supervised and self-supervised training. Supervised training is conducted on downstream 
+tasks provided by the GLUE and SuperGLUE benchmarks (changing them to 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 is replaced with a single sentinel token). The input of the encoder is the corrupted sentence, the input of the decoder is the
+Self-supervised training consists of corrupted pretrained, which means randomly removing 15% of the tokens and 
+replacing them by individual sentinel tokens (if several consecutive tokens are marked for removal, they are replaced 
+by one single sentinel token). The input of the encoder is the corrupted sentence, the input of the decoder 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 "cute", the encoder
-input becomes “My <x> very <y> .” and the target input becomes “<x> dog is <y> cute .<z>”
+For instance, if we have the sentence “My dog is very cute .”, and we decide to remove the token dog, is and cute, the 
+input becomes “My <x> very <y> .” and the target is “<x> dog is <y> . <z>”

 The library provides a version of this model for conditional generation.

-MBart
----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=mbart">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-mbart-blueviolet">
-   </a>
-   <a href="model_doc/mbart.html">
-       <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 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 
-and is intended for supervised and unsupervised machine translation. MBart is one of the first methods 
-for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages,
-
-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 -> romanian translation.
-
-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.
-
 .. _multimodal-models:

 Multimodal models
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-There is one multimodal model in the library which has not been pretrained in the self-supervised fashion like the
+There is one multimodal model in the library which has not been pretrained in the self-supervised fashion like the 
 others.

 MMBT
 ----------------------------------------------

-`Supervised Multimodal Bitransformers for Classifying Images and Text <https://arxiv.org/abs/1909.02950>`_, Douwe Kiela
+`Supervised Multimodal Bitransformers for Classifying Images and Text <https://arxiv.org/abs/1909.02950>`_, Douwe Kiela 
 et al.

-A transformers model used in multimodal settings, combining a text and an image to make predictions. The transformer
-model takes as inputs the embeddings of the tokenized text and the final activations of a pretrained on images resnet
-(after the pooling layer) that goes through a linear layer (to go from number of features at the end of the
+A transformers model used in multimodal settings, combining a text and an image to make predictions. The transformer 
+model takes as inputs the embeddings of the tokenized text and a the final activations of a pretrained resnet on the 
+images (after the pooling layer) that goes through a linear layer (to go from number of features at the end of the 
 resnet to the hidden state dimension of the transformer).

-The different inputs are concatenated, and on top of the positional embeddings, a segment embedding is added to let the
-model know which part of the input vector corresponds to the text and which to the image.
+The different inputs are concatenated, and on top of the positional embeddings, a segment embedding is added to let the 
+model know which part of the input vector corresponds to the text or the image.

 The pretrained model only works for classification.

@@ -611,39 +564,39 @@ More technical aspects
 Full vs sparse attention
 ----------------------------------------------

-Most transformer models use full attention in the sense that the attention matrix is square. It can be a big
-computational bottleneck when you have long texts. Longformer and reformer are models that try to be more efficient and
+Most transformer models use full attention in the sense that the attention matrix is square. It can be a big 
+computational bottleneck when you have long texts. Longformer and reformer are models that try to be more efficient and 
 use a sparse version of the attention matrix to speed up training.

 .. _lsh-attention:

 **LSH attention**

-:ref:`Reformer <reformer>` uses LSH attention. In the softmax(QK^t), only the biggest elements (in the softmax
-dimension) of the matrix QK^t are going to give useful contributions. So for each query q in Q, we can  consider only
-the keys k in K that are close to q. A hash function is used to determine if q and k are close. The attention mask is
-modified to mask the current token (except at the first position), because it will give a query and a key equal (so very
-similar to each other). Since the hash can be a bit random, several hash functions are used in practice (determined by
-a n_rounds parameter) and then are averaged together.
+:ref:`Reformer <reformer>` uses LSH attention. In the softmax(QK^t), only the biggest elements (in the softmax 
+dimension) of the matrix QK^t are going to give useful contributions. So for each query q in Q, we can only consider 
+the keys k in K that are close to q. A hash function is used to determine if q and k are close. The attention mask is 
+modified to mask the current token (except at the first position) because it will give a query and key equal (so very 
+similar to each other). Since the hash can be a bit random, several hash functions are used in practice (determined by 
+a n_rounds parameter) then are averaged together.

 .. _local-attention:

 **Local attention**

-:ref:`Longformer <longformer>` uses local attention: often, the local context (e.g., what are the two tokens to the left and
-right?) is enough to take action for a given token. Also, by stacking attention layers that have a small window, the
-last layer will have a receptive field of more than just the tokens in the window, allowing them to build a
+:ref:`Longformer <longformer>` uses local attention: often, the local context (e.g., what are the two tokens left and 
+right?) is enough to take action for a given token. Also, by stacking attention layers that have a small window, the 
+last layer will have a receptive field of more than just the tokens on the window, allowing them to build a 
 representation of the whole sentence.

-Some preselected input tokens are also given global attention: for those few tokens, the attention matrix can access
-all tokens and this process is symmetric: all other tokens have access to those specific tokens (on top of the ones in
+Some preselected input tokens are also given global attention: for those few tokens, the attention matrix can access 
+all tokens and this process is symmetric: all other tokens have access to those specific tokens (on top of the ones in 
 their local window). This is shown in Figure 2d of the paper, see below for a sample attention mask:

 .. image:: imgs/local_attention_mask.png
   :scale: 50 %
   :align: center

-Using those attention matrices with less parameters then allows the model to have inputs having a bigger sequence
+Using those attention matrices with less parameters then allows the model to have inputs having a bigger sequence 
 length.

 Other tricks
@@ -653,10 +606,13 @@ Other tricks

 **Axial positional encodings**

-:ref:`Reformer <reformer>` uses axial positional encodings: in traditional transformer models, the positional encoding
-E is a matrix of size :math:`l` by :math:`d`, :math:`l` being the sequence length and :math:`d` the dimension of the
-hidden state. If you have very long texts, this matrix can be huge and take way too much space on the GPU. To alleviate that, axial positional encodings consist of factorizing that big matrix E in two smaller matrices E1 and
+:ref:`Reformer <reformer>` uses axial positional encodings: in traditional transformer models, the positional encoding 
+E is a matrix of size :math:`l` by :math:`d`, :math:`l` being the sequence length and :math:`d` the dimension of the 
+hidden state. If you have very long texts, this matrix can be huge and take way too much space on the GPU.
+
+To alleviate that, axial positional encodings consists in factorizing that big matrix E in two smaller matrices E1 and 
 E2, with dimensions :math:`l_{1} \times d_{1}` and :math:`l_{2} \times d_{2}`, such that :math:`l_{1} \times l_{2} = l`
-and :math:`d_{1} + d_{2} = d` (with the product for the lengths, this ends up being way smaller). The embedding for
-time step :math:`j` in E is obtained by concatenating the embeddings for timestep :math:`j \% l1` in E1 and
+and :math:`d_{1} + d_{2} = d` (with the product for the lengths, this ends up being way smaller). The embedding for 
+time step :math:`j` in E is obtained by concatenating the embeddings for timestep :math:`j \% l1` in E1 and 
 :math:`j // l1` in E2.
+
@@ -123,7 +123,7 @@ are 512 preceding tokens available to condition on).
    stride = 512

    lls = []
-    for i in tqdm(range(0, encodings.input_ids.size(1), stride)):
+    for i in tqdm(range(1, encodings.input_ids.size(1), stride)):
        begin_loc = max(i + stride - max_length, 0)
        end_loc = i + stride
        input_ids = encodings.input_ids[:,begin_loc:end_loc].to(device)
@@ -45,12 +45,12 @@ A few other goals:
    - A simple/consistent way to add new tokens to the vocabulary and embeddings for fine-tuning.
    - Simple ways to mask and prune transformer heads.

- Switch easily between PyTorch and TensorFlow 2.0, allowing training using one framework and inference using another.
+- Switch easily between PyTorch and TensorFlow 2.0, allowing training using one framwork and inference using another.

 Main concepts
 ~~~~~~~~~~~~~

-The library is built around three types of classes for each model:
+The library is build around three types of classes for each model:

 - **Model classes**  such as :class:`~transformers.BertModel`, which are 30+ PyTorch models 
  (`torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__) or Keras models 
@@ -65,9 +65,9 @@ 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
+- :obj:`from_pretrained()` let you instantiate a model/configuration/tokenizer from a pretrained version either
  provided by the library itself (the suported models are provided in the list :doc:`here <pretrained_models>`
  or stored locally (or on a server) by the user,
- :obj:`save_pretrained()` lets you save a model/configuration/tokenizer locally so that it can be reloaded using
+- :obj:`save_pretrained()` let you save a model/configuration/tokenizer locally so that it can be reloaded using
  :obj:`from_pretrained()`.

@@ -20,7 +20,7 @@ work properly.
 To automatically download the vocab used during pretraining or fine-tuning a given model, you can use the 
 :func:`~transformers.AutoTokenizer.from_pretrained` method:

-.. code-block::
+::

    from transformers import AutoTokenizer
    tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
@@ -31,41 +31,56 @@ Base use
 A :class:`~transformers.PreTrainedTokenizer` has many methods, but the only one you need to remember for preprocessing
 is its ``__call__``: you just need to feed your sentence to your tokenizer object.

-.. code-block::
+::
+
+    encoded_input = tokenizer("Hello, I'm a single sentence!")
+    print(encoded_input)
+
+This will return a dictionary string to list of ints like this one:
+
+::

-    >>> encoded_input = tokenizer("Hello, I'm a single sentence!")
-    >>> print(encoded_input)
    {'input_ids': [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102], 
     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}

-This returns a dictionary string to list of ints.
 The `input_ids <glossary.html#input-ids>`__ are the indices corresponding to each token in our sentence. We will see
 below what the `attention_mask <glossary.html#attention-mask>`__ is used for and in
 :ref:`the next section <sentence-pairs>` the goal of `token_type_ids <glossary.html#token-type-ids>`__.

 The tokenizer can decode a list of token ids in a proper sentence:

-.. code-block::
+::
+
+    tokenizer.decode(encoded_input["input_ids"])
+
+which should return
+
+::

-    >>> tokenizer.decode(encoded_input["input_ids"])
    "[CLS] Hello, I'm a single sentence! [SEP]"

 As you can see, the tokenizer automatically added some special tokens that the model expect. Not all model need special
 tokens; for instance, if we had used` gtp2-medium` instead of `bert-base-cased` to create our tokenizer, we would have
+
 seen the same sentence as the original one here. You can disable this behavior (which is only advised if you have added
 those special tokens yourself) by passing ``add_special_tokens=False``.

 If you have several sentences you want to process, you can do this efficiently by sending them as a list to the
 tokenizer:

-.. code-block::
+::
+
+    batch_sentences = ["Hello I'm a single sentence",
+                       "And another sentence",
+                       "And the very very last one"]
+    encoded_inputs = tokenizer(batch_sentences)
+    print(encoded_inputs)
+
+We get back a dictionary once again, this time with values being list of list of ints:
+
+::

-    >>> batch_sentences = ["Hello I'm a single sentence",
-    ...                    "And another sentence",
-    ...                    "And the very very last one"]
-    >>> encoded_inputs = tokenizer(batch_sentences)
-    >>> print(encoded_inputs)
    {'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
                   [101, 1262, 1330, 5650, 102],
                   [101, 1262, 1103, 1304, 1304, 1314, 1141, 102]],
@@ -76,8 +91,6 @@ tokenizer:
                        [1, 1, 1, 1, 1],
                        [1, 1, 1, 1, 1, 1, 1, 1]]}

-We get back a dictionary once again, this time with values being list of list of ints.
-
 If the purpose of sending several sentences at a time to the tokenizer is to build a batch to feed the model, you will
 probably want:

@@ -87,11 +100,19 @@ probably want:

 You can do all of this by using the following options when feeding your list of sentences to the tokenizer:

-.. code-block::
+::
+
+    ## PYTORCH CODE
+    batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
+    print(batch)
+    ## TENSORFLOW CODE
+    batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="tf")
+    print(batch)
+
+which should now return a dictionary string to tensor like this:
+
+::

-    >>> ## PYTORCH CODE
-    >>> batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
-    >>> print(batch)
    {'input_ids': tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
                          [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
                          [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
@@ -101,22 +122,9 @@ You can do all of this by using the following options when feeding your list of
     'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
                               [1, 1, 1, 1, 1, 0, 0, 0, 0],
                               [1, 1, 1, 1, 1, 1, 1, 1, 0]])}
-    >>> ## TENSORFLOW CODE
-    >>> batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="tf")
-    >>> print(batch)
-    {'input_ids': tf.Tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
-                          [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
-                          [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
-     'token_type_ids': tf.Tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
-     'attention_mask': tf.Tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
-                               [1, 1, 1, 1, 1, 0, 0, 0, 0],
-                               [1, 1, 1, 1, 1, 1, 1, 1, 0]])}

-It returns a dictionary string to tensor. We can now see what the `attention_mask <glossary.html#attention-mask>`__ is
-all about: it points out which tokens the model should pay attention to and which ones it should not (because they
-represent padding in this case).
+We can now see what the `attention_mask <glossary.html#attention-mask>`__ is all about: it points out which tokens the
+model should pay attention to and which ones it should not (because they represent padding in this case).


 Note that if your model does not have a maximum length associated to it, the command above will throw a warning. You
@@ -129,16 +137,26 @@ Preprocessing pairs of sentences

 Sometimes you need to feed pair of sentences to your model. For instance, if you want to classify if two sentences in a
 pair are similar, or for question-answering models, which take a context and a question. For BERT models, the input is
-then represented like this: :obj:`[CLS] Sequence A [SEP] Sequence B [SEP]`
+then represented like this:
+
+::
+
+    [CLS] Sequence A [SEP] Sequence B [SEP]

 You can encode a pair of sentences in the format expected by your model by supplying the two sentences as two arguments
+
 (not a list since a list of two sentences will be interpreted as a batch of two single sentences, as we saw before).
+
+
+::
+
+    encoded_input = tokenizer("How old are you?", "I'm 6 years old")
+    print(encoded_input)
+
 This will once again return a dict string to list of ints:

-.. code-block::
+::

-    >>> encoded_input = tokenizer("How old are you?", "I'm 6 years old")
-    >>> print(encoded_input)
    {'input_ids': [101, 1731, 1385, 1132, 1128, 136, 102, 146, 112, 182, 127, 1201, 1385, 102], 
     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], 
     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
@@ -151,24 +169,34 @@ using ``return_input_ids`` or ``return_token_type_ids``.

 If we decode the token ids we obtained, we will see that the special tokens have been properly added.

-.. code-block::
+::
+
+    tokenizer.decode(encoded_input["input_ids"])
+
+will return:
+
+::

-    >>> tokenizer.decode(encoded_input["input_ids"])
    "[CLS] How old are you? [SEP] I'm 6 years old [SEP]"

 If you have a list of pairs of sequences you want to process, you should feed them as two lists to your tokenizer: the
 list of first sentences and the list of second sentences:

-.. code-block::
+::
+
+    batch_sentences = ["Hello I'm a single sentence",
+                       "And another sentence",
+                       "And the very very last one"]
+    batch_of_second_sentences = ["I'm a sentence that goes with the first sentence",
+                                 "And I should be encoded with the second sentence",
+                                 "And I go with the very last one"]
+    encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences)
+    print(encoded_inputs)
+
+will return a dict with the values being list of lists of ints:
+
+::

-    >>> batch_sentences = ["Hello I'm a single sentence",
-    ...                    "And another sentence",
-    ...                    "And the very very last one"]
-    >>> batch_of_second_sentences = ["I'm a sentence that goes with the first sentence",
-    ...                              "And I should be encoded with the second sentence",
-    ...                              "And I go with the very last one"]
-    >>> encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences)
-    >>> print(encoded_inputs)
    {'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102, 146, 112, 182, 170, 5650, 1115, 2947, 1114, 1103, 1148, 5650, 102], 
                   [101, 1262, 1330, 5650, 102, 1262, 146, 1431, 1129, 12544, 1114, 1103, 1248, 5650, 102], 
                   [101, 1262, 1103, 1304, 1304, 1314, 1141, 102, 1262, 146, 1301, 1114, 1103, 1304, 1314, 1141, 102]], 
@@ -179,14 +207,17 @@ list of first sentences and the list of second sentences:
                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}

-As we can see, it returns a dictionary with the values being list of lists of ints.
-
 To double-check what is fed to the model, we can decode each list in `input_ids` one by one:

-.. code-block::
+::
+
+    for ids in encoded_inputs["input_ids"]:
+        print(tokenizer.decode(ids))
+
+which will return:
+
+::

-    >>> for ids in encoded_inputs["input_ids"]:
-    >>>     print(tokenizer.decode(ids))
    [CLS] Hello I'm a single sentence [SEP] I'm a sentence that goes with the first sentence [SEP]
    [CLS] And another sentence [SEP] And I should be encoded with the second sentence [SEP]
    [CLS] And the very very last one [SEP] And I go with the very last one [SEP]
@@ -194,7 +225,7 @@ To double-check what is fed to the model, we can decode each list in `input_ids`
 Once again, you can automatically pad your inputs to the maximum sentence length in the batch, truncate to the maximum
 length the model can accept and return tensors directly with the following:

-.. code-block::
+::

    ## PYTORCH CODE
    batch = tokenizer(batch_sentences, batch_of_second_sentences, padding=True, truncation=True, return_tensors="pt")
@@ -284,19 +315,18 @@ The tokenizer also accept pre-tokenized inputs. This is particularly useful when
 predictions in `named entity recognition (NER) <https://en.wikipedia.org/wiki/Named-entity_recognition>`__ or
 `part-of-speech tagging (POS tagging) <https://en.wikipedia.org/wiki/Part-of-speech_tagging>`__.

-.. warning::
-
-    Pre-tokenized does not mean your inputs are already tokenized (you wouldn't need to pass them though the tokenizer
-    if that was the case) but just split into words (which is often the first step in subword tokenization algorithms
-    like BPE).
-
 If you want to use pre-tokenized inputs, just set :obj:`is_pretokenized=True` when passing your inputs to the
-tokenizer. For instance, we have:
+tokenizer. For instance:

-.. code-block::
+::
+
+    encoded_input = tokenizer(["Hello", "I'm", "a", "single", "sentence"], is_pretokenized=True)
+    print(encoded_input)
+
+will return:
+
+::

-    >>> encoded_input = tokenizer(["Hello", "I'm", "a", "single", "sentence"], is_pretokenized=True)
-    >>> print(encoded_input)
    {'input_ids': [101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0], 
     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}
@@ -307,7 +337,7 @@ Note that the tokenizer still adds the ids of special tokens (if applicable) unl
 This works exactly as before for batch of sentences or batch of pairs of sentences. You can encode a batch of sentences
 like this:

-.. code-block::
+::

    batch_sentences = [["Hello", "I'm", "a", "single", "sentence"],
                       ["And", "another", "sentence"],
@@ -316,7 +346,7 @@ like this:

 or a batch of pair sentences like this:

-.. code-block::
+::

    batch_of_second_sentences = [["I'm", "a", "sentence", "that", "goes", "with", "the", "first", "sentence"],
                                 ["And", "I", "should", "be", "encoded", "with", "the", "second", "sentence"],
@@ -325,7 +355,7 @@ or a batch of pair sentences like this:

 And you can add padding, truncation as well as directly return tensors like before:

-.. code-block::
+::

    ## PYTORCH CODE
    batch = tokenizer(batch_sentences,
@@ -74,16 +74,14 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 |                   |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``cl-tohoku/bert-base-japanese``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies,                     |
-|                   |                                                            | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__.               |
-|                   |                                                            | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them.                  |
+|                   |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece.                                                               |
+|                   |                                                            | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization.                                                          |
 |                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``cl-tohoku/bert-base-japanese-whole-word-masking``        | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies,                     |
-|                   |                                                            | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__.               |
-|                   |                                                            | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them.                  |
+|                   |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized with MeCab and WordPiece.                                      |
+|                   |                                                            | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization.                                                          |
 |                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
@@ -331,6 +329,9 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``facebook/bart-large-cnn``                                | | 12-layer, 1024-hidden, 16-heads, 406M parameters       (same as base)                                                               |
 |                   |                                                            | | bart-large base architecture finetuned on cnn summarization task                                                                    |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``facebook/mbart-large-en-ro``                             | | 12-layer, 1024-hidden, 16-heads, 880M parameters                                                                                    |
+|                   |                                                            | | bart-large architecture pretrained on cc25 multilingual data , finetuned on WMT english romanian translation.                       |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | DialoGPT          | ``DialoGPT-small``                                         | | 12-layer, 768-hidden, 12-heads, 124M parameters                                                                                     |
 |                   |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
@@ -350,17 +351,9 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 | MarianMT          | ``Helsinki-NLP/opus-mt-{src}-{tgt}``                       | | 12-layer, 512-hidden, 8-heads, ~74M parameter Machine translation models. Parameter counts vary depending on vocab size.            |
 |                   |                                                            | | (see `model list <https://huggingface.co/Helsinki-NLP>`_)                                                                           |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Pegasus           | ``google/pegasus-{dataset}``                               | | 16-layer, 1024-hidden, 16-heads, ~568M parameter, 2.2 GB for summary. `model list <https://huggingface.co/models?search=pegasus>`__ |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | Longformer        | ``allenai/longformer-base-4096``                           | | 12-layer, 768-hidden, 12-heads, ~149M parameters                                                                                    |
 |                   |                                                            | | Starting from RoBERTa-base checkpoint, trained on documents of max length 4,096                                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``allenai/longformer-large-4096``                          | | 24-layer, 1024-hidden, 16-heads, ~435M parameters                                                                                   |
 |                   |                                                            | | Starting from RoBERTa-large checkpoint, trained on documents of max length 4,096                                                    |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| MBart             | ``facebook/mbart-large-cc25``                              | | 24-layer, 1024-hidden, 16-heads, 610M parameters                                                                                    |
-|                   |                                                            | | mBART (bart-large architecture) model trained on 25 languages' monolingual corpus                                                   |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``facebook/mbart-large-en-ro``                             | | 24-layer, 1024-hidden, 16-heads, 610M parameters                                                                                    |
-|                   |                                                            | | mbart-large-cc25 model finetuned on WMT english romanian translation.                                                               |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
@@ -128,7 +128,7 @@ Under the hood: pretrained models
 Let's now see what happens beneath the hood when using those pipelines. As we saw, the model and tokenizer are created
 using the :obj:`from_pretrained` method:

-.. code-block::
+::

    >>> ## PYTORCH CODE
    >>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
@@ -146,7 +146,7 @@ Using the tokenizer

 We mentioned the tokenizer is responsible for the preprocessing of your texts. First, it will split a given text in
 words (or part of words, punctuation symbols, etc.) usually called `tokens`. There are multiple rules that can govern
-that process (you can learn more about them in the :doc:`tokenizer summary <tokenizer_summary>`, which is why we need
+that process (you can learn more about them in the :doc:`tokenizer_summary <tokenizer_summary>`, which is why we need
 to instantiate the tokenizer using the name of the model, to make sure we use the same rules as when the model was
 pretrained.

@@ -235,11 +235,9 @@ final activations of the model.
    >>> ## TENSORFLOW CODE
    >>> print(tf_outputs)
    (<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
-    array([[-4.0832963 ,  4.336414  ],
-           [ 0.08181786, -0.04179301]], dtype=float32)>,)
+    array([[-4.0832963 ,  4.3364134 ],
+           [ 0.08181238, -0.04178794]], dtype=float32)>,)

-The model can return more than just the final activations, which is why the output is a tuple. Here we only asked for
-the final activations, so we get a tuple with one element.
 .. note::

    All 🤗 Transformers models (PyTorch or TensorFlow) return the activations of the model *before* the final
@@ -264,7 +262,7 @@ We can see we get the numbers from before:
    >>> print(tf_predictions)
    tf.Tensor(
    [[2.2042994e-04 9.9977952e-01]
-     [5.3086340e-01 4.6913657e-01]], shape=(2, 2), dtype=float32)
+     [5.3086078e-01 4.6913919e-01]], shape=(2, 2), dtype=float32)
    >>> ## PYTORCH CODE
    >>> print(pt_predictions)
    tensor([[2.2043e-04, 9.9978e-01],
@@ -287,15 +285,9 @@ training loop. 🤗 Transformers also provides a :class:`~transformers.Trainer`
 you are using TensorFlow) class to help with your training (taking care of things such as distributed training, mixed
 precision, etc.). See the :doc:`training tutorial <training>` for more details.

-.. note::
-
-    Pytorch model outputs are special dataclasses so that you can get autocompletion for their attributes in an IDE.
-    They also behave like a tuple or a dictionary (e.g., you can index with an integer, a slice or a string) in which
-    case the attributes not set (that have :obj:`None` values) are ignored.
-
 Once your model is fine-tuned, you can save it with its tokenizer in the following way:

-.. code-block::
+::

    tokenizer.save_pretrained(save_directory)
    model.save_pretrained(save_directory)
@@ -305,14 +297,14 @@ directory name instead of the model name. One cool feature of 🤗 Transformers
 PyTorch and TensorFlow: any model saved as before can be loaded back either in PyTorch or TensorFlow. If you are
 loading a saved PyTorch model in a TensorFlow model, use :func:`~transformers.TFAutoModel.from_pretrained` like this:

-.. code-block::
+::

    tokenizer = AutoTokenizer.from_pretrained(save_directory)
    model = TFAutoModel.from_pretrained(save_directory, from_pt=True)

 and if you are loading a saved TensorFlow model in a PyTorch model, you should use the following code:

-.. code-block::
+::

    tokenizer = AutoTokenizer.from_pretrained(save_directory)
    model = AutoModel.from_pretrained(save_directory, from_tf=True)
@@ -320,7 +312,7 @@ and if you are loading a saved TensorFlow model in a PyTorch model, you should u
 Lastly, you can also ask the model to return all hidden states and all attention weights if you need them:


-.. code-block::
+::

    >>> ## PYTORCH CODE
    >>> pt_outputs = pt_model(**pt_batch, output_hidden_states=True, output_attentions=True)
@@ -338,8 +330,8 @@ code is easy to access and tweak if you need to.

 In our previous example, the model was called "distilbert-base-uncased-finetuned-sst-2-english", which means it's
 using the :doc:`DistilBERT </model_doc/distilbert>` architecture. As
-:class:`~transformers.AutoModelForSequenceClassification` (or :class:`~transformers.TFAutoModelForSequenceClassification`
-if you are using TensorFlow) was used, the model automatically created is then a
+:class:`~transformers.AutoModelForSequenceClassification` (or  :class:`~transformers.TFAutoModelForSequenceClassification`
+if you are using TensorFlow)` was used, the model automatically created is then a
 :class:`~transformers.DistilBertForSequenceClassification`. You can look at its documentation for all details relevant
 to that specific model, or browse the source code. This is how you would directly instantiate model and tokenizer
 without the auto magic:
@@ -1,252 +0,0 @@
-**********************************************
-Exporting transformers models
-**********************************************
-
-ONNX / ONNXRuntime
-==============================================
-
-Projects `ONNX (Open Neural Network eXchange) <http://onnx.ai>`_ and `ONNXRuntime (ORT) <https://microsoft.github.io/onnxruntime/>`_ are part of an effort from leading industries in the AI field
-to provide a unified and community-driven format to store and, by extension, efficiently execute neural network leveraging a variety
-of hardware and dedicated optimizations.
-
-Starting from transformers v2.10.0 we partnered with ONNX Runtime to provide an easy export of transformers models to
-the ONNX format. You can have a look at the effort by looking at our joint blog post `Accelerate your NLP pipelines using
-Hugging Face Transformers and ONNX Runtime <https://medium.com/microsoftazure/accelerate-your-nlp-pipelines-using-hugging-face-transformers-and-onnx-runtime-2443578f4333>`_.
-
-Exporting a model is done through the script `convert_graph_to_onnx.py` at the root of the transformers sources.
-The following command shows how easy it is to export a BERT model from the library, simply run:
-
-.. code-block:: bash
-
-    python convert_graph_to_onnx.py --framework <pt, tf> --model bert-base-cased bert-base-cased.onnx
-
-The conversion tool works for both PyTorch and Tensorflow models and ensures:
-
-* The model and its weights are correctly initialized from the Hugging Face model hub or a local checkpoint.
-* The inputs and outputs are correctly generated to their ONNX counterpart.
-* The generated model can be correctly loaded through onnxruntime.
-
-.. note::
-    Currently, inputs and outputs are always exported with dynamic sequence axes preventing some optimizations
-    on the ONNX Runtime. If you would like to see such support for fixed-length inputs/outputs, please
-    open up an issue on transformers.
-
-
-Also, the conversion tool supports different options which let you tune the behavior of the generated model:
-
-* **Change the target opset version of the generated model.**  (More recent opset generally supports more operators and enables faster inference)
-
-* **Export pipeline-specific prediction heads.**  (Allow to export model along with its task-specific prediction head(s))
-
-* **Use the external data format (PyTorch only).**  (Lets you export model which size is above 2Gb (`More info <https://github.com/pytorch/pytorch/pull/33062>`_))
-
-
-Optimizations
------------------------------------------------
-
-ONNXRuntime includes some transformers-specific transformations to leverage optimized operations in the graph.
-Below are some of the operators which can be enabled to speed up inference through ONNXRuntime (*see note below*):
-
-* Constant folding
-* Attention Layer fusing
-* Skip connection LayerNormalization fusing
-* FastGeLU approximation
-
-Some of the optimizations performed by ONNX runtime can be hardware specific and thus lead to different performances
-if used on another machine with a different hardware configuration than the one used for exporting the model.
-For this reason, when using ``convert_graph_to_onnx.py`` optimizations are not enabled,
-ensuring the model can be easily exported to various hardware.
-Optimizations can then be enabled when loading the model through ONNX runtime for inference.
-
-
-.. note::
-    When quantization is enabled (see below), ``convert_graph_to_onnx.py`` script will enable optimizations on the model
-    because quantization would modify the underlying graph making it impossible for ONNX runtime to do the optimizations
-    afterwards.
-
-.. note::
-    For more information about the optimizations enabled by ONNXRuntime, please have a look at the (`ONNXRuntime Github <https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers>`_)
-
-Quantization
------------------------------------------------
-
-ONNX exporter supports generating a quantized version of the model to allow efficient inference.
-
-Quantization works by converting the memory representation of the parameters in the neural network
-to a compact integer format. By default, weights of a neural network are stored as single-precision float (`float32`)
-which can express a wide-range of floating-point numbers with decent precision.
-These properties are especially interesting at training where you want fine-grained representation.
-
-On the other hand, after the training phase, it has been shown one can greatly reduce the range and the precision of `float32` numbers
-without changing the performances of the neural network.
-
-More technically, `float32` parameters are converted to a type requiring fewer bits to represent each number, thus reducing
-the overall size of the model. Here, we are enabling `float32` mapping to `int8` values (a non-floating, single byte, number representation)
-according to the following formula:
-
-.. math::
-    y_{float32} = scale * x_{int8} - zero\_point
-
-.. note::
-    The quantization process will infer the parameter `scale` and `zero_point` from the neural network parameters
-
-Leveraging tiny-integers has numerous advantages when it comes to inference:
-
-* Storing fewer bits instead of 32 bits for the `float32` reduces the size of the model and makes it load faster.
-* Integer operations execute a magnitude faster on modern hardware
-* Integer operations require less power to do the computations
-
-In order to convert a transformers model to ONNX IR with quantized weights you just need to specify ``--quantize``
-when using ``convert_graph_to_onnx.py``. Also, you can have a look at the ``quantize()`` utility-method in this
-same script file.
-
-Example of quantized BERT model export:
-
-.. code-block:: bash
-
-    python convert_graph_to_onnx.py --framework <pt, tf> --model bert-base-cased --quantize bert-base-cased.onnx
-
-.. note::
-    Quantization support requires ONNX Runtime >= 1.4.0
-
-.. note::
-    When exporting quantized model you will end up with two different ONNX files. The one specified at the end of the
-    above command will contain the original ONNX model storing `float32` weights.
-    The second one, with ``-quantized`` suffix, will hold the quantized parameters.
-
-
-TorchScript
-=======================================
-
-.. note::
-    This is the very beginning of our experiments with TorchScript and we are still exploring its capabilities
-    with variable-input-size models. It is a focus of interest to us and we will deepen our analysis in upcoming
-    releases, with more code examples, a more flexible implementation, and benchmarks comparing python-based codes
-    with compiled TorchScript.
-
-
-According to Pytorch's documentation: "TorchScript is a way to create serializable and optimizable models from PyTorch code".
-Pytorch's two modules `JIT and TRACE <https://pytorch.org/docs/stable/jit.html>`_ allow the developer to export
-their model to be re-used in other programs, such as efficiency-oriented C++ programs.
-
-We have provided an interface that allows the export of 🤗 Transformers models to TorchScript so that they can
-be reused in a different environment than a Pytorch-based python program. Here we explain how to use our models so that
-they can be exported, and what to be mindful of when using these models with TorchScript.
-
-Exporting a model needs two things:
-
-* dummy inputs to execute a model forward pass.
-* the model needs to be instantiated with the ``torchscript`` flag.
-
-These necessities imply several things developers should be careful about. These are detailed below.
-
-
-Implications
------------------------------------------------
-
-TorchScript flag and tied weights
------------------------------------------------
-This flag is necessary because most of the language models in this repository have tied weights between their
-``Embedding`` layer and their ``Decoding`` layer. TorchScript does not allow the export of models that have tied weights,
-it is therefore necessary to untie the weights beforehand.
-
-This implies that models instantiated with the ``torchscript`` flag have their ``Embedding`` layer and ``Decoding`` layer
-separate, which means that they should not be trained down the line. Training would de-synchronize the two layers,
-leading to unexpected results.
-
-This is not the case for models that do not have a Language Model head, as those do not have tied weights. These models
-can be safely exported without the ``torchscript`` flag.
-
-Dummy inputs and standard lengths
------------------------------------------------
-
-The dummy inputs are used to do a model forward pass. While the inputs' values are propagating through the layers,
-Pytorch keeps track of the different operations executed on each tensor. These recorded operations are then used
-to create the "trace" of the model.
-
-The trace is created relatively to the inputs' dimensions. It is therefore constrained by the dimensions of the dummy
-input, and will not work for any other sequence length or batch size. When trying with a different size, an error such
-as:
-
-``The expanded size of the tensor (3) must match the existing size (7) at non-singleton dimension 2``
-
-will be raised. It is therefore recommended to trace the model with a dummy input size at least as large as the largest
-input that will be fed to the model during inference. Padding can be performed to fill the missing values. As the model
-will have been traced with a large input size however, the dimensions of the different matrix will be large as well,
-resulting in more calculations.
-
-It is recommended to be careful of the total number of operations done on each input and to follow performance closely
-when exporting varying sequence-length models.
-
-Using TorchScript in Python
-------------------------------------------------
-
-Below are examples of using the Python to save, load models as well as how to use the trace for inference.
-
-Saving a model
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-This snippet shows how to use TorchScript to export a ``BertModel``. Here the ``BertModel`` is instantiated
-according to a ``BertConfig`` class and then saved to disk under the filename ``traced_bert.pt``
-
-.. code-block:: python
-
-    from transformers import BertModel, BertTokenizer, BertConfig
-    import torch
-
-    enc = BertTokenizer.from_pretrained("bert-base-uncased")
-
-    # Tokenizing input text
-    text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
-    tokenized_text = enc.tokenize(text)
-
-    # Masking one of the input tokens
-    masked_index = 8
-    tokenized_text[masked_index] = '[MASK]'
-    indexed_tokens = enc.convert_tokens_to_ids(tokenized_text)
-    segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]
-
-    # Creating a dummy input
-    tokens_tensor = torch.tensor([indexed_tokens])
-    segments_tensors = torch.tensor([segments_ids])
-    dummy_input = [tokens_tensor, segments_tensors]
-
-    # Initializing the model with the torchscript flag
-    # Flag set to True even though it is not necessary as this model does not have an LM Head.
-    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
-        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, torchscript=True)
-
-    # Instantiating the model
-    model = BertModel(config)
-
-    # The model needs to be in evaluation mode
-    model.eval()
-
-    # If you are instantiating the model with `from_pretrained` you can also easily set the TorchScript flag
-    model = BertModel.from_pretrained("bert-base-uncased", torchscript=True)
-
-    # Creating the trace
-    traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors])
-    torch.jit.save(traced_model, "traced_bert.pt")
-
-Loading a model
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-This snippet shows how to load the ``BertModel`` that was previously saved to disk under the name ``traced_bert.pt``.
-We are re-using the previously initialised ``dummy_input``.
-
-.. code-block:: python
-
-    loaded_model = torch.jit.load("traced_model.pt")
-    loaded_model.eval()
-
-    all_encoder_layers, pooled_output = loaded_model(dummy_input)
-
-Using a traced model for inference
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Using the traced model for inference is as simple as using its ``__call__`` dunder method:
-
-.. code-block:: python
-
-    traced_model(tokens_tensor, segments_tensors)
@@ -15,17 +15,18 @@ checkpoints are usually pre-trained on a large corpus of data and fine-tuned on
 following:

 - Not all models were fine-tuned on all tasks. If you want to fine-tune a model on a specific task, you can leverage
-  one of the `run_$TASK.py` scripts in the
-  `examples <https://github.com/huggingface/transformers/tree/master/examples>`__ directory.
+  one of the `run_$TASK.py` script in the
+  `examples <https://github.com/huggingface/transformers/tree/master/examples>`_ directory.
 - Fine-tuned models were fine-tuned on a specific dataset. This dataset may or may not overlap with your use-case
  and domain. As mentioned previously, you may leverage the
-  `examples <https://github.com/huggingface/transformers/tree/master/examples>`__ scripts to fine-tune your model, or you
+  `examples <https://github.com/huggingface/transformers/tree/master/examples>`_ scripts to fine-tune your model, or you
  may create your own training script.

 In order to do an inference on a task, several mechanisms are made available by the library:

 - Pipelines: very easy-to-use abstractions, which require as little as two lines of code.
- Direct model use: Less abstractions, but more flexibility and power via a direct access to a tokenizer (PyTorch/TensorFlow) and full inference capacity.
+- Using a model directly with a tokenizer (PyTorch/TensorFlow): the full inference using the model. Less abstraction,
+  but much more powerful.

 Both approaches are showcased here.

@@ -43,11 +44,10 @@ Sequence Classification
 Sequence classification is the task of classifying sequences according to a given number of classes. An example
 of sequence classification is the GLUE dataset, which is entirely based on that task. If you would like to fine-tune
 a model on a GLUE sequence classification task, you may leverage the
-`run_glue.py <https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_glue.py>`__ and
-`run_pl_glue.py <https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_pl_glue.py>`__ or
-`run_tf_glue.py <https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_tf_glue.py>`__ scripts.
+`run_glue.py <https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_glue.py>`_ or
+`run_tf_glue.py <https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_tf_glue.py>`_ scripts.

-Here is an example of using pipelines to do sentiment analysis: identifying if a sequence is positive or negative.
+Here is an example using the pipelines do to sentiment analysis: identifying if a sequence is positive or negative.
 It leverages a fine-tuned model on sst2, which is a GLUE task.

 This returns a label ("POSITIVE" or "NEGATIVE") alongside a score, as follows:
@@ -70,17 +70,15 @@ This returns a label ("POSITIVE" or "NEGATIVE") alongside a score, as follows:
 Here is an example of doing a sequence classification using a model to determine if two sequences are paraphrases
 of each other. The process is the following:

-1. Instantiate a tokenizer and a model from the checkpoint name. The model is
-   identified as a BERT model and loads it with the weights stored in the
-   checkpoint.
-2. Build a sequence from the two sentences, with the correct model-specific
-   separators token type ids and attention masks
-   (:func:`~transformers.PreTrainedTokenizer.encode` and
-   :func:`~transformers.PreTrainedTokenizer.__call__` take care of this).
-3. Pass this sequence through the model so that it is classified in one of the
-   two available classes: 0 (not a paraphrase) and 1 (is a paraphrase).
-4. Compute the softmax of the result to get probabilities over the classes.
-5. Print the results.
+- Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
+  with the weights stored in the checkpoint.
+- Build a sequence from the two sentences, with the correct model-specific separators token type ids
+  and attention masks (:func:`~transformers.PreTrainedTokenizer.encode` and
+  :func:`~transformers.PreTrainedTokenizer.__call__` take care of this)
+- Pass this sequence through the model so that it is classified in one of the two available classes: 0
+  (not a paraphrase) and 1 (is a paraphrase)
+- Compute the softmax of the result to get probabilities over the classes
+- Print the results

 .. code-block::

@@ -100,8 +98,8 @@ of each other. The process is the following:
    >>> paraphrase = tokenizer(sequence_0, sequence_2, return_tensors="pt")
    >>> not_paraphrase = tokenizer(sequence_0, sequence_1, return_tensors="pt")

-    >>> paraphrase_classification_logits = model(**paraphrase).logits
-    >>> not_paraphrase_classification_logits = model(**not_paraphrase).logits
+    >>> paraphrase_classification_logits = model(**paraphrase)[0]
+    >>> not_paraphrase_classification_logits = model(**not_paraphrase)[0]

    >>> paraphrase_results = torch.softmax(paraphrase_classification_logits, dim=1).tolist()[0]
    >>> not_paraphrase_results = torch.softmax(not_paraphrase_classification_logits, dim=1).tolist()[0]
@@ -156,12 +154,9 @@ Extractive Question Answering

 Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
 question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
-a model on a SQuAD task, you may leverage the
-`run_squad.py <https://github.com/huggingface/transformers/tree/master/examples/question-answering/run_squad.py>`__ and
-`run_tf_squad.py <https://github.com/huggingface/transformers/tree/master/examples/question-answering/run_tf_squad.py>`__ scripts.
+a model on a SQuAD task, you may leverage the `run_squad.py`.

-
-Here is an example of using pipelines to do question answering: extracting an answer from a text given a question.
+Here is an example using the pipelines do to question answering: extracting an answer from a text given a question.
 It leverages a fine-tuned model on SQuAD.

 .. code-block::
@@ -176,7 +171,7 @@ It leverages a fine-tuned model on SQuAD.
    ... a model on a SQuAD task, you may leverage the examples/question-answering/run_squad.py script.
    ... """

-This returns an answer extracted from the text, a confidence score, alongside "start" and "end" values, which
+This returns an answer extracted from the text, a confidence score, alongside "start" and "end" values which
 are the positions of the extracted answer in the text.

 .. code-block::
@@ -192,19 +187,16 @@ are the positions of the extracted answer in the text.

 Here is an example of question answering using a model and a tokenizer. The process is the following:

-1. Instantiate a tokenizer and a model from the checkpoint name. The model is
-   identified as a BERT model and loads it with the weights stored in the
-   checkpoint.
-2. Define a text and a few questions.
-3. Iterate over the questions and build a sequence from the text and the current
-   question, with the correct model-specific separators token type ids and
-   attention masks.
-4. Pass this sequence through the model. This outputs a range of scores across
-   the entire sequence tokens (question and text), for both the start and end
-   positions.
-5. Compute the softmax of the result to get probabilities over the tokens.
-6. Fetch the tokens from the identified start and stop values, convert those tokens to a string.
-7. Print the results.
+- Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
+  with the weights stored in the checkpoint.
+- Define a text and a few questions.
+- Iterate over the questions and build a sequence from the text and the current question, with the correct
+  model-specific separators token type ids and attention masks
+- Pass this sequence through the model. This outputs a range of scores across the entire sequence tokens (question and
+  text), for both the start and end positions.
+- Compute the softmax of the result to get probabilities over the tokens
+- Fetch the tokens from the identified start and stop values, convert those tokens to a string.
+- Print the results

 .. code-block::

@@ -299,8 +291,8 @@ Here is an example of question answering using a model and a tokenizer. The proc
 Language Modeling
 ----------------------------------------------------

-Language modeling is the task of fitting a model to a corpus, which can be domain specific. All popular transformer-based
-models are trained using a variant of language modeling, e.g. BERT with masked language modeling, GPT-2 with
+Language modeling is the task of fitting a model to a corpus, which can be domain specific. All popular transformer
+based models are trained using a variant of language modeling, e.g. BERT with masked language modeling, GPT-2 with
 causal language modeling.

 Language modeling can be useful outside of pre-training as well, for example to shift the model distribution to be
@@ -313,7 +305,7 @@ Masked Language Modeling
 Masked language modeling is the task of masking tokens in a sequence with a masking token, and prompting the model to
 fill that mask with an appropriate token. This allows the model to attend to both the right context (tokens on the
 right of the mask) and the left context (tokens on the left of the mask). Such a training creates a strong basis
-for downstream tasks, requiring bi-directional context such as SQuAD (question answering,
+for downstream tasks requiring bi-directional context such as SQuAD (question answering,
 see `Lewis, Lui, Goyal et al. <https://arxiv.org/abs/1910.13461>`__, part 4.2).

 Here is an example of using pipelines to replace a mask from a sequence:
@@ -324,7 +316,7 @@ Here is an example of using pipelines to replace a mask from a sequence:

    >>> nlp = pipeline("fill-mask")

-This outputs the sequences with the mask filled, the confidence score, and the token id in the tokenizer
+This outputs the sequences with the mask filled, the confidence score as well as the token id in the tokenizer
 vocabulary:

 .. code-block::
@@ -357,19 +349,17 @@ vocabulary:
      'token': 17715,
      'token_str': 'Ġprototype'}]

-Here is an example of doing masked language modeling using a model and a tokenizer. The process is the following:
+Here is an example doing masked language modeling using a model and a tokenizer. The process is the following:

-1. Instantiate a tokenizer and a model from the checkpoint name. The model is
-   identified as a DistilBERT model and loads it with the weights stored in the
-   checkpoint.
-2. Define a sequence with a masked token, placing the :obj:`tokenizer.mask_token` instead of a word.
-3. Encode that sequence into a list of IDs and find the position of the masked token in that list.
-4. Retrieve the predictions at the index of the mask token: this tensor has the
-   same size as the vocabulary, and the values are the scores attributed to each
-   token. The model gives higher score to tokens it deems probable in that
-   context.
-5. Retrieve the top 5 tokens using the PyTorch :obj:`topk` or TensorFlow :obj:`top_k` methods.
-6. Replace the mask token by the tokens and print the results
+- Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a DistilBERT model and
+  loads it with the weights stored in the checkpoint.
+- Define a sequence with a masked token, placing the :obj:`tokenizer.mask_token` instead of a word.
+- Encode that sequence into IDs and find the position of the masked token in that list of IDs.
+- Retrieve the predictions at the index of the mask token: this tensor has the same size as the vocabulary, and the
+  values are the scores attributed to each token. The model gives higher score to tokens he deems probable in that
+  context.
+- Retrieve the top 5 tokens using the PyTorch :obj:`topk` or TensorFlow :obj:`top_k` methods.
+- Replace the mask token by the tokens and print the results

 .. code-block::

@@ -385,7 +375,7 @@ Here is an example of doing masked language modeling using a model and a tokeniz
    >>> input = tokenizer.encode(sequence, return_tensors="pt")
    >>> mask_token_index = torch.where(input == tokenizer.mask_token_id)[1]

-    >>> token_logits = model(input).logits
+    >>> token_logits = model(input)[0]
    >>> mask_token_logits = token_logits[0, mask_token_index, :]

    >>> top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()
@@ -429,7 +419,7 @@ for generation tasks.

 Usually, the next token is predicted by sampling from the logits of the last hidden state the model produces from the input sequence.

-Here is an example of using the tokenizer and model and leveraging the :func:`~transformers.PreTrainedModel.top_k_top_p_filtering` method to sample the next token following an input sequence of tokens.
+Here is an example using the tokenizer and model and leveraging the :func:`~transformers.PreTrainedModel.top_k_top_p_filtering` method to sample the next token following an input sequence of tokens.

 .. code-block::

@@ -446,7 +436,7 @@ Here is an example of using the tokenizer and model and leveraging the :func:`~t
    >>> input_ids = tokenizer.encode(sequence, return_tensors="pt")

    >>> # get logits of last hidden state
-    >>> next_token_logits = model(input_ids).logits[:, -1, :]
+    >>> next_token_logits = model(input_ids)[0][:, -1, :]

    >>> # filter
    >>> filtered_next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=50, top_p=1.0)
@@ -483,11 +473,11 @@ Here is an example of using the tokenizer and model and leveraging the :func:`~t
    >>> resulting_string = tokenizer.decode(generated.numpy().tolist()[0])


-This outputs a (hopefully) coherent next token following the original sequence, which in our case is the word *has*:
+This outputs a (hopefully) coherent next token following the original sequence, which is in our case is the word *has*:

 .. code-block::

-    >>> print(resulting_string)
+    print(resulting_string)
    Hugging Face is based in DUMBO, New York City, and has

 In the next section, we show how this functionality is leveraged in :func:`~transformers.PreTrainedModel.generate` to generate multiple tokens up to a user-defined length.
@@ -495,7 +485,7 @@ In the next section, we show how this functionality is leveraged in :func:`~tran
 Text Generation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-In text generation (*a.k.a* *open-ended text generation*) the goal is to create a coherent portion of text that is a continuation from the given context. The following example shows how *GPT-2* can be used in pipelines to generate text. As a default all models apply *Top-K* sampling when used in pipelines, as configured in their respective configurations (see `gpt-2 config <https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json>`__ for example).
+In text generation (*a.k.a* *open-ended text generation*) the goal is to create a coherent portion of text that is a continuation from the given context. As an example, is it shown how *GPT-2* can be used in pipelines to generate text. As a default all models apply *Top-K* sampling when used in pipelines as configured in their respective configurations (see `gpt-2 config <https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json>`_ for example).

 .. code-block::

@@ -507,10 +497,10 @@ In text generation (*a.k.a* *open-ended text generation*) the goal is to create



-Here, the model generates a random text with a total maximal length of *50* tokens from context *"As far as I am concerned, I will"*.
-The default arguments of ``PreTrainedModel.generate()`` can be directly overriden in the pipeline, as is shown above for the argument ``max_length``.
+Here the model generates a random text with a total maximal length of *50* tokens from context *"As far as I am concerned, I will"*.
+The default arguments of ``PreTrainedModel.generate()`` can directly be overriden in the pipeline as is shown above for the argument ``max_length``.

-Here is an example of text generation using ``XLNet`` and its tokenzier.
+Here is an example for text generation using XLNet and its tokenzier. 

 .. code-block::

@@ -566,27 +556,24 @@ Here is an example of text generation using ``XLNet`` and its tokenzier.

 .. code-block::

-    >>> print(generated)
-    Today the weather is really nice and I am planning on anning on taking a nice...... of a great time!<eop>...............
+    print(generated)

-Text generation is currently possible with *GPT-2*, *OpenAi-GPT*, *CTRL*, *XLNet*, *Transfo-XL* and *Reformer* in PyTorch and for most models in Tensorflow as well. As can be seen in the example above *XLNet* and *Transfo-XL* often need to be padded to work well.
-GPT-2 is usually a good choice for *open-ended text generation* because it was trained on millions of webpages with a causal language modeling objective.
+Text generation is currently possible with *GPT-2*, *OpenAi-GPT*, *CTRL*, *XLNet*, *Transfo-XL* and *Reformer* in PyTorch and for most models in Tensorflow as well. As can be seen in the example above *XLNet* and *Transfo-xl* often need to be padded to work well.
+GPT-2 is usually a good choice for *open-ended text generation* because it was trained on millions on webpages with a causal language modeling objective.

-For more information on how to apply different decoding strategies for text generation, please also refer to our text generation blog post `here <https://huggingface.co/blog/how-to-generate>`__.
+For more information on how to apply different decoding strategies for text generation, please also refer to our generation blog post `here <https://huggingface.co/blog/how-to-generate>`_.


 Named Entity Recognition
 ----------------------------------------------------

-Named Entity Recognition (NER) is the task of classifying tokens according to a class, for example, identifying a
+Named Entity Recognition (NER) is the task of classifying tokens according to a class, for example identifying a
 token as a person, an organisation or a location.
 An example of a named entity recognition dataset is the CoNLL-2003 dataset, which is entirely based on that task.
-If you would like to fine-tune a model on an NER task, you may leverage the
-`run_ner.py <https://github.com/huggingface/transformers/tree/master/examples/token-classification/run_ner.py>`__ (PyTorch),
-`run_pl_ner.py <https://github.com/huggingface/transformers/tree/master/examples/token-classification/run_pl_ner.py>`__ (leveraging pytorch-lightning) or the
-`run_tf_ner.py <https://github.com/huggingface/transformers/tree/master/examples/token-classification/run_tf_ner.py>`__ (TensorFlow) scripts.
+If you would like to fine-tune a model on an NER task, you may leverage the `ner/run_ner.py` (PyTorch),
+`ner/run_pl_ner.py` (leveraging pytorch-lightning) or the `ner/run_tf_ner.py` (TensorFlow) scripts.

-Here is an example of using pipelines to do named entity recognition, specifically, trying to identify tokens as belonging to one
+Here is an example using the pipelines do to named entity recognition, trying to identify tokens as belonging to one
 of 9 classes:

 - O, Outside of a named entity
@@ -612,12 +599,13 @@ It leverages a fine-tuned model on CoNLL-2003, fine-tuned by `@stefan-it <https:
    ...            "close to the Manhattan Bridge which is visible from the window."


-This outputs a list of all words that have been identified as one of the entities from the 9 classes defined above. Here are the
+This outputs a list of all words that have been identified as an entity from the 9 classes defined above. Here is the
 expected results:

 .. code-block::

-    >>> print(nlp(sequence))
+    print(nlp(sequence))
+
    [
        {'word': 'Hu', 'score': 0.9995632767677307, 'entity': 'I-ORG'},
        {'word': '##gging', 'score': 0.9915938973426819, 'entity': 'I-ORG'},
@@ -633,25 +621,22 @@ expected results:
        {'word': 'Bridge', 'score': 0.990249514579773, 'entity': 'I-LOC'}
    ]

-Note, how the tokens of the sequence "Hugging Face" have been identified as an organisation, and "New York City", "DUMBO" and
+Note how the words "Hugging Face" have been identified as an organisation, and "New York City", "DUMBO" and
 "Manhattan Bridge" have been identified as locations.

-Here is an example of doing named entity recognition, using a model and a tokenizer. The process is the following:
+Here is an example doing named entity recognition using a model and a tokenizer. The process is the following:

-1. Instantiate a tokenizer and a model from the checkpoint name. The model is
-   identified as a BERT model and loads it with the weights stored in the
-   checkpoint.
-2. Define the label list with which the model was trained on.
-3. Define a sequence with known entities, such as "Hugging Face" as an organisation and "New York City" as a location.
-4. Split words into tokens so that they can be mapped to predictions. We use a
-   small hack by, first, completely encoding and decoding the sequence, so that
-   we're left with a string that contains the special tokens.
-5. Encode that sequence into IDs (special tokens are added automatically).
-6. Retrieve the predictions by passing the input to the model and getting the
-   first output. This results in a distribution over the 9 possible classes for
-   each token. We take the argmax to retrieve the most likely class for each
-   token.
-7. Zip together each token with its prediction and print it.
+- Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and
+  loads it with the weights stored in the checkpoint.
+- Define the label list with which the model was trained on.
+- Define a sequence with known entities, such as "Hugging Face" as an organisation and "New York City" as a location.
+- Split words into tokens so that they can be mapped to the predictions. We use a small hack by firstly completely
+  encoding and decoding the sequence, so that we're left with a string that contains the special tokens.
+- Encode that sequence into IDs (special tokens are added automatically).
+- Retrieve the predictions by passing the input to the model and getting the first output. This results in a
+  distribution over the 9 possible classes for each token. We take the argmax to retrieve the most likely class
+  for each token.
+- Zip together each token with its prediction and print it.

 .. code-block::

@@ -681,7 +666,7 @@ Here is an example of doing named entity recognition, using a model and a tokeni
    >>> tokens = tokenizer.tokenize(tokenizer.decode(tokenizer.encode(sequence)))
    >>> inputs = tokenizer.encode(sequence, return_tensors="pt")

-    >>> outputs = model(inputs).logits
+    >>> outputs = model(inputs)[0]
    >>> predictions = torch.argmax(outputs, dim=2)
    >>> ## TENSORFLOW CODE
    >>> from transformers import TFAutoModelForTokenClassification, AutoTokenizer
@@ -713,8 +698,8 @@ Here is an example of doing named entity recognition, using a model and a tokeni
    >>> predictions = tf.argmax(outputs, axis=2)


-This outputs a list of each token mapped to its corresponding prediction. Differently from the pipeline, here every token has
-a prediction as we didn't remove the "0"th class, which means that no particular entity was found on that token. The
+This outputs a list of each token mapped to their prediction. Differently from the pipeline, here every token has
+a prediction as we didn't remove the "0" class which means that no particular entity was found on that token. The
 following array should be the output:

 .. code-block::
@@ -725,13 +710,13 @@ following array should be the output:
 Summarization
 ----------------------------------------------------

-Summarization is the task of summarizing a document or an article into a shorter text.
+Summarization is the task of summarizing a text / an article into a shorter text.

 An example of a summarization dataset is the CNN / Daily Mail dataset, which consists of long news articles and was created for the task of summarization.
-If you would like to fine-tune a model on a summarization task, various approaches are described in this
-`document <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/README.md>`__.
+If you would like to fine-tune a model on a summarization task, you may leverage the ``examples/summarization/bart/run_train.sh`` (leveraging pytorch-lightning) script.

-Here is an example of using the pipelines to do summarization. It leverages a Bart model that was fine-tuned on the CNN / Daily Mail data set.
+Here is an example using the pipelines do to summarization. 
+It leverages a Bart model that was fine-tuned on the CNN / Daily Mail data set.

 .. code-block::

@@ -758,8 +743,8 @@ 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
-of ``PretrainedModel.generate()`` directly in the pipeline for ``max_length`` and ``min_length`` as shown below.
+Because the summarization 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`` and ``min_length`` above.
 This outputs the following summary:

 .. code-block::
@@ -767,15 +752,14 @@ This outputs the following summary:
    >>> print(summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False))
    [{'summary_text': 'Liana Barrientos, 39, is charged with two counts of "offering a false instrument for filing in the first degree" In total, she has been married 10 times, with nine of her marriages occurring between 1999 and 2002. She is believed to still be married to four men.'}]

-Here is an example of doing summarization using a model and a tokenizer. The process is the following:
+Here is an example doing summarization 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 summarized.
-3. Add the T5 specific prefix "summarize: ".
-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.
+- Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder model, such as ``Bart`` or ``T5``.
+- Define the article that should be summarizaed.
+- Leverage the ``PretrainedModel.generate()`` method.
+- Add the T5 specific prefix "summarize: ".

+Here Google`s T5 model is used that was only pre-trained on a multi-task mixed data set (including CNN / Daily Mail), but nevertheless yields very good results.
 .. code-block::

    >>> ## PYTORCH CODE
@@ -802,14 +786,12 @@ Translation

 Translation is the task of translating a text from one language to another.

-An example of a translation dataset is the WMT English to German dataset, which has sentences in English as the input data
-and the corresponding sentences in German as the target data.
-If you would like to fine-tune a model on a translation task, various approaches are described in this
-`document <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/README.md>`__.
+An example of a translation dataset is the WMT English to German dataset, which has English sentences as the input data 
+and German sentences as the target data.

-Here is an example of using the pipelines to do translation.
-It leverages a T5 model that was only pre-trained on a multi-task mixture dataset (including WMT), yet, yielding impressive
-translation results.
+Here is an example using the pipelines do to translation. 
+It leverages a T5 model that was only pre-trained on a multi-task mixture dataset (including WMT), but yields impressive 
+translation results nevertheless.

 .. code-block::

@@ -819,15 +801,20 @@ 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.
+This outputs the following translation into German:

-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.
+  Hugging Face ist ein Technologieunternehmen mit Sitz in New York und Paris.
+  
+Here is an example doing translation using a model and a tokenizer. The process is the following:
+
+- Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder model, such as ``Bart`` or ``T5``.
+- Define the article that should be summarizaed.
+- Leverage the ``PretrainedModel.generate()`` method.
+- Add the T5 specific prefix "translate English to German: "

 .. code-block::

@@ -839,6 +826,10 @@ Here is an example of doing translation using a model and a tokenizer. The proce

    >>> inputs = tokenizer.encode("translate English to German: Hugging Face is a technology company based in New York and Paris", return_tensors="pt")
    >>> outputs = model.generate(inputs, max_length=40, num_beams=4, early_stopping=True)
+
+    >>> print(outputs)
+    tensor([[    0, 11560,  3896,  8881,   229,   236,     3, 14366, 15377,   181,
+             11216,    16,   368,  1060,    64,  1919,     5]])
    >>> ## TENSORFLOW CODE
    >>> from transformers import TFAutoModelWithLMHead, AutoTokenizer

@@ -848,9 +839,7 @@ Here is an example of doing translation using a model and a tokenizer. The proce
    >>> inputs = tokenizer.encode("translate English to German: Hugging Face is a technology company based in New York and Paris", return_tensors="tf")
    >>> outputs = model.generate(inputs, max_length=40, num_beams=4, early_stopping=True)

-As with the pipeline example, we get the same translation:
-
-.. code-block::
-
-    >>> print(tokenizer.decode(outputs[0]))
-    Hugging Face ist ein Technologieunternehmen mit Sitz in New York und Paris.
+    >>> print(outputs)
+    tf.Tensor(
+    [[    0 11560  3896  8881   229   236     3 14366 15377   181 11216    16
+        368  1060    64  1919     5]], shape=(1, 17), dtype=int32)
@@ -73,7 +73,7 @@ subwords. This also enables the model to process words it has never seen before,
 subwords it knows. For instance, the base :class:`~transformers.BertTokenizer` will tokenize "I have a new GPU!" like
 this:

-.. code-block::
+::

    >>> from transformers import BertTokenizer
    >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
@@ -87,7 +87,7 @@ predictions and reverse the tokenization).

 Another example is when we use the base :class:`~transformers.XLNetTokenizer` to tokenize our previous text:

-.. code-block::
+::

    >>> from transformers import XLNetTokenizer
    >>> tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
@@ -130,7 +130,7 @@ Then the base vocabulary is ['b', 'g', 'h', 'n', 'p', 's', 'u'] and all our word

 We then take each pair of symbols and look at the most frequent. For instance 'hu' is present `10 + 5 = 15` times (10
 times in the 10 occurrences of 'hug', 5 times in the 5 occurrences of 'hugs'). The most frequent here is 'ug', present
-`10 + 5 + 5 = 20` times in total. So the first merge rule the tokenizer learns is to group all 'u' and 'g' together
+`10 + 5 + 2 + 5 = 22` times in total. So the first merge rule the tokenizer learns is to group all 'u' and 'g' together
 then it adds 'ug' to the vocabulary. Our corpus then becomes

 ::
@@ -0,0 +1,135 @@
+TorchScript
+================================================
+
+.. note::
+    This is the very beginning of our experiments with TorchScript and we are still exploring its capabilities
+    with variable-input-size models. It is a focus of interest to us and we will deepen our analysis in upcoming
+    releases, with more code examples, a more flexible implementation, and benchmarks comparing python-based codes
+    with compiled TorchScript.
+
+
+According to Pytorch's documentation: "TorchScript is a way to create serializable and optimizable models from PyTorch code".
+Pytorch's two modules `JIT and TRACE <https://pytorch.org/docs/stable/jit.html>`_ allow the developer to export
+their model to be re-used in other programs, such as efficiency-oriented C++ programs.
+
+We have provided an interface that allows the export of 🤗 Transformers models to TorchScript so that they can
+be reused in a different environment than a Pytorch-based python program. Here we explain how to use our models so that
+they can be exported, and what to be mindful of when using these models with TorchScript.
+
+Exporting a model needs two things:
+
+* dummy inputs to execute a model forward pass.
+* the model needs to be instantiated with the ``torchscript`` flag.
+
+These necessities imply several things developers should be careful about. These are detailed below.
+
+
+Implications
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+TorchScript flag and tied weights
+------------------------------------------------
+This flag is necessary because most of the language models in this repository have tied weights between their
+``Embedding`` layer and their ``Decoding`` layer. TorchScript does not allow the export of models that have tied weights,
+it is therefore necessary to untie the weights beforehand.
+
+This implies that models instantiated with the ``torchscript`` flag have their ``Embedding`` layer and ``Decoding`` layer
+separate, which means that they should not be trained down the line. Training would de-synchronize the two layers,
+leading to unexpected results.
+
+This is not the case for models that do not have a Language Model head, as those do not have tied weights. These models
+can be safely exported without the ``torchscript`` flag.
+
+Dummy inputs and standard lengths
+------------------------------------------------
+
+The dummy inputs are used to do a model forward pass. While the inputs' values are propagating through the layers,
+Pytorch keeps track of the different operations executed on each tensor. These recorded operations are then used
+to create the "trace" of the model.
+
+The trace is created relatively to the inputs' dimensions. It is therefore constrained by the dimensions of the dummy
+input, and will not work for any other sequence length or batch size. When trying with a different size, an error such
+as:
+
+``The expanded size of the tensor (3) must match the existing size (7) at non-singleton dimension 2``
+
+will be raised. It is therefore recommended to trace the model with a dummy input size at least as large as the largest
+input that will be fed to the model during inference. Padding can be performed to fill the missing values. As the model
+will have been traced with a large input size however, the dimensions of the different matrix will be large as well,
+resulting in more calculations.
+
+It is recommended to be careful of the total number of operations done on each input and to follow performance closely
+when exporting varying sequence-length models.
+
+Using TorchScript in Python
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Below are examples of using the Python to save, load models as well as how to use the trace for inference.
+
+Saving a model
+------------------------------------------------
+
+This snippet shows how to use TorchScript to export a ``BertModel``. Here the ``BertModel`` is instantiated
+according to a ``BertConfig`` class and then saved to disk under the filename ``traced_bert.pt``
+
+.. code-block:: python
+
+    from transformers import BertModel, BertTokenizer, BertConfig
+    import torch
+
+    enc = BertTokenizer.from_pretrained("bert-base-uncased")
+
+    # Tokenizing input text
+    text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
+    tokenized_text = enc.tokenize(text)
+
+    # Masking one of the input tokens
+    masked_index = 8
+    tokenized_text[masked_index] = '[MASK]'
+    indexed_tokens = enc.convert_tokens_to_ids(tokenized_text)
+    segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]
+
+    # Creating a dummy input
+    tokens_tensor = torch.tensor([indexed_tokens])
+    segments_tensors = torch.tensor([segments_ids])
+    dummy_input = [tokens_tensor, segments_tensors]
+
+    # Initializing the model with the torchscript flag
+    # Flag set to True even though it is not necessary as this model does not have an LM Head.
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, torchscript=True)
+
+    # Instantiating the model
+    model = BertModel(config)
+
+    # The model needs to be in evaluation mode
+    model.eval()
+
+    # If you are instantiating the model with `from_pretrained` you can also easily set the TorchScript flag
+    model = BertModel.from_pretrained("bert-base-uncased", torchscript=True)
+
+    # Creating the trace
+    traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors])
+    torch.jit.save(traced_model, "traced_bert.pt")
+
+Loading a model
+------------------------------------------------
+
+This snippet shows how to load the ``BertModel`` that was previously saved to disk under the name ``traced_bert.pt``.
+We are re-using the previously initialised ``dummy_input``.
+
+.. code-block:: python
+
+    loaded_model = torch.jit.load("traced_model.pt")
+    loaded_model.eval()
+
+    all_encoder_layers, pooled_output = loaded_model(dummy_input)
+
+Using a traced model for inference
+------------------------------------------------
+
+Using the traced model for inference is as simple as using its ``__call__`` dunder method:
+
+.. code-block:: python
+
+    traced_model(tokens_tensor, segments_tensors)
@@ -16,10 +16,10 @@ TF2, and focus specifically on the nuances and tools for training models in

 Sections:

-  - :ref:`pytorch`
-  - :ref:`tensorflow`
-  - :ref:`trainer`
-  - :ref:`additional-resources`
+  * :ref:`pytorch`
+  * :ref:`tensorflow`
+  * :ref:`trainer`
+  * :ref:`additional-resources`

 .. _pytorch:

@@ -49,7 +49,7 @@ put it in train mode.
 .. code-block:: python

    from transformers import BertForSequenceClassification
-    model = BertForSequenceClassification.from_pretrained('bert-base-uncased', return_dict=True)
+    model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
    model.train()

 This is useful because it allows us to make use of the pre-trained BERT
@@ -99,7 +99,7 @@ backwards pass and update the weights:

    labels = torch.tensor([1,0]).unsqueeze(0)
    outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
-    loss = outputs.loss
+    loss = outputs[0]
    loss.backward()
    optimizer.step()

@@ -111,7 +111,7 @@ The following is equivalent to the previous example:
    from torch.nn import functional as F
    labels = torch.tensor([1,0]).unsqueeze(0)
    outputs = model(input_ids, attention_mask=attention_mask)
-    loss = F.cross_entropy(labels, outputs.logitd)
+    loss = F.cross_entropy(labels, outputs[0])
    loss.backward()
    optimizer.step()

@@ -131,6 +131,7 @@ Then all we have to do is call ``scheduler.step()`` after ``optimizer.step()``.

 .. code-block:: python

+    ...
    loss.backward()
    optimizer.step()
    scheduler.step()
@@ -150,7 +151,7 @@ the encoder parameters, which can be accessed with the ``base_model``
 submodule on any task-specific model in the library:

 .. code-block:: python
-
+   
    for param in model.base_model.parameters():
        param.requires_grad = False

@@ -181,7 +182,6 @@ the pretrained tokenizer name.
 .. code-block:: python

    from transformers import BertTokenizer, glue_convert_examples_to_features
-    import tensorflow as tf
    import tensorflow_datasets as tfds
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    data = tfds.load('glue/mrpc')
@@ -191,7 +191,7 @@ the pretrained tokenizer name.
 The model can then be compiled and trained as any Keras model:

 .. code-block:: python
-
+    
    optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5)
    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    model.compile(optimizer=optimizer, loss=loss)
@@ -282,7 +282,7 @@ your own ``compute_metrics`` function and pass it to the trainer.

 .. code-block:: python

-    from sklearn.metrics import accuracy_score, precision_recall_fscore_support
+    from sklearn.metrics import precision_recall_fscore_support

    def compute_metrics(pred):
        labels = pred.label_ids
@@ -305,14 +305,19 @@ launching tensorboard in your specified ``logging_dir`` directory.
 Additional resources
 ^^^^^^^^^^^^^^^^^^^^

- `A lightweight colab demo <https://colab.research.google.com/drive/1-JIJlao4dI-Ilww_NnTc0rxtp-ymgDgM?usp=sharing>`_
-  which uses ``Trainer`` for IMDb sentiment classification.
+    * `A lightweight colab demo
+      <https://colab.research.google.com/drive/1-JIJlao4dI-Ilww_NnTc0rxtp-ymgDgM?usp=sharing>`_
+      which uses ``Trainer`` for IMDb sentiment classification.

- `🤗 Transformers Examples <https://github.com/huggingface/transformers/tree/master/examples>`_
-  including scripts for training and fine-tuning on GLUE, SQuAD, and several other tasks.
+    * `🤗 Transformers Examples <https://github.com/huggingface/transformers/tree/master/examples>`_
+      including scripts for training and fine-tuning on GLUE, SQuAD, and
+      several other tasks.

- `How to train a language model <https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb>`_,
-  a detailed colab notebook which uses ``Trainer`` to train a masked language model from scratch on Esperanto.
+    * `How to train a language model
+      <https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb>`_,
+      a detailed colab notebook which uses ``Trainer`` to train a masked
+      language model from scratch on Esperanto.

- `🤗 Transformers Notebooks <notebooks.html>`_ which contain dozens of example notebooks from the community for
-  training and using 🤗 Transformers on a variety of tasks.
+    * `🤗 Transformers Notebooks <./notebooks.html>`_ which contain dozens
+      of example notebooks from the community for training and using
+      🤗 Transformers on a variety of tasks.
@@ -1,7 +1,7 @@
 # Examples

 Version 2.9 of 🤗 Transformers introduces a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2.
-Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.2+.
+Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.1+.

 Here is the list of all our examples:
 - **grouped by task** (all official examples work for multiple models)
@@ -78,50 +78,3 @@ python examples/xla_spawn.py --num_cores 8 \
 ```

 Feedback and more use cases and benchmarks involving TPUs are welcome, please share with the community.
-
-## Logging & Experiment tracking
-
-You can easily log and monitor your runs code. The following are currently supported:
-
-* [TensorBoard](https://www.tensorflow.org/tensorboard)
-* [Weights & Biases](https://docs.wandb.com/library/integrations/huggingface)
-* [Comet ML](https://www.comet.ml/docs/python-sdk/huggingface/)
-
-### Weights & Biases
-
-To use Weights & Biases, install the wandb package with:
-
-```bash
-pip install wandb
-```
-
-Then log in the command line:
-
-```bash
-wandb login
-```
-
-If you are in Jupyter or Colab, you should login with:
-
-```python
-import wandb
-wandb.login()
-```
-
-Whenever you use `Trainer` or `TFTrainer` classes, your losses, evaluation metrics, model topology and gradients (for `Trainer` only) will automatically be logged.
-
-When using 🤗 Transformers with PyTorch Lightning, runs can be tracked through `WandbLogger`. Refer to related [documentation & examples](https://docs.wandb.com/library/integrations/lightning).
-
-### Comet.ml
-
-To use `comet_ml`, install the Python package with:
-
-```bash
-pip install comet_ml
-```
-
-or if in a Conda environment:
-
-```bash
-conda install -c comet_ml -c anaconda -c conda-forge comet_ml
-```
@@ -255,11 +255,7 @@ class HansProcessor(DataProcessor):
        return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_evaluation_set.txt")), "dev")

    def get_labels(self):
-        """See base class.
-        Note that we follow the standard three labels for MNLI
-        (see :class:`~transformers.data.processors.utils.MnliProcessor`)
-        but the HANS evaluation groups `contradiction` and `neutral` into `non-entailment` (label 0) while
-        `entailment` is label 1."""
+        """See base class."""
        return ["contradiction", "entailment", "neutral"]

    def _create_examples(self, lines, set_type):
@@ -272,7 +268,7 @@ class HansProcessor(DataProcessor):
            text_a = line[5]
            text_b = line[6]
            pairID = line[7][2:] if line[7].startswith("ex") else line[7]
-            label = line[0]
+            label = line[-1]
            examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label, pairID=pairID))
        return examples

@@ -1,10 +1,10 @@
 import argparse
 import logging
 import sys
+import unittest
 from unittest.mock import patch

 import run_glue_with_pabee
-from transformers.testing_utils import TestCasePlus


 logging.basicConfig(level=logging.DEBUG)
@@ -19,31 +19,29 @@ def get_setup_file():
    return args.f


-class PabeeTests(TestCasePlus):
+class PabeeTests(unittest.TestCase):
    def test_run_glue(self):
        stream_handler = logging.StreamHandler(sys.stdout)
        logger.addHandler(stream_handler)

-        tmp_dir = self.get_auto_remove_tmp_dir()
-        testargs = f"""
+        testargs = """
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
-            --output_dir {tmp_dir}
-            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
+            --output_dir ./tests/fixtures/tests_samples/temp_dir
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
+            --overwrite_output_dir
            --seed=42
            --max_seq_length=128
            """.split()
-
        with patch.object(sys, "argv", testargs):
            result = run_glue_with_pabee.main()
            for value in result.values():
@@ -1,11 +0,0 @@
-# tests directory-specific settings - this file is run automatically
-# by pytest before any tests are run
-
-import sys
-from os.path import abspath, dirname, join
-
-
-# allow having multiple repository checkouts and not needing to remember to rerun
-# '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)
@@ -5,7 +5,6 @@ import unittest
 from unittest.mock import patch

 import run_glue_deebert
-from transformers.testing_utils import slow


 logging.basicConfig(level=logging.DEBUG)
@@ -21,13 +20,10 @@ def get_setup_file():


 class DeeBertTests(unittest.TestCase):
-    def setup(self) -> None:
+    def test_glue_deebert(self):
        stream_handler = logging.StreamHandler(sys.stdout)
        logger.addHandler(stream_handler)

-    @slow
-    def test_glue_deebert_train(self):
-
        train_args = """
            run_glue_deebert.py
            --model_type roberta
@@ -50,10 +46,6 @@ class DeeBertTests(unittest.TestCase):
            --overwrite_cache
            --eval_after_first_stage
            """.split()
-        with patch.object(sys, "argv", train_args):
-            result = run_glue_deebert.main()
-            for value in result.values():
-                self.assertGreaterEqual(value, 0.666)

        eval_args = """
            run_glue_deebert.py
@@ -71,10 +63,6 @@ class DeeBertTests(unittest.TestCase):
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
-        with patch.object(sys, "argv", eval_args):
-            result = run_glue_deebert.main()
-            for value in result.values():
-                self.assertGreaterEqual(value, 0.666)

        entropy_eval_args = """
            run_glue_deebert.py
@@ -92,7 +80,18 @@ class DeeBertTests(unittest.TestCase):
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
+
+        with patch.object(sys, "argv", train_args):
+            result = run_glue_deebert.main()
+            for value in result.values():
+                self.assertGreaterEqual(value, 0.75)
+
+        with patch.object(sys, "argv", eval_args):
+            result = run_glue_deebert.main()
+            for value in result.values():
+                self.assertGreaterEqual(value, 0.75)
+
        with patch.object(sys, "argv", entropy_eval_args):
            result = run_glue_deebert.main()
            for value in result.values():
-                self.assertGreaterEqual(value, 0.666)
+                self.assertGreaterEqual(value, 0.75)
@@ -55,7 +55,7 @@ Here are the results on the *test* sets for 6 of the languages available in XNLI

 ## Setup

-This part of the library has only be tested with Python3.6+. There are few specific dependencies to install before launching a distillation, you can install them with the command `pip install -r requirements.txt`.
+This part of the library has only be tested with Python3.6+. There are few specific dependencies to install before launching a distillation, you can install them with the command `pip install -r requirements.txt`. 

 **Important note:** The training scripts have been updated to support PyTorch v1.2.0 (there are breakings changes compared to v1.1.0).

@@ -161,7 +161,7 @@ python -m torch.distributed.launch \
    --master_port $MASTER_PORT \
    train.py \
        --force \
-        --gpus $WORLD_SIZE \
+        --n_gpu $WORLD_SIZE \
        --student_type distilbert \
        --student_config training_configs/distilbert-base-uncased.json \
        --teacher_type bert \
@@ -210,7 +210,7 @@ def main():
        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
        "See details at https://nvidia.github.io/apex/amp.html",
    )
-    parser.add_argument("--gpus", type=int, default=1, help="Number of GPUs in the node.")
+    parser.add_argument("--n_gpu", type=int, default=1, help="Number of GPUs in the node.")
    parser.add_argument("--local_rank", type=int, default=-1, help="Distributed training - Local rank")
    parser.add_argument("--seed", type=int, default=56, help="Random seed")

@@ -60,27 +60,3 @@ python run_language_modeling.py \
    --mlm
 ```

-### XLNet and permutation language modeling
-
-XLNet uses a different training objective, which is permutation language modeling. It is an autoregressive method 
-to learn bidirectional contexts by maximizing the expected likelihood over all permutations of the input 
-sequence factorization order.
-
-We use the `--plm_probability` flag to define the ratio of length of a span of masked tokens to surrounding 
-context length for permutation language modeling.
-
-The `--max_span_length` flag may also be used to limit the length of a span of masked tokens used 
-for permutation language modeling.
-
-```bash
-export TRAIN_FILE=/path/to/dataset/wiki.train.raw
-export TEST_FILE=/path/to/dataset/wiki.test.raw
-
-python run_language_modeling.py \
-    --output_dir=output \
-    --model_name_or_path=xlnet-base-cased \
-    --do_train \
-    --train_data_file=$TRAIN_FILE \
-    --do_eval \
-    --eval_data_file=$TEST_FILE \
-```
@@ -1,11 +1,14 @@
 import argparse
 import logging
 import os
+import random
 from pathlib import Path
 from typing import Any, Dict

+import numpy as np
 import pytorch_lightning as pl
-from pytorch_lightning.utilities import rank_zero_info
+import torch
+from pytorch_lightning.utilities import rank_zero_info, rank_zero_only

 from transformers import (
    AdamW,
@@ -20,12 +23,7 @@ from transformers import (
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
-)
-from transformers.optimization import (
-    get_cosine_schedule_with_warmup,
-    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
-    get_polynomial_decay_schedule_with_warmup,
 )


@@ -44,17 +42,12 @@ MODEL_MODES = {
 }


-# update this and the import above to support new schedulers from transformers.optimization
-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,
-    # '': get_constant_schedule,             # not supported for now
-    # '': get_constant_schedule_with_warmup, # not supported for now
-}
-arg_to_scheduler_choices = sorted(arg_to_scheduler.keys())
-arg_to_scheduler_metavar = "{" + ", ".join(arg_to_scheduler_choices) + "}"
+def set_seed(args: argparse.Namespace):
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    if args.gpus > 0:
+        torch.cuda.manual_seed_all(args.seed)


 class BaseTransformer(pl.LightningModule):
@@ -70,12 +63,9 @@ class BaseTransformer(pl.LightningModule):
    ):
        """Initialize a model, tokenizer and config."""
        super().__init__()
-        # TODO: move to self.save_hyperparameters()
-        # self.save_hyperparameters()
-        # can also expand arguments into trainer signature for easier reading
-
-        self.save_hyperparameters(hparams)
+        self.hparams = hparams  # TODO: move to self.save_hyperparameters()
        self.step_count = 0
+        self.tfmr_ckpts = {}
        self.output_dir = Path(self.hparams.output_dir)
        cache_dir = self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
@@ -87,13 +77,6 @@ class BaseTransformer(pl.LightningModule):
            )
        else:
            self.config: PretrainedConfig = config
-
-        extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
-        for p in extra_model_params:
-            if getattr(self.hparams, p, None):
-                assert hasattr(self.config, p), f"model config doesn't have a `{p}` attribute"
-                setattr(self.config, p, getattr(self.hparams, p))
-
        if tokenizer is None:
            self.tokenizer = AutoTokenizer.from_pretrained(
                self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,
@@ -115,16 +98,8 @@ class BaseTransformer(pl.LightningModule):
    def load_hf_checkpoint(self, *args, **kwargs):
        self.model = self.model_type.from_pretrained(*args, **kwargs)

-    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
-        )
-        scheduler = {"scheduler": scheduler, "interval": "step", "frequency": 1}
-        return scheduler
-
    def configure_optimizers(self):
-        """Prepare optimizer and schedule (linear warmup and decay)"""
+        "Prepare optimizer and schedule (linear warmup and decay)"
        model = self.model
        no_decay = ["bias", "LayerNorm.weight"]
        optimizer_grouped_parameters = [
@@ -139,10 +114,17 @@ class BaseTransformer(pl.LightningModule):
        ]
        optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)
        self.opt = optimizer
+        return [optimizer]

-        scheduler = self.get_lr_scheduler()
-
-        return [optimizer], [scheduler]
+    def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None):
+        if self.trainer.use_tpu:
+            xm.optimizer_step(optimizer)
+        else:
+            optimizer.step()
+        optimizer.zero_grad()
+        self.lr_scheduler.step()  # By default, PL will only step every epoch.
+        lrs = {f"lr_group_{i}": lr for i, lr in enumerate(self.lr_scheduler.get_lr())}
+        self.logger.log_metrics(lrs)

    def test_step(self, batch, batch_nb):
        return self.validation_step(batch, batch_nb)
@@ -150,29 +132,26 @@ 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
-        dataset_size = len(self.train_loader.dataset)
-        return (dataset_size / effective_batch_size) * self.hparams.max_epochs
-
-    def setup(self, mode):
-        if mode == "fit":
-            self.train_loader = self.get_dataloader("train", self.hparams.train_batch_size, shuffle=True)
-
-    def get_dataloader(self, type_path, batch_size, shuffle=False):
-        raise NotImplementedError("You must implement this for your task")
-
    def train_dataloader(self):
-        return self.train_loader
+        train_batch_size = self.hparams.train_batch_size
+        dataloader = self.load_dataset("train", train_batch_size)
+
+        t_total = (
+            (len(dataloader.dataset) // (train_batch_size * max(1, self.hparams.n_gpu)))
+            // self.hparams.gradient_accumulation_steps
+            * float(self.hparams.num_train_epochs)
+        )
+        scheduler = get_linear_schedule_with_warmup(
+            self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=t_total
+        )
+        self.lr_scheduler = scheduler
+        return dataloader

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

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

    def _feature_file(self, mode):
        return os.path.join(
@@ -187,9 +166,11 @@ class BaseTransformer(pl.LightningModule):
    @pl.utilities.rank_zero_only
    def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
        save_path = self.output_dir.joinpath("best_tfmr")
+        save_path.mkdir(exist_ok=True)
        self.model.config.save_step = self.step_count
        self.model.save_pretrained(save_path)
        self.tokenizer.save_pretrained(save_path)
+        self.tfmr_ckpts[self.step_count] = save_path

    @staticmethod
    def add_model_specific_args(parser, root_dir):
@@ -215,46 +196,21 @@ class BaseTransformer(pl.LightningModule):
            type=str,
            help="Where do you want to store the pre-trained models downloaded from s3",
        )
-        parser.add_argument(
-            "--encoder_layerdrop",
-            type=float,
-            help="Encoder layer dropout probability (Optional). Goes into model.config",
-        )
-        parser.add_argument(
-            "--decoder_layerdrop",
-            type=float,
-            help="Decoder layer dropout probability (Optional). Goes into model.config",
-        )
-        parser.add_argument(
-            "--dropout", type=float, help="Dropout probability (Optional). Goes into model.config",
-        )
-        parser.add_argument(
-            "--attention_dropout", type=float, help="Attention dropout probability (Optional). Goes into model.config",
-        )
        parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
-        parser.add_argument(
-            "--lr_scheduler",
-            default="linear",
-            choices=arg_to_scheduler_choices,
-            metavar=arg_to_scheduler_metavar,
-            type=str,
-            help="Learning rate scheduler",
-        )
        parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
        parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
        parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
        parser.add_argument("--num_workers", default=4, type=int, help="kwarg passed to DataLoader")
-        parser.add_argument("--num_train_epochs", dest="max_epochs", default=3, type=int)
+        parser.add_argument(
+            "--num_train_epochs", default=3, type=int, help="Total number of training epochs to perform."
+        )
+
        parser.add_argument("--train_batch_size", default=32, type=int)
        parser.add_argument("--eval_batch_size", default=32, type=int)


 class LoggingCallback(pl.Callback):
-    def on_batch_end(self, trainer, pl_module):
-        lr_scheduler = trainer.lr_schedulers[0]["scheduler"]
-        lrs = {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr())}
-        pl_module.logger.log_metrics(lrs)
-
+    @rank_zero_only
    def on_validation_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
        rank_zero_info("***** Validation results *****")
        metrics = trainer.callback_metrics
@@ -263,15 +219,16 @@ class LoggingCallback(pl.Callback):
            if key not in ["log", "progress_bar"]:
                rank_zero_info("{} = {}\n".format(key, str(metrics[key])))

+    @rank_zero_only
    def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
-        rank_zero_info("***** Test results *****")
+        logger.info("***** Test results *****")
        metrics = trainer.callback_metrics
        # Log and save results to file
        output_test_results_file = os.path.join(pl_module.hparams.output_dir, "test_results.txt")
        with open(output_test_results_file, "w") as writer:
            for key in sorted(metrics):
                if key not in ["log", "progress_bar"]:
-                    rank_zero_info("{} = {}\n".format(key, str(metrics[key])))
+                    logger.info("{} = {}\n".format(key, str(metrics[key])))
                    writer.write("{} = {}\n".format(key, str(metrics[key])))


@@ -284,6 +241,7 @@ def add_generic_args(parser, root_dir) -> None:
        required=True,
        help="The output directory where the model predictions and checkpoints will be written.",
    )
+
    parser.add_argument(
        "--fp16",
        action="store_true",
@@ -293,29 +251,26 @@ def add_generic_args(parser, root_dir) -> None:
    parser.add_argument(
        "--fp16_opt_level",
        type=str,
-        default="O2",
+        default="O1",
        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
        "See details at https://nvidia.github.io/apex/amp.html",
    )
-    parser.add_argument("--n_tpu_cores", dest="tpu_cores", type=int)
-    parser.add_argument("--max_grad_norm", dest="gradient_clip_val", default=1.0, type=float, help="Max gradient norm")
+    parser.add_argument("--fast_dev_run", action="store_true")
+    parser.add_argument("--gpus", type=int, default=1)
+    parser.add_argument("--n_tpu_cores", type=int, default=0)
+    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
    parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
    parser.add_argument("--do_predict", action="store_true", help="Whether to run predictions on the test set.")
    parser.add_argument(
        "--gradient_accumulation_steps",
-        dest="accumulate_grad_batches",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
+
    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
-    parser.add_argument(
-        "--data_dir",
-        default=None,
-        type=str,
-        required=True,
-        help="The input data dir. Should contain the training files for the CoNLL-2003 NER task.",
-    )
+    parser.add_argument("--resume_from_checkpoint", type=str, default=None)
+    parser.add_argument("--val_check_interval", default=1.0, type=float)


 def generic_train(
@@ -328,13 +283,10 @@ def generic_train(
    logging_callback=None,
    **extra_train_kwargs
 ):
-    pl.seed_everything(args.seed)
-
    # init model
+    set_seed(args)
    odir = Path(model.hparams.output_dir)
    odir.mkdir(exist_ok=True)
-
-    # add custom checkpoints
    if checkpoint_callback is None:
        checkpoint_callback = pl.callbacks.ModelCheckpoint(
            filepath=args.output_dir, prefix="checkpoint", monitor="val_loss", mode="min", save_top_k=1
@@ -344,25 +296,38 @@ def generic_train(

    train_params = {}

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

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

-    trainer = pl.Trainer.from_argparse_args(
-        args,
-        weights_summary=None,
-        callbacks=[logging_callback] + extra_callbacks,
+    trainer = pl.Trainer(
        logger=logger,
-        checkpoint_callback=checkpoint_callback,
+        accumulate_grad_batches=args.gradient_accumulation_steps,
+        gpus=args.gpus,
+        max_epochs=args.num_train_epochs,
        early_stop_callback=early_stopping_callback,
+        gradient_clip_val=args.max_grad_norm,
+        checkpoint_callback=checkpoint_callback,
+        callbacks=[logging_callback] + extra_callbacks,
+        fast_dev_run=args.fast_dev_run,
+        val_check_interval=args.val_check_interval,
+        weights_summary=None,
+        resume_from_checkpoint=args.resume_from_checkpoint,
        **train_params,
    )

    if args.do_train:
        trainer.fit(model)
-
+    trainer.logger.log_hyperparams(args)
+    trainer.logger.save()
    return trainer
@@ -204,8 +204,6 @@ if is_tf_available():
            )

        def get_dataset(self):
-            self.dataset = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))
-
            return self.dataset

        def __len__(self):
@@ -313,6 +313,7 @@ def evaluate(args, model, tokenizer, prefix=""):
                    inputs.update(
                        {"langs": (torch.ones(batch[0].shape, dtype=torch.int64) * args.lang_id).to(args.device)}
                    )
+
            outputs = model(**inputs)

        for i, feature_index in enumerate(feature_indices):
@@ -21,8 +21,6 @@ import os
 from dataclasses import dataclass, field
 from typing import Optional

-import tensorflow as tf
-
 from transformers import (
    AutoConfig,
    AutoTokenizer,
@@ -70,7 +68,6 @@ class DataTrainingArguments:
    data_dir: Optional[str] = field(
        default=None, metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."}
    )
-    use_tfds: Optional[bool] = field(default=True, metadata={"help": "If TFDS should be used or not."})
    max_seq_length: int = field(
        default=128,
        metadata={
@@ -173,7 +170,7 @@ def main():
        )

    # Get datasets
-    if data_args.use_tfds:
+    if not data_args.data_dir:
        if data_args.version_2_with_negative:
            logger.warn("tensorflow_datasets does not handle version 2 of SQuAD. Switch to version 1 automatically")

@@ -182,7 +179,7 @@ def main():
        except ImportError:
            raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.")

-        tfds_examples = tfds.load("squad", data_dir=data_args.data_dir)
+        tfds_examples = tfds.load("squad")
        train_examples = (
            SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=False)
            if training_args.do_train
@@ -212,8 +209,6 @@ def main():
        else None
    )

-    train_dataset = train_dataset.apply(tf.data.experimental.assert_cardinality(len(train_examples)))
-
    eval_dataset = (
        squad_convert_examples_to_features(
            examples=eval_examples,
@@ -228,8 +223,6 @@ def main():
        else None
    )

-    eval_dataset = eval_dataset.apply(tf.data.experimental.assert_cardinality(len(eval_examples)))
-
    # Initialize our Trainer
    trainer = TFTrainer(model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset,)

@@ -5,7 +5,7 @@ psutil
 sacrebleu
 rouge-score
 tensorflow_datasets
-pytorch-lightning==0.8.5
+pytorch-lightning==0.8.1
 matplotlib
 git-python==1.0.3
 faiss
@@ -13,6 +13,3 @@ streamlit
 elasticsearch
 pandas
 nlp
-fire
-pytest
-conllu
@@ -3,10 +3,23 @@
 This directory contains examples for finetuning and evaluating transformers on summarization and translation tasks.
 Summarization support is more mature than translation support.
 Please tag @sshleifer with any issues/unexpected behaviors, or send a PR!
-For `bertabs` instructions, see [`bertabs/README.md`](bertabs/README.md).
+For `bertabs` instructions, see `bertabs/README.md`. 


 ### Data
+
+CNN/DailyMail data
+```bash
+cd examples/seq2seq
+wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/cnn_dm.tgz
+tar -xzvf cnn_dm.tgz
+
+export CNN_DIR=${PWD}/cnn_dm
+```
+
+this should make a directory called cnn_dm/ with files like `test.source`.
+To use your own data, copy that files format. Each article to be summarized is on its own line.
+
 XSUM Data:
 ```bash
 cd examples/seq2seq
@@ -14,87 +27,42 @@ wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz
 tar -xzvf xsum.tar.gz
 export XSUM_DIR=${PWD}/xsum
 ```
-this should make a directory called `xsum/` with files like `test.source`.
-To use your own data, copy that files format. Each article to be summarized is on its own line.

-CNN/DailyMail data
-```bash
-cd examples/seq2seq
-wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/cnn_dm.tgz
-tar -xzvf cnn_dm.tgz
-export CNN_DIR=${PWD}/cnn_dm
-this should make a directory called `cnn_dm/` with files like `test.source`.
-```

 WMT16 English-Romanian Translation Data:
-download with this command:
 ```bash
+cd examples/seq2seq
 wget https://s3.amazonaws.com/datasets.huggingface.co/translation/wmt_en_ro.tar.gz
 tar -xzvf wmt_en_ro.tar.gz
 export ENRO_DIR=${PWD}/wmt_en_ro
-this should make a directory called `wmt_en_ro/` with files like `test.source`.
 ```

-If you are using your own data, it must be formatted as one directory with 6 files: 
-```
-train.source
-train.target
-val.source
-val.target
-test.source
-test.target
-```
+If you are using your own data, it must be formatted as one directory with 6 files: train.source, train.target, val.source, val.target, test.source, test.target.  
 The `.source` files are the input, the `.target` files are the desired output.


 ### Tips and Tricks

 General Tips:
- since you need to run from `examples/seq2seq`, and likely need to modify code, the easiest workflow is fork transformers, clone your fork, and run `pip install -e .` before you get started.
+- since you need to run from `examples/seq2seq`, and likely need to modify code, the easiest workflow is fork transformers, clone your fork, and run `pip install -e .` before you get started.   
 - try `--freeze_encoder` or `--freeze_embeds` for faster training/larger batch size.  (3hr per epoch with bs=8, see the "xsum_shared_task" command below)
 - `fp16_opt_level=O1` (the default works best).
 - In addition to the pytorch-lightning .ckpt checkpoint, a transformers checkpoint will be saved.
 Load it with `BartForConditionalGeneration.from_pretrained(f'{output_dir}/best_tfmr)`.
 - At the moment, `--do_predict` does not work in a multi-gpu setting. You need to use `evaluate_checkpoint` or the `run_eval.py` code.
- This warning can be safely ignored:
+- This warning can be safely ignored: 
    > "Some weights of BartForConditionalGeneration were not initialized from the model checkpoint at facebook/bart-large-xsum and are newly initialized: ['final_logits_bias']"
 - Both finetuning and eval are 30% faster with `--fp16`. For that you need to [install apex](https://github.com/NVIDIA/apex#quick-start).
- Read scripts before you run them!
+- Read scripts before you run them! 

 Summarization Tips:
 - (summ) 1 epoch at batch size 1 for bart-large takes 24 hours and requires 13GB GPU RAM with fp16 on an NVIDIA-V100.
 - If you want to run experiments on improving the summarization finetuning process, try the XSUM Shared Task (below). It's faster to train than CNNDM because the summaries are shorter.
 - For CNN/DailyMail, the default `val_max_target_length` and `test_max_target_length` will truncate the ground truth labels, resulting in slightly higher rouge scores. To get accurate rouge scores, you should rerun calculate_rouge on the `{output_dir}/test_generations.txt` file saved by `trainer.test()`
 - `--max_target_length=60 --val_max_target_length=60 --test_max_target_length=100 ` is a reasonable setting for XSUM.
- `wandb` can be used by specifying `--logger_name wandb`. It is useful for reproducibility. Specify the environment variable `WANDB_PROJECT='hf_xsum'` to do the XSUM shared task.
+- `wandb` can be used by specifying `--logger wandb`. It is useful for reproducibility. Specify the environment variable `WANDB_PROJECT='hf_xsum'` to do the XSUM shared task.
 - If you are finetuning on your own dataset, start from `distilbart-cnn-12-6` if you want long summaries and `distilbart-xsum-12-6` if you want short summaries.
-(It rarely makes sense to start from `bart-large` unless you are a researching finetuning methods).
-
-**Update 2018-07-18**
-Datasets: `Seq2SeqDataset` should be used for all tokenizers without a `prepare_seq2seq_batch` method. For those who do (like Marian, MBart), `TranslationDataset` should be used.**
-A new dataset is needed to support multilingual tasks.
-
-
-### Command Line Options
-
-To see all the possible command line options, run:
-
-```bash
-./finetune.sh --help  # this calls python finetune.py --help
-```
-
-### Finetuning Training Params
-
-To override the pretrained model's training params, you can pass them to `./finetune.sh`:
-
-```bash
-./finetune.sh \
-    [...]
-    --encoder_layerdrop 0.1 \
-    --decoder_layerdrop 0.1 \
-    --dropout 0.1 \
-    --attention_dropout 0.1 \
-```
+(It rarely makes sense to start from `bart-large` unless you are a researching finetuning methods). 

 ### Summarization Finetuning
 Run/modify `finetune.sh`
@@ -106,42 +74,30 @@ The following command should work on a 16GB GPU:
    --train_batch_size=1 \
    --eval_batch_size=1 \
    --output_dir=xsum_results \
-    --num_train_epochs 6 \
+    --num_train_epochs 1 \
    --model_name_or_path facebook/bart-large
 ```

+*Note*: The following tips mostly apply to summarization finetuning.
+
 ### Translation Finetuning

 First, follow the wmt_en_ro download instructions.
 Then you can finetune mbart_cc25 on english-romanian with the following command.
-**Recommendation:** Read and potentially modify the fairly opinionated defaults in `train_mbart_cc25_enro.sh` script before running it.
-
-Best performing command:
+**Recommendation:** Read and potentially modify the fairly opinionated defaults in `train_mbart_cc25_enro.sh` script before running it. 
 ```bash
-# optionally
-export ENRO_DIR='wmt_en_ro' # Download instructions above
-# export WANDB_PROJECT="MT" # optional
-export MAX_LEN=128
-export BS=8
-./train_mbart_cc25_enro.sh --output_dir enro_finetune_baseline_dropper --label_smoothing 0 --fp16_opt_level=O1 --logger_name wandb --sortish_sampler
-```
-This should take < 6h/epoch on a 16GB v100 and achieve test BLEU above 26
-To get results in line with fairseq, you need to do some postprocessing. (see `romanian_postprocessing.md`)
-
-MultiGPU command
-(using 8 GPUS as an example)
-```bash
-export ENRO_DIR='wmt_en_ro' # Download instructions above
- # export WANDB_PROJECT="MT" # optional
-export MAX_LEN=128
+export ENRO_DIR=${PWD}/wmt_en_ro   # may need to be fixed depending on where you downloaded
 export BS=4
-./train_mbart_cc25_enro.sh --output_dir enro_finetune_baseline --gpus 8 --logger_name wandb
+export GAS=8
+./train_mbart_cc25_enro.sh --output_dir cc25_v1_frozen/
 ```
-### Finetuning Outputs
-As you train, `output_dir` will be filled with files, that look kind of like this (comments are mine).
+
+
+### Finetuning Outputs 
+As you train, `output_dir` will be filled with files, that look kind of like this (comments are mine). 
 Some of them are metrics, some of them are checkpoints, some of them are metadata. Here is a quick tour:

-```
+```bash
 output_dir
 ├── best_tfmr  # this is a huggingface checkpoint generated by save_pretrained. It is the same model as the PL .ckpt file below
 │   ├── config.json
@@ -151,13 +107,13 @@ output_dir
 │   ├── tokenizer_config.json
 │   └── vocab.json
 ├── git_log.json   # repo, branch, and commit hash
-├── val_avg_rouge2=0.1984-step_count=11.ckpt  # this is a pytorch lightning checkpoint associated with the best val score. (it will be called BLEU for MT)
+├── val_avg_rouge2=0.1984-step_count=11.ckpt  # this is a pytorch lightning checkpoint associated with the best val score.
 ├── metrics.json  # new validation metrics will continually be appended to this
 ├── student  # this is a huggingface checkpoint generated by SummarizationDistiller. It is the student before it gets finetuned.
 │   ├── config.json
 │   └── pytorch_model.bin
-├── test_generations.txt
-# ^^ are the summaries or translations produced by your best checkpoint on the test data. Populated when training is done
+├── test_generations.txt 
+# ^^ are the summaries or translations produced by your best checkpoint on the test data. Populated when training is done 
 ├── test_results.txt  # a convenience file with the test set metrics. This data is also in metrics.json['test']
 ├── hparams.pkl  # the command line args passed after some light preprocessing. Should be saved fairly quickly.
 ```
@@ -167,6 +123,23 @@ from transformers import AutoModelForSeq2SeqLM
 model = AutoModelForSeq2SeqLM.from_pretrained(f'{output_dir}/best_tfmr')
 ```

+#### XSUM Shared Task
+Compare XSUM results with others by using `--logger wandb_shared`. This requires `wandb` registration.
+
+Here is an example command, but you can do whatever you want. Hopefully this will make debugging and collaboration easier!
+```bash
+WANDB_PROJECT='hf_xsum' ./finetune.sh \
+    --data_dir $XSUM_DIR \
+    --output_dir xsum_frozen_embs \
+    --model_name_or_path facebook/bart-large \
+    --train_batch_size 16 --eval_batch_size 16 --freeze_embeds --freeze_encoder \
+    --num_train_epochs 6 \
+    --max_target_length=60 --val_max_target_length=60 --test_max_target_length=100 \
+    --logger wandb
+```
+
+You can see your wandb logs [here](https://app.wandb.ai/sshleifer/hf_xsum?workspace=user-)
+
 ### Evaluation Commands

 To create summaries for each article in dataset, we use `run_eval.py`, here are a few commands that run eval for different tasks and models.
@@ -175,7 +148,7 @@ If 'translation' is in your task name, the computed metric will be BLEU. Otherwi
 For t5, you need to specify --task translation_{src}_to_{tgt} as follows:
 ```bash
 export DATA_DIR=wmt_en_ro
-python run_eval.py t5-base \
+python run_eval.py t5_base \
    $DATA_DIR/val.source t5_val_generations.txt \
    --reference_path $DATA_DIR/val.target \
    --score_path enro_bleu.json \
@@ -208,8 +181,6 @@ python run_eval.py sshleifer/distilbart-cnn-12-6 $DATA_DIR/val.source dbart_val_
    --task summarization \
    --n_obs 100 \
    --device cuda \
-    --max_source_length 1024 \
-    --max_target_length 56 \
    --fp16 \
    --bs 32
 ```
@@ -219,7 +190,7 @@ python run_eval.py sshleifer/distilbart-cnn-12-6 $DATA_DIR/val.source dbart_val_
 ![DBART](https://huggingface.co/front/thumbnails/distilbart_large.png)

 For the CNN/DailyMail dataset, (relatively longer, more extractive summaries), we found a simple technique that works:
-you just copy alternating layers from `bart-large-cnn` and finetune more on the same data.
+you just copy alternating layers from `bart-large-cnn` and finetune more on the same data. 

 For the XSUM dataset, that didn’t work as well so we used that same initialization strategy followed by a combination of Distillbert’s ce_loss and the hidden states MSE loss used in the tinybert paper.

@@ -235,7 +206,7 @@ They are initialized by copying layers from the associated `bart-large-{cnn|xsum
 The command that produced `sshleifer/distilbart-cnn-12-6` is
 ```bash
 ./train_distilbart_cnn.sh
-```
+```  
 runtime: 6H on NVIDIA RTX 24GB GPU

 *Note*: You can get the same simple distillation logic by using `./run_distiller.sh --no_teacher` followed by identical arguments as the ones in `train_distilbart_cnn.sh`.
@@ -251,15 +222,15 @@ This is how `sshleifer/distilbart-xsum*` checkpoints were produced.
 The command that produced `sshleifer/distilbart-xsum-12-6` is:

 ```bash
-./train_distilbart_xsum.sh
+./train_distilbart_xsum.sh  
 ```

-runtime: 13H on V-100 16GB GPU.
+runtime: 13H on V-100 16GB GPU. 

 ### Contributing
 - follow the standard contributing guidelines and code of conduct.
 - add tests to `test_seq2seq_examples.py`
 - To run only the seq2seq tests, you must be in the root of the repository and run:
 ```bash
-pytest examples/seq2seq/
+pytest examples/seq2seq/  
 ```
@@ -5,7 +5,7 @@ from pathlib import Path
 import numpy as np
 import pytorch_lightning as pl
 import torch
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
+from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.utilities import rank_zero_only


@@ -19,10 +19,6 @@ logger = logging.getLogger(__name__)


 class Seq2SeqLoggingCallback(pl.Callback):
-    def on_batch_end(self, trainer, pl_module):
-        lrs = {f"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
-        pl_module.logger.log_metrics(lrs)
-
    @rank_zero_only
    def _write_logs(
        self, trainer: pl.Trainer, pl_module: pl.LightningModule, type_path: str, save_generations=True
@@ -94,7 +90,3 @@ def get_checkpoint_callback(output_dir, metric):
        period=0,  # maybe save a checkpoint every time val is run, not just end of epoch.
    )
    return checkpoint_callback
-
-
-def get_early_stopping_callback(metric, patience):
-    return EarlyStopping(monitor=f"val_{metric}", mode="max", patience=patience, verbose=True,)
@@ -1,21 +0,0 @@
-from typing import Union
-
-import fire
-import torch
-from tqdm import tqdm
-
-
-def convert(src_path: str, map_location: str = "cpu", save_path: Union[str, None] = None) -> None:
-    """Convert a pytorch_model.bin or model.pt file to torch.float16 for faster downloads, less disk space."""
-    state_dict = torch.load(src_path, map_location=map_location)
-    for k, v in tqdm(state_dict.items()):
-        if not isinstance(v, torch.Tensor):
-            raise TypeError("FP16 conversion only works on paths that are saved state dics, like pytorch_model.bin")
-        state_dict[k] = v.half()
-    if save_path is None:  # overwrite src_path
-        save_path = src_path
-    torch.save(state_dict, save_path)
-
-
-if __name__ == "__main__":
-    fire.Fire(convert)
@@ -10,32 +10,32 @@ from torch import nn
 from torch.nn import functional as F

 from lightning_base import generic_train
-from transformers import BartConfig, BartForConditionalGeneration, MBartTokenizer, T5Config, T5ForConditionalGeneration
+from transformers import AdamW, BartConfig, BartForConditionalGeneration, T5Config, T5ForConditionalGeneration


 try:
-    from .finetune import SummarizationModule, TranslationModule
+    from .finetune import SummarizationModule
    from .initialization_utils import init_student, copy_layers
    from .utils import (
        use_task_specific_params,
+        SummarizationDataset,
        pickle_load,
        freeze_params,
        assert_all_frozen,
        any_requires_grad,
-        calculate_bleu_score,
    )
    from .finetune import main as ft_main
 except ImportError:
-    from finetune import SummarizationModule, TranslationModule
+    from finetune import SummarizationModule
    from finetune import main as ft_main
    from initialization_utils import init_student, copy_layers
    from utils import (
        use_task_specific_params,
+        SummarizationDataset,
        pickle_load,
        freeze_params,
        assert_all_frozen,
        any_requires_grad,
-        calculate_bleu_score,
    )


@@ -115,6 +115,11 @@ class BartSummarizationDistiller(SummarizationModule):
        if self.different_encoder:
            copy_layers(teacher.encoder.block, student.encoder.block, e_layers_to_copy)

+    def get_dataset(self, type_path) -> SummarizationDataset:
+        n_obs = self.n_obs[type_path]
+        dataset = SummarizationDataset(self.tokenizer, type_path=type_path, n_obs=n_obs, **self.dataset_kwargs)
+        return dataset
+
    def calc_mse_loss(self, teacher_outputs: torch.Tensor, student_outputs: torch.Tensor, mask) -> torch.FloatTensor:
        if mask is not None:
            # mask has False at padding_idx
@@ -151,10 +156,38 @@ class BartSummarizationDistiller(SummarizationModule):
        )
        return loss_ce, s_logits_slct, t_logits_slct

+    def configure_optimizers(self):
+        "Prepare optimizer and schedule (linear warmup and decay)"
+        model = self.model
+        no_decay = ["bias", "LayerNorm.weight"]
+        optimizer_grouped_parameters = [
+            {
+                "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
+                "weight_decay": self.hparams.weight_decay,
+            },
+            {
+                "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
+                "weight_decay": 0.0,
+            },
+        ]
+        optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)
+        self.opt = optimizer
+        return [optimizer]
+
    @staticmethod
    def add_model_specific_args(parser, root_dir):
        SummarizationModule.add_model_specific_args(parser, root_dir)
-        add_distill_args(parser)
+        parser.add_argument("--teacher", default="facebook/bart-large-cnn", type=str)
+        parser.add_argument("--alpha_ce", default=0.8, type=float)
+        parser.add_argument("--alpha_mlm", default=0.2, type=float)
+        # parser.add_argument("--alpha_cos", default=0.0, type=float)
+        parser.add_argument("--alpha_encoder_loss", default=0.0, type=float)
+        parser.add_argument("--alpha_hid", default=0.0, type=float, required=False)
+        parser.add_argument("--student_decoder_layers", default=12, type=int, required=False)
+        parser.add_argument("--student_encoder_layers", default=12, type=int, required=False)
+        parser.add_argument("--no_teacher", action="store_true", default=False)
+        parser.add_argument("--length_penalty", type=float, default=-1)
+
        return parser

    def _step(self, batch):
@@ -232,44 +265,6 @@ class BartSummarizationDistiller(SummarizationModule):
        return sum(hidden_losses)


-def add_distill_args(parser):
-    parser.add_argument("--teacher", default="facebook/bart-large-cnn", type=str)
-    parser.add_argument("--alpha_ce", default=0.8, type=float)
-    parser.add_argument("--alpha_mlm", default=0.2, type=float)
-    parser.add_argument("--alpha_encoder_loss", default=0.0, type=float)
-    parser.add_argument("--alpha_hid", default=0.0, type=float, required=False)
-    parser.add_argument("--student_decoder_layers", default=12, type=int, required=False)
-    parser.add_argument("--student_encoder_layers", default=12, type=int, required=False)
-    parser.add_argument("--no_teacher", action="store_true", default=False)
-    parser.add_argument("--length_penalty", type=float, default=-1)
-
-
-class BartTranslationDistiller(BartSummarizationDistiller):
-    mode = "translation"
-    loss_names = ["loss"]
-    metric_names = ["bleu"]
-    val_metric = "bleu"
-
-    def __init__(self, hparams, **kwargs):
-        super().__init__(hparams, **kwargs)
-        assert isinstance(self.tokenizer, MBartTokenizer)
-        assert hparams.src_lang is not None
-        assert hparams.tgt_lang is not None
-        self.dataset_kwargs["src_lang"] = hparams.src_lang
-        self.dataset_kwargs["tgt_lang"] = hparams.tgt_lang
-        if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer, MBartTokenizer):
-            self.decoder_start_token_id = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
-
-    def calc_generative_metrics(self, preds, target) -> dict:
-        return calculate_bleu_score(preds, target)
-
-    @staticmethod
-    def add_model_specific_args(parser, root_dir):
-        TranslationModule.add_model_specific_args(parser, root_dir)
-        add_distill_args(parser)
-        return parser
-
-
 class T5SummarizationDistiller(BartSummarizationDistiller):
    def pre_init(self, hparams):
        raise NotImplementedError("T5 Distillation does not work yet")
@@ -387,14 +382,15 @@ class T5SummarizationDistiller(BartSummarizationDistiller):
 def create_module(args):
    t5 = "t5" in args.model_name_or_path
    if args.no_teacher:
-        module_cls = TranslationModule if "translation" in args.task else SummarizationModule
-    elif t5:  # DISTILL T5 WITH TEACHER FOR SUMMARIZATION
-        assert "translation" not in args.task, "t5 translation distillation not supported"
+        assert not args.enc_only
+        module_cls = SummarizationModule
+    elif t5:
        module_cls = T5SummarizationDistiller
-    else:  # DISTILL WITH TEACHER
-        module_cls = BartTranslationDistiller if "translation" in args.task else BartSummarizationDistiller
+    elif args.enc_only:
+        raise ValueError("Deleted that")
+    else:
+        module_cls = BartSummarizationDistiller
    args.setup_cls: str = module_cls.__name__
-    print(f"using module {args.setup_cls}")
    model = module_cls(args)
    return model

@@ -435,18 +431,6 @@ def get_layers_to_copy(n_to_get, tot):
            12: all_layers,
        }
        return layers_to_copy[n_to_get]
-    elif tot == 16:
-        layers_to_copy = {  # maps  num layers in student -> which teacher layers to copy
-            1: [0],
-            2: [0, 8],
-            3: [0, 8, 15],
-            4: [0, 5, 10, 15],
-            6: [0, 3, 6, 9, 12, 15],
-            8: [0, 2, 4, 6, 8, 10, 12, 15],
-            9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
-            16: all_layers,
-        }
-        return layers_to_copy[n_to_get]
    else:
        return all_layers[:n_to_get]  # TODO: better version on theseus-bart branch

@@ -462,7 +446,6 @@ def distill_main(args):

 if __name__ == "__main__":
    parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
    parser = BartSummarizationDistiller.add_model_specific_args(parser, os.getcwd())
    args = parser.parse_args()

@@ -1,52 +0,0 @@
-from pathlib import Path
-
-import fire
-from tqdm import tqdm
-
-
-def download_wmt_dataset(src_lang="ro", tgt_lang="en", dataset="wmt16", save_dir=None) -> None:
-    """Download a dataset using the nlp package and save it to the format expected by finetune.py
-    Format of save_dir: train.source, train.target, val.source, val.target, test.source, test.target.
-
-    Args:
-        src_lang: <str> source language
-        tgt_lang: <str> target language
-        dataset: <str> wmt16, wmt17, etc. wmt16 is a good start as it's small. To get the full list run `import nlp; print([d.id for d in nlp.list_datasets() if "wmt" in d.id])`
-        save_dir: <str>, where to save the datasets, defaults to f'{dataset}-{src_lang}-{tgt_lang}'
-
-    Usage:
-        >>> download_wmt_dataset('ro', 'en', dataset='wmt16') # saves to wmt16-ro-en
-    """
-    try:
-        import nlp
-    except (ModuleNotFoundError, ImportError):
-        raise ImportError("run pip install nlp")
-    pair = f"{src_lang}-{tgt_lang}"
-    print(f"Converting {dataset}-{pair}")
-    ds = nlp.load_dataset(dataset, pair)
-    if save_dir is None:
-        save_dir = f"{dataset}-{pair}"
-    save_dir = Path(save_dir)
-    save_dir.mkdir(exist_ok=True)
-
-    for split in ds.keys():
-        print(f"Splitting {split} with {ds[split].num_rows} records")
-
-        # to save to val.source, val.target like summary datasets
-        fn = "val" if split == "validation" else split
-        src_path = save_dir.joinpath(f"{fn}.source")
-        tgt_path = save_dir.joinpath(f"{fn}.target")
-        src_fp = src_path.open("w+")
-        tgt_fp = tgt_path.open("w+")
-
-        # reader is the bottleneck so writing one record at a time doesn't slow things down
-        for x in tqdm(ds[split]):
-            ex = x["translation"]
-            src_fp.write(ex[src_lang] + "\n")
-            tgt_fp.write(ex[tgt_lang] + "\n")
-
-    print(f"Saved {dataset} dataset to {save_dir}")
-
-
-if __name__ == "__main__":
-    fire.Fire(download_wmt_dataset)
@@ -3,6 +3,7 @@ import glob
 import logging
 import os
 import time
+import warnings
 from collections import defaultdict
 from pathlib import Path
 from typing import Dict, List, Tuple
@@ -13,14 +14,14 @@ import torch
 from torch.utils.data import DataLoader

 from lightning_base import BaseTransformer, add_generic_args, generic_train
-from transformers import MarianTokenizer, MBartTokenizer, T5ForConditionalGeneration
-from transformers.modeling_bart import shift_tokens_right
+from transformers import MBartTokenizer, get_linear_schedule_with_warmup


 try:
    from .utils import (
        assert_all_frozen,
        use_task_specific_params,
+        SummarizationDataset,
        lmap,
        flatten_list,
        pickle_save,
@@ -31,19 +32,12 @@ try:
        get_git_info,
        ROUGE_KEYS,
        calculate_bleu_score,
-        Seq2SeqDataset,
-        TranslationDataset,
-        label_smoothed_nll_loss,
    )
-
-    from .callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
-    from .loss_dropper import LossDropper
+    from .callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback
 except ImportError:
    from utils import (
-        Seq2SeqDataset,
-        TranslationDataset,
-        assert_all_frozen,
        use_task_specific_params,
+        SummarizationDataset,
        lmap,
        flatten_list,
        pickle_save,
@@ -54,24 +48,22 @@ except ImportError:
        get_git_info,
        ROUGE_KEYS,
        calculate_bleu_score,
-        label_smoothed_nll_loss,
+        assert_all_frozen,
    )
-    from callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
-    from loss_dropper import LossDropper
+    from callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback

 logger = logging.getLogger(__name__)


 class SummarizationModule(BaseTransformer):
    mode = "summarization"
-    loss_names = ["loss", "dropper_mask_mean"]
+    loss_names = ["loss"]
    metric_names = ROUGE_KEYS
    val_metric = "rouge2"

    def __init__(self, hparams, **kwargs):
        super().__init__(hparams, num_labels=None, mode=self.mode, **kwargs)
        use_task_specific_params(self.model, "summarization")
-        self.dropper = LossDropper(dropc=.05)
        save_git_info(self.hparams.output_dir)
        self.metrics_save_path = Path(self.output_dir) / "metrics.json"
        self.hparams_save_path = Path(self.output_dir) / "hparams.pkl"
@@ -108,13 +100,6 @@ class SummarizationModule(BaseTransformer):
        self.hparams.git_sha = get_git_info()["repo_sha"]
        self.num_workers = hparams.num_workers
        self.decoder_start_token_id = None
-        if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer, MBartTokenizer):
-            self.decoder_start_token_id = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
-            self.model.config.decoder_start_token_id = self.decoder_start_token_id
-        if isinstance(self.tokenizer, MBartTokenizer) or isinstance(self.tokenizer, MarianTokenizer):
-            self.dataset_class = TranslationDataset
-        else:
-            self.dataset_class = Seq2SeqDataset

    def freeze_embeds(self):
        """Freeze token embeddings and positional embeddings for bart, just token embeddings for t5."""
@@ -139,56 +124,17 @@ class SummarizationModule(BaseTransformer):

    def _step(self, batch: dict) -> Tuple:
        pad_token_id = self.tokenizer.pad_token_id
-        source_ids, source_mask, target_ids = batch["input_ids"], batch["attention_mask"], batch["decoder_input_ids"]
-
-        if "labels" in batch:
-            lm_labels = batch["labels"]
-            decoder_input_ids = shift_tokens_right(lm_labels, pad_token_id)
-        elif isinstance(self.model, T5ForConditionalGeneration):
-            decoder_input_ids = self.model._shift_right(target_ids)
-            lm_labels = target_ids
-        else:
-            decoder_input_ids = target_ids[:, :-1].contiguous()  # Why this line?
-            lm_labels = target_ids[:, 1:].clone()  # why clone?
-
-        outputs = self(source_ids, attention_mask=source_mask, decoder_input_ids=decoder_input_ids, use_cache=False)
-        bs = source_ids.shape[0]
-
-        if self.hparams.label_smoothing == 0:
-
-            # Same behavior as modeling_bart.py
-            loss_fct = torch.nn.CrossEntropyLoss(reduction='none', ignore_index=pad_token_id)
-            lm_logits = outputs[0]
-            assert lm_logits.shape[-1] == self.model.config.vocab_size
-
-            #loss_fct = torch.nn.NLLLoss(reduction='none', ignore_index=pad_token_id)
-            #logit_shape =
-            #weights = torch.ones(logit_shape
-            loss = loss_fct(lm_logits.view(-1, lm_logits.shape[-1]), lm_labels.view(-1))
-            loss = loss.view(-1, bs)
-            loss = loss.mean(dim=0)
-            mask = self.dropper(loss)
-            loss *= mask
-            loss = loss.mean()
-            return (loss, 1-mask.mean())
-            #loss = loss.view(-1, bs)
-        else:
-            lprobs = torch.nn.functional.log_softmax(outputs[0], dim=-1)
-            loss, nll_loss = label_smoothed_nll_loss(
-                lprobs, lm_labels, self.hparams.label_smoothing, ignore_index=pad_token_id
-            )
-        return (loss,torch.tensor(1.))
-
-    @property
-    def pad(self) -> int:
-        return self.tokenizer.pad_token_id
+        source_ids, source_mask, y = batch["input_ids"], batch["attention_mask"], batch["decoder_input_ids"]
+        y_ids = y[:, :-1].contiguous()
+        lm_labels = y[:, 1:].clone()
+        lm_labels[y[:, 1:] == pad_token_id] = -100
+        outputs = self(source_ids, attention_mask=source_mask, decoder_input_ids=y_ids, labels=lm_labels,)
+        loss = outputs[0]
+        return (loss,)

    def training_step(self, batch, batch_idx) -> Dict:
        loss_tensors = self._step(batch)
-
        logs = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
-        # tokens per batch
-        logs["tpb"] = batch["input_ids"].ne(self.pad).sum() + batch["decoder_input_ids"].ne(self.pad).sum()
        return {"loss": loss_tensors[0], "log": logs}

    def validation_step(self, batch, batch_idx) -> Dict:
@@ -198,7 +144,7 @@ class SummarizationModule(BaseTransformer):
        self.step_count += 1
        losses = {k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
        loss = losses["loss"]
-        rouges = {k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ["gen_time", "gen_len"]}
+        rouges = {k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ["gen_time", "summ_len"]}
        rouge_tensor: torch.FloatTensor = torch.tensor(rouges[self.val_metric]).type_as(loss)
        rouges.update({k: v.item() for k, v in losses.items()})
        losses.update(rouges)
@@ -216,21 +162,23 @@ class SummarizationModule(BaseTransformer):
        return calculate_rouge(preds, target)

    def _generative_step(self, batch: dict) -> dict:
+        pad_token_id = self.tokenizer.pad_token_id
+        source_ids, source_mask, y = SummarizationDataset.trim_seq2seq_batch(batch, pad_token_id)
        t0 = time.time()
        generated_ids = self.model.generate(
-            batch["input_ids"],
-            attention_mask=batch["attention_mask"],
+            input_ids=source_ids,
+            attention_mask=source_mask,
            use_cache=True,
            decoder_start_token_id=self.decoder_start_token_id,
        )
-        gen_time = (time.time() - t0) / batch["input_ids"].shape[0]
-        preds: List[str] = self.ids_to_clean_text(generated_ids)
-        target: List[str] = self.ids_to_clean_text(batch["decoder_input_ids"])
+        gen_time = (time.time() - t0) / source_ids.shape[0]
+        preds = self.ids_to_clean_text(generated_ids)
+        target = self.ids_to_clean_text(y)
        loss_tensors = self._step(batch)
        base_metrics = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
        rouge: Dict = self.calc_generative_metrics(preds, target)
        summ_len = np.mean(lmap(len, generated_ids))
-        base_metrics.update(gen_time=gen_time, gen_len=summ_len, preds=preds, target=target, **rouge)
+        base_metrics.update(gen_time=gen_time, summ_len=summ_len, preds=preds, target=target, **rouge)
        return base_metrics

    def test_step(self, batch, batch_idx):
@@ -239,10 +187,10 @@ class SummarizationModule(BaseTransformer):
    def test_epoch_end(self, outputs):
        return self.validation_epoch_end(outputs, prefix="test")

-    def get_dataset(self, type_path) -> Seq2SeqDataset:
+    def get_dataset(self, type_path) -> SummarizationDataset:
        n_obs = self.n_obs[type_path]
        max_target_length = self.target_lens[type_path]
-        dataset = self.dataset_class(
+        dataset = SummarizationDataset(
            self.tokenizer,
            type_path=type_path,
            n_obs=n_obs,
@@ -271,6 +219,17 @@ class SummarizationModule(BaseTransformer):

    def train_dataloader(self) -> DataLoader:
        dataloader = self.get_dataloader("train", batch_size=self.hparams.train_batch_size, shuffle=True)
+        t_total = (
+            (len(dataloader.dataset) // (self.hparams.train_batch_size * max(1, self.hparams.gpus)))
+            // self.hparams.gradient_accumulation_steps
+            * float(self.hparams.num_train_epochs)
+        )
+        scheduler = get_linear_schedule_with_warmup(
+            self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=t_total
+        )
+        if max(scheduler.get_last_lr()) > 0:
+            warnings.warn("All learning rates are 0")
+        self.lr_scheduler = scheduler
        return dataloader

    def val_dataloader(self) -> DataLoader:
@@ -311,32 +270,31 @@ class SummarizationModule(BaseTransformer):
            help="The maximum total input sequence length after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded.",
        )
+        parser.add_argument(
+            "--data_dir",
+            type=str,
+            required=True,
+            help="The input data dir. Should contain train.source, train.target, val.source, val.target, test.source, test.target",
+        )
        parser.add_argument("--freeze_encoder", action="store_true")
        parser.add_argument("--freeze_embeds", action="store_true")
        parser.add_argument("--sortish_sampler", action="store_true", default=False)
-        parser.add_argument("--logger_name", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
+        parser.add_argument("--logger", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
        parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.")
        parser.add_argument("--n_val", type=int, default=500, required=False, help="# examples. -1 means use all.")
        parser.add_argument("--n_test", type=int, default=-1, required=False, help="# examples. -1 means use all.")
        parser.add_argument(
            "--task", type=str, default="summarization", required=False, help="# examples. -1 means use all."
        )
-        parser.add_argument("--label_smoothing", type=float, default=0.0, required=False)
-        parser.add_argument("--loss_dropper", type=float, default=0.0, required=False)
        parser.add_argument("--src_lang", type=str, default="", required=False)
        parser.add_argument("--tgt_lang", type=str, default="", required=False)
-        parser.add_argument(
-            "--early_stopping_patience",
-            type=int,
-            default=-1,
-            required=False,
-            help="-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So val_check_interval will effect it.",
-        )
+
        return parser


 class TranslationModule(SummarizationModule):
    mode = "translation"
+    loss_names = ["loss"]
    metric_names = ["bleu"]
    val_metric = "bleu"

@@ -344,6 +302,8 @@ class TranslationModule(SummarizationModule):
        super().__init__(hparams, **kwargs)
        self.dataset_kwargs["src_lang"] = hparams.src_lang
        self.dataset_kwargs["tgt_lang"] = hparams.tgt_lang
+        if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer, MBartTokenizer):
+            self.decoder_start_token_id = self.tokenizer.lang_code_to_id[hparams.tgt_lang]

    def calc_generative_metrics(self, preds, target) -> dict:
        return calculate_bleu_score(preds, target)
@@ -358,36 +318,27 @@ def main(args, model=None) -> SummarizationModule:
            model: SummarizationModule = SummarizationModule(args)
        else:
            model: SummarizationModule = TranslationModule(args)
-
-    dataset = Path(args.data_dir).name
    if (
-        args.logger_name == "default"
+        args.logger == "default"
        or args.fast_dev_run
        or str(args.output_dir).startswith("/tmp")
        or str(args.output_dir).startswith("/var")
    ):
        logger = True  # don't pollute wandb logs unnecessarily
-    elif args.logger_name == "wandb":
+    elif args.logger == "wandb":
        from pytorch_lightning.loggers import WandbLogger

-        project = os.environ.get("WANDB_PROJECT", dataset)
-        logger = WandbLogger(name=model.output_dir.name, project=project)
+        logger = WandbLogger(name=model.output_dir.name)

-    elif args.logger_name == "wandb_shared":
+    elif args.logger == "wandb_shared":
        from pytorch_lightning.loggers import WandbLogger

-        logger = WandbLogger(name=model.output_dir.name, project=f"hf_{dataset}")
-
-    if args.early_stopping_patience >= 0:
-        es_callback = get_early_stopping_callback(model.val_metric, args.early_stopping_patience)
-    else:
-        es_callback = False
+        logger = WandbLogger(name=model.output_dir.name)
    trainer: pl.Trainer = generic_train(
        model,
        args,
        logging_callback=Seq2SeqLoggingCallback(),
        checkpoint_callback=get_checkpoint_callback(args.output_dir, model.val_metric),
-        early_stopping_callback=es_callback,
        logger=logger,
        # TODO: early stopping callback seems messed up
    )
@@ -401,17 +352,13 @@ def main(args, model=None) -> SummarizationModule:
        model.hparams.test_checkpoint = checkpoints[-1]
        trainer.resume_from_checkpoint = checkpoints[-1]
    trainer.logger.log_hyperparams(model.hparams)
-
-    # test() without a model tests using the best checkpoint automatically
-    trainer.test()
+    trainer.test(model)  # this breaks in DDP, known lightning issue. See evaluate_checkpoint to recover metrics.
    return model


 if __name__ == "__main__":
    parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
    parser = SummarizationModule.add_model_specific_args(parser, os.getcwd())
-
    args = parser.parse_args()

    main(args)
@@ -2,7 +2,6 @@
 export PYTHONPATH="../":"${PYTHONPATH}"

 # the proper usage is documented in the README, you need to specify data_dir, output_dir and model_name_or_path
-# run ./finetune.sh --help to see all the possible options
 python finetune.py \
    --learning_rate=3e-5 \
    --fp16 \
@@ -11,4 +10,5 @@ python finetune.py \
    --do_predict \
    --n_val 1000 \
    --val_check_interval 0.1 \
-    "$@"
+    --sortish_sampler \
+    $@
@@ -23,7 +23,7 @@ python finetune.py \
 --output_dir=$OUTPUT_DIR \
 --num_train_epochs=1  \
 --gpus=0 \
--do_train "$@"
+--do_train $@

 rm -rf cnn_tiny
 rm -rf $OUTPUT_DIR
@@ -1,14 +0,0 @@
-#!/usr/bin/env bash
-export PYTHONPATH="../":"${PYTHONPATH}"
-
-# From appendix C of paper https://arxiv.org/abs/1912.08777
-# Set --gradient_accumulation_steps  so that effective batch size is 256 (2*128, 4*64, 8*32, 16*16)
-python finetune.py \
-    --learning_rate=1e-4 \
-    --do_train \
-    --do_predict \
-    --n_val 1000 \
-    --val_check_interval 0.25 \
-    --max_source_length 512 --max_target_length 56 \
-    --freeze_embeds --max_target_length 56 --label_smoothing 0.1 \
-    "$@"
@@ -8,7 +8,6 @@ python finetune.py \
 --eval_batch_size=$BS \
 --output_dir=$OUTPUT_DIR \
 --max_source_length=512 \
--max_target_length=56 \
 --val_check_interval=0.1 --n_val=200 \
 --do_train --do_predict \
- "$@"
+ $@
@@ -1,53 +0,0 @@
-import numpy as np
-import torch.nn as nn
-
-
-class LossDropper(nn.Module):
-    def __init__(
-            self,
-            dropc=0.4,
-            min_count=10000,
-            recompute=10000,
-            verbose=True
-    ):
-        super().__init__()
-        self.keepc = 1. - dropc
-        self.count = 0
-        self.min_count = min_count
-
-        self.recompute = recompute
-        self.last_computed = 0
-        self.percentile_val = 100000000.
-        self.cur_idx = 0
-
-        self.verbose = verbose
-
-        self.vals = np.zeros(self.recompute, dtype=np.float32)
-
-    def forward(self, loss):
-        if loss is None:
-            return loss
-
-        self.last_computed += loss.numel()
-        self.count += loss.numel()
-        if self.count < len(self.vals):
-            self.vals[self.count - loss.numel():self.count] = loss.detach().cpu().numpy().flatten()
-            self.cur_idx += loss.numel()
-            return (loss < np.inf).type(loss.dtype)
-        else:
-            for idx, item in enumerate(loss):
-                self.vals[self.cur_idx] = item
-                self.cur_idx += 1
-                if self.cur_idx >= len(self.vals):
-                    self.cur_idx = 0
-        if self.count < self.min_count:
-            return (loss < np.inf).type(loss.dtype)
-
-        if self.last_computed > self.recompute:
-            self.percentile_val = np.percentile(self.vals, self.keepc * 100)
-            if self.verbose:
-                print('Using cutoff', self.percentile_val)
-            self.last_computed = 0
-
-        mask = (loss < self.percentile_val).type(loss.dtype)
-        return mask
@@ -1,19 +0,0 @@
-from pathlib import Path
-
-import fire
-
-
-def minify(src_dir: str, dest_dir: str, n: int):
-    """Write first n lines of each file f in src_dir to dest_dir/f """
-    src_dir = Path(src_dir)
-    dest_dir = Path(dest_dir)
-    dest_dir.mkdir(exist_ok=True)
-    for path in src_dir.iterdir():
-        new = [x.rstrip() for x in list(path.open().readlines())][:n]
-        dest_path = dest_dir.joinpath(path.name)
-        print(dest_path)
-        dest_path.open("w").write("\n".join(new))
-
-
-if __name__ == "__main__":
-    fire.Fire(minify)
@@ -1,74 +0,0 @@
-"""Fill examples with bitext up to max_tokens without breaking up examples.
-[['I went', 'yo fui'],
-['to the store', 'a la tienda']
-]
-=> ['I went to the store', 'yo fui a la tienda']
-"""
-
-import argparse
-import shutil
-from pathlib import Path
-
-from tqdm import tqdm
-
-from transformers import AutoTokenizer
-
-
-def pack_examples(tok, src_examples, tgt_examples, max_tokens=1024):
-
-    finished_src, finished_tgt = [], []
-
-    sorted_examples = list(zip(src_examples, tgt_examples))
-    new_src, new_tgt = sorted_examples[0]
-
-    def is_too_big(strang):
-        return tok(strang, return_tensors="pt").input_ids.shape[1] > max_tokens
-
-    for src, tgt in tqdm(sorted_examples[1:]):
-        cand_src = new_src + " " + src
-        cand_tgt = new_tgt + " " + tgt
-        if is_too_big(cand_src) or is_too_big(cand_tgt):  # cant fit, finalize example
-            finished_src.append(new_src)
-            finished_tgt.append(new_tgt)
-            new_src, new_tgt = src, tgt
-        else:  # can fit, keep adding
-            new_src, new_tgt = cand_src, cand_tgt
-
-    # cleanup
-    if new_src:
-        assert new_tgt
-        finished_src.append(new_src)
-        finished_tgt.append(new_tgt)
-    return finished_src, finished_tgt
-
-
-def pack_data_dir(tok, data_dir: Path, max_tokens, save_path):
-    save_path = Path(save_path)
-    save_path.mkdir(exist_ok=True)
-    for split in ["train"]:
-        src_path, tgt_path = data_dir / f"{split}.source", data_dir / f"{split}.target"
-        src_docs = [x.rstrip() for x in Path(src_path).open().readlines()]
-        tgt_docs = [x.rstrip() for x in Path(tgt_path).open().readlines()]
-        packed_src, packed_tgt = pack_examples(tok, src_docs, tgt_docs, max_tokens)
-        print(f"packed {split} split from {len(src_docs)} examples -> {len(packed_src)}.")
-        Path(save_path / f"{split}.source").open("w").write("\n".join(packed_src))
-        Path(save_path / f"{split}.target").open("w").write("\n".join(packed_tgt))
-    for split in ["val", "test"]:
-        src_path, tgt_path = data_dir / f"{split}.source", data_dir / f"{split}.target"
-        shutil.copyfile(src_path, save_path / f"{split}.source")
-        shutil.copyfile(tgt_path, save_path / f"{split}.target")
-
-
-def packer_cli():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--tok_name", type=str, help="like facebook/bart-large-cnn,t5-base, etc.")
-    parser.add_argument("--max_seq_len", type=int, default=128)
-    parser.add_argument("--data_dir", type=str)
-    parser.add_argument("--save_path", type=str)
-    args = parser.parse_args()
-    tokenizer = AutoTokenizer.from_pretrained(args.tok_name)
-    return pack_data_dir(tokenizer, Path(args.data_dir), args.max_seq_len, args.save_path)
-
-
-if __name__ == "__main__":
-    packer_cli()
@@ -1,65 +0,0 @@
-### Motivation
-Without processing, english-> romanian mbart-large-en-ro gets BLEU score 26.8 on the WMT data.
-With post processing, it can score 37..
-Here is the postprocessing code, stolen from @mjpost in this [issue](https://github.com/pytorch/fairseq/issues/1758)
-
-
-
-### Instructions
-Note: You need to have your test_generations.txt before you start this process.
-(1) Setup `mosesdecoder` and `wmt16-scripts`
-```bash
-cd $HOME
-git clone git@github.com:moses-smt/mosesdecoder.git
-cd mosesdecoder  
-git@github.com:rsennrich/wmt16-scripts.git
-```
-
-(2) define a function for post processing.
- It removes diacritics and does other things I don't understand 
-```bash
-ro_post_process () {
-  sys=$1
-  ref=$2
-  export MOSES_PATH=$HOME/mosesdecoder
-  REPLACE_UNICODE_PUNCT=$MOSES_PATH/scripts/tokenizer/replace-unicode-punctuation.perl
-  NORM_PUNC=$MOSES_PATH/scripts/tokenizer/normalize-punctuation.perl
-  REM_NON_PRINT_CHAR=$MOSES_PATH/scripts/tokenizer/remove-non-printing-char.perl
-  REMOVE_DIACRITICS=$MOSES_PATH/wmt16-scripts/preprocess/remove-diacritics.py
-  NORMALIZE_ROMANIAN=$MOSES_PATH/wmt16-scripts/preprocess/normalise-romanian.py
-  TOKENIZER=$MOSES_PATH/scripts/tokenizer/tokenizer.perl
-
-
-
-  lang=ro
-  for file in $sys $ref; do
-    cat $file \
-    | $REPLACE_UNICODE_PUNCT \
-    | $NORM_PUNC -l $lang \
-    | $REM_NON_PRINT_CHAR \
-    | $NORMALIZE_ROMANIAN \
-    | $REMOVE_DIACRITICS \
-    | $TOKENIZER -no-escape -l $lang \
-    > $(basename $file).tok
-  done
-  # compute BLEU
-  cat $(basename $sys).tok | sacrebleu -tok none -s none -b $(basename $ref).tok
-}
-```
-
-(3) Call the function on test_generations.txt and test.target
-For example,
-```bash
-ro_post_process enro_finetune/test_generations.txt wmt_en_ro/test.target
-```
-This will split out a new blue score and write a new fine called `test_generations.tok` with post-processed outputs.
-
-
-
-
-
-
-
-
-
-```
@@ -7,4 +7,4 @@ python distillation.py \
 --do_predict \
 --fp16 \
 --val_check_interval 0.1 \
-"$@"
+$@
@@ -30,7 +30,6 @@ def generate_summaries_or_translations(
    device: str = DEFAULT_DEVICE,
    fp16=False,
    task="summarization",
-    decoder_start_token_id=None,
    **gen_kwargs,
 ) -> None:
    fout = Path(out_file).open("w", encoding="utf-8")
@@ -38,8 +37,6 @@ def generate_summaries_or_translations(
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
    if fp16:
        model = model.half()
-    if decoder_start_token_id is None:
-        decoder_start_token_id = gen_kwargs.pop("decoder_start_token_id", None)

    tokenizer = AutoTokenizer.from_pretrained(model_name)

@@ -49,14 +46,11 @@ def generate_summaries_or_translations(
    for batch in tqdm(list(chunks(examples, batch_size))):
        if "t5" in model_name:
            batch = [model.config.prefix + text for text in batch]
-        batch = tokenizer(batch, return_tensors="pt", truncation=True, padding="max_length").to(device)
-        input_ids, attention_mask = trim_batch(**batch, pad_token_id=tokenizer.pad_token_id)
-        summaries = model.generate(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            decoder_start_token_id=decoder_start_token_id,
-            **gen_kwargs,
+        batch = tokenizer(batch, max_length=1024, return_tensors="pt", truncation=True, padding="max_length").to(
+            device
        )
+        input_ids, attention_mask = trim_batch(**batch, pad_token_id=tokenizer.pad_token_id)
+        summaries = model.generate(input_ids=input_ids, attention_mask=attention_mask, **gen_kwargs)
        dec = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False)
        for hypothesis in dec:
            fout.write(hypothesis + "\n")
@@ -74,13 +68,6 @@ def run_generate():
    parser.add_argument("--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.")
    parser.add_argument("--task", type=str, default="summarization", help="typically translation or summarization")
    parser.add_argument("--bs", type=int, default=8, required=False, help="batch size")
-    parser.add_argument(
-        "--decoder_start_token_id",
-        type=int,
-        default=None,
-        required=False,
-        help="decoder_start_token_id (otherwise will look at config)",
-    )
    parser.add_argument(
        "--n_obs", type=int, default=-1, required=False, help="How many observations. Defaults to all."
    )
@@ -89,7 +76,7 @@ def run_generate():
    examples = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path).readlines()]
    if args.n_obs > 0:
        examples = examples[: args.n_obs]
-    Path(args.save_path).parent.mkdir(exist_ok=True)
+
    generate_summaries_or_translations(
        examples,
        args.save_path,
@@ -98,7 +85,6 @@ def run_generate():
        device=args.device,
        fp16=args.fp16,
        task=args.task,
-        decoder_start_token_id=args.decoder_start_token_id,
    )
    if args.reference_path is None:
        return
@@ -107,7 +93,6 @@ def run_generate():
    output_lns = [x.rstrip() for x in open(args.save_path).readlines()]
    reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()][: len(output_lns)]
    scores: dict = score_fn(output_lns, reference_lns)
-    print(scores)
    if args.score_path is not None:
        json.dump(scores, open(args.score_path, "w+"))
    return scores
@@ -1,108 +0,0 @@
-import argparse
-import os
-import sys
-import tempfile
-from pathlib import Path
-from unittest.mock import patch
-
-import pytest
-import pytorch_lightning as pl
-import timeout_decorator
-import torch
-
-from transformers import BartForConditionalGeneration
-from transformers.testing_utils import slow
-
-from .finetune import SummarizationModule, main
-from .test_seq2seq_examples import CUDA_AVAILABLE, MBART_TINY
-from .utils import load_json
-
-
-MODEL_NAME = MBART_TINY
-# TODO(SS): MODEL_NAME = "sshleifer/student_mbart_en_ro_1_1"
-
-
-@slow
-@pytest.mark.skipif(not CUDA_AVAILABLE, reason="too slow to run on CPU")
-def test_model_download():
-    """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)
-
-
-@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():
-    data_dir = "examples/seq2seq/test_data/wmt_en_ro"
-    env_vars_to_replace = {
-        "$MAX_LEN": 200,
-        "$BS": 4,
-        "$GAS": 1,
-        "$ENRO_DIR": data_dir,
-        "facebook/mbart-large-cc25": MODEL_NAME,
-        # 1 encoder and 1 decoder layer from finetuned mbart en-ro. Should be able to start >0 and improve quickly.
-        # Download is 600MB in previous test.
-        "val_check_interval=0.25": "val_check_interval=1.0",
-    }
-
-    # Clean up bash script
-    bash_script = Path("examples/seq2seq/train_mbart_cc25_enro.sh").open().read().split("finetune.py")[1].strip()
-    bash_script = bash_script.replace("\\\n", "").strip().replace("$@", "")
-    for k, v in env_vars_to_replace.items():
-        bash_script = bash_script.replace(k, str(v))
-    output_dir = tempfile.mkdtemp(prefix="output")
-
-    if CUDA_AVAILABLE:
-        gpus = 1  # torch.cuda.device_count()
-    else:
-        gpus = 0
-    bash_script = bash_script.replace("--fp16", "")
-    testargs = (
-        ["finetune.py"]
-        + bash_script.split()
-        + [
-            f"--output_dir={output_dir}",
-            f"--gpus={gpus}",
-            "--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
-    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 accounts for val_sanity_check
-
-    assert last_step_stats["val_avg_gen_time"] >= 0.01
-
-    assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
-    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
-    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(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
@@ -1,20 +0,0 @@
-UN Chief Says There Is No Military Solution in Syria Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people. The U.N. chief again urged all parties, including the divided U.N. Security Council, to unite and support inclusive negotiations to find a political solution. Ban told a news conference Wednesday that he plans to meet with foreign ministers of the five permanent council nations - the U.S., Russia, China, Britain and France - on the sidelines of the General Assembly's ministerial session later this month to discuss Syria.
-He expressed regret that divisions in the council and among the Syrian people and regional powers "made this situation unsolvable." Ban urged the five permanent members to show the solidarity and unity they did in achieving an Iran nuclear deal in addressing the Syria crisis. 8 Poll Numbers That Show Donald Trump Is For Real Some have tried to label him a flip-flopper. Others have dismissed him as a joke. And some are holding out for an implosion. But no matter how some Republicans are trying to drag Donald Trump down from atop the polls, it hasn't worked (yet).
-Ten of the last 11 national polls have shown Donald Trump's lead at double digits, and some are starting to ask seriously what it means for the real estate mogul's nomination chances. Of course, it's still early in the election cycle. None of this is to say that Trump is likely to win the Republican nomination. Pundits point out that at this time in 2011, Rick Perry's lead was giving way to a rising Herman Cain, neither of whom won even one state in the nomination process. And there are many reasons he would struggle in a general election. But outside groups like Jeb Bush's Super PAC and the economic conservative group Club for Growth are recognizing Trump's staying power and beginning to unload their dollars to topple him.
-Here are some recent poll numbers that suggest that the real estate mogul isn't just a passing phase: Trump's favorability ratings have turned 180 degrees. Right before Donald Trump announced his candidacy in mid-June, a Monmouth University poll showed only two in 10 Republicans had a positive view of the real estate mogul. By mid-July, it was 40 percent. In early August, it was 52 percent. Now, six in 10 Republicans have a favorable view of Donald Trump. Roughly three in 10 say they have a negative view. And these numbers hold up in early states. A Quinnipiac poll in Iowa last week found that 60 percent of Republicans there had a favorable view of Trump.
-Two-thirds of GOP voters would be happy with Trump as the nominee. In a CNN/ORC poll last week, 67 percent of Republicans said they would be either "enthusiastic" or "satisfied" if Trump were the nominee. Only two in 10 say they would be "upset" if he were the nominee. Only Ben Carson generates roughly the same level of enthusiasm as Trump (43 percent say they would be "enthusiastic" vs. 40 percent who say the same of Trump). The next closest in enthusiasm? Marco Rubio with only 21 percent.
-On the flip side, 47 percent of Republican voters say they would be "dissatisfied" or "upset" if establishment favorite Jeb Bush becomes the nominee. A majority of Republicans don't see Trump's temperament as a problem. While Donald Trump has been widely criticized for his bombast and insults, 52 percent of leaned Republican voters nationwide think that the real estate mogul has the right temperament to be president, according to Monday's ABC News/Washington Post poll. The same number holds in the first-in-the-nation caucus state of Iowa, where the same 52 percent of Republicans think he has the personality to be commander in chief, according to Quinnipiac last week.
-Still, 44 percent think he doesn't have the personality to serve effectively, and almost six in 10 independents say his temperament does not belong in the White House, according to ABC/Post. Republican voters are getting used to the idea. When they put on their pundit hats, Republican voters think Trump is for real. When asked who is most likely to win the GOP nomination, four in 10 said Trump was the best bet, according to a CNN/ORC poll out last week. That's a change from when four in 10 placed their money on Jeb Bush in late July. Full disclosure: GOP voters haven't had the clearest crystal ball in the past.
-At this time last cycle, four in 10 Republicans picked Rick Perry to win the nomination, vs. only 28 percent for eventual nominee Mitt Romney. Still, it shows that a plurality of GOP voters see Trump's campaign as plausible. Even if Republicans rallied around another candidate, Trump still beats almost everyone. Some pundits point out that the splintered field is likely contributing to Trump's lead, while anti-Trump support is be spread diffusely among more than a dozen other candidates. But a Monmouth University poll in early September shows that, in a hypothetical head-to-head matchup between Trump and most other Republican candidates, Trump almost always garners majority support.
-He leads Carly Fiorina by 13 points, Marco Rubio by 14 points, Walker by 15 points, Jeb Bush by 19 points, and, finally, Rand Paul, John Kasich and Chris Christie by 33 points each. He's in a dead heat with Ted Cruz. The only candidate who beats him? Ben Carson would lead the businessman by a wide 19 points in a hypothetical head-to-head. A bare majority of Donald Trump's supporters say they've made up their minds. A new CBS/NYT poll out on Tuesday shows that just more than half of voters who support Trump say they have locked in their votes. Obviously, a lot can happen to change that, and no one can really say they would never change their mind.
-46 percent said they are leaving the door open to switching candidates. Still, Trump's strongest competition at the moment is from fellow outsider neurosurgeon Ben Carson, but voters who say they have made up their minds are twice as likely to go for Trump. Six in 10 Republicans say they agree with Trump on immigration. Even since Donald Trump called immigrants from Mexico "rapists" in his campaign announcement speech two months ago, immigration has been front and center in the 2016 conversation. Some are worried that Trump's bombast will drive crucial Hispanic voters away from the Republican Party and damage rebranding efforts.
-But according to Monday's new ABC/Post poll, six in 10 Republicans say they agree with Trump on immigration issues. So as long as immigration remains in the spotlight, it seems Donald Trump will remain too. Frustration with government is climbing to new highs. Donald Trump and Ben Carson now account for roughly half of the support from Republican voters, largely due to their outsider status. Six in 10 Republicans in Monday's new ABC/Post poll say they want a political outsider over someone with government experience. And they are angry at Washington, too.
-A Des Moines Register/Bloomberg poll in Iowa from two weeks ago shows that three in four Iowa Republicans are frustrated with Republicans in Congress, with 54 percent "unsatisfied" and 21 percent "mad as hell." Jeremy Corbyn to make debut at Prime Minister's Questions Since his election, Mr Corbyn's debut at PMQs has been keenly awaited New Labour leader Jeremy Corbyn is to make his debut at Prime Minister's Questions later, taking on David Cameron for the first time.
-Mr Corbyn will rise to ask the first of his six allotted questions shortly after midday, with his performance likely to be closely scrutinised by the media and Labour MPs. He has called for "less theatre and more facts" at the weekly showpiece. He has also said he could skip some sessions, leaving them to colleagues. The encounter will be the first parliamentary test of Mr Corbyn's leadership, coming after his appointment of a shadow cabinet and his speech to the TUC annual congress on Tuesday.
-Meanwhile, the Labour leader's decision to stand in silence during the singing of the national anthem at a service on Tuesday to mark the 75th anniversary of the Battle of Britain has attracted criticism from a number of Tory MPs and is the focus of several front page stories in the newspapers. Mr Corbyn's decision not to sing the national anthem has attracted attention A spokesman for Mr Corbyn said he had "stood in respectful silence" and did recognise the "heroism of the Royal Air Force in the Battle of Britain."
-But a member of Mr Corbyn's shadow cabinet, Owen Smith, told BBC Two's Newsnight programme he would have advised the Labour leader to sing the national anthem "irrespective" of his belief that the monarchy should be abolished. Nearly a dozen shadow ministers have refused to serve in Mr Corbyn's top team, citing differences over the economy, defence and foreign affairs, while less than a sixth of the parliamentary party originally backed him as leader. BBC political correspondent Robin Brant says policy differences are also "stacking up" within Labour following Mr Corbyn's appointment over its position on the European Union and the government's cap on benefits.
-Mr Corbyn told the TUC conference Labour was putting forward amendments to remove the whole idea of a cap altogether. Hours later Mr Smith, the shadow work and pensions secretary, said the party was "very clear" that it was only opposing government plans to reduce the level of cap from £26,000 to £23,000. Mr Corbyn will be the fifth Labour leader that David Cameron has faced across the despatch box over the past decade since he became Tory leader. The Labour leader, who has promised a different approach to politics, says he has "crowd sourced" ideas for questions to ask Mr Cameron and has been given more than 30,000 suggestions.
-The Islington North MP has said PMQs is too confrontational and that he will refrain from both "repartee" and trading barbs, instead vowing to focus on serious issues such as poverty, inequality and the challenges facing young people. Mr Corbyn has said that Angela Eagle, the shadow business secretary, will deputise for him at PMQs when he does not attend - for instance when Mr Cameron is travelling abroad. He has also floated the idea of allowing other colleagues to take the floor on occasion, saying he had approached the Commons Speaker John Bercow to discuss the issue.
-When he became leader in 2005, Mr Cameron said he wanted to move away from the "Punch and Judy" style of politics often associated with PMQs but admitted some years later that he had failed. Since it was first televised in 1990, PMQs has been seen as a key barometer of a leader's judgement, their command of the Commons and their standing among their fellow MPs although critics have argued it has become a caricature and is in need of far-reaching reforms. 'Shot in Joburg': Homeless youth trained as photographers Downtown Johannesburg is a tough place to be homeless.
-But one group of former street children have found a way to learn a skill and make a living. "I was shot in Joburg" is a non-profit studio that teaches homeless youngsters how to take photographs of their neighbourhood and make a profit from it. BBC News went to meet one of the project's first graduates. JD Sports boss says higher wages could hurt expansion JD Sports Executive Chairman Peter Cowgill says a higher minimum wage for UK workers could mean "more spending power in the pockets of potential consumers." But that spending power is unlikely to outweigh the higher labour costs at his firm, he says.
-The costs could hit JD Sports' expansion plans, he added, which could mean fewer extra jobs. Thanasi Kokkinakis backed by Tennis Australia president Steve Healy Thanasi Kokkinakis deserves kudos rather than criticism for his behaviour. Thanasi Kokkinakis has been the collateral damage in the recent storm around his friend Nick Kyrgios and deserves kudos rather than criticism for his own behaviour, according to Tennis Australia president Steve Healy.
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-Șeful ONU declară că nu există soluții militare în Siria Secretarul General Ban Ki-moon afirmă că răspunsul său la suportul militar al Rusiei pentru Siria este că „nu există o soluție militară” la conflictul care durează de aproape cinci ani iar mai multe arme nu ar face decât să agraveze violența și suferința a milioane de oameni. Șeful ONU a solicitat din nou tuturor părților, inclusiv Consiliului de securitate ONU divizat să se unifice și să susțină negocierile pentru a găsi o soluție politică. Ban a declarat miercuri în cadrul unei conferințe că intenționează să se întâlnească luna aceasta cu miniștrii de externe din cinci țări permanent prezente în consiliu - SUA, Rusia, China, Anglia și Franța - pe marginea sesiunii ministeriale a Adunării Generale pentru a discuta despre Siria.
-Ban și-a exprimat regretul că divizările în consiliu și între poporul sirian și puterile regionale „au făcut această situație de nerezolvat”. Ban le-a cerut celor cinci membri permanenți să dea dovadă de solidaritatea și unitatea arătate atunci când au reușit să încheie un acord referitor la armele nucleare ale Iranului, abordând astfel criza din Siria. 8 cifre din sondaje care arată că Donald Trump are șanse reale Unii au încercat să îl eticheteze ca politician „flip-flop”. Alții l-au numit o glumă. Iar alții așteaptă implozia. Însă indiferent de modul în care unii republicani încearcă să îl dărâme pe Donald Trump din vârful sondajelor, nu a funcționat (încă).
-Zece din ultimele 11 sondaje naționale au arătat că Donald Trump conduce cu un procent din două cifre iar unele voci încep să se întrebe serios ce înseamnă acest lucru pentru șansele de numire ale mogulului imobiliar. Desigur, este încă prematur. Nimic din toate acestea nu spune că Trump va câștiga cursa pentru nominalizarea republicanilor. Pundits arată că, în aceeași perioadă a anului 2011, avansul lui Rick Perry îi făcea loc lui Herman Cain în sondaje, dar niciunul dintre ei nu a câștigat în vreun stat în cursa de nominalizare. Iar motivele pentru care s-ar lupta din greu la alegerile generale sunt numeroase. Însă grupurile din exterior precum Super PAC al lui Jeb Bush și grupul conservator economic Club for Growth admit puterea lui Trump și încep să îl susțină cu bani.
-În continuare vă prezentăm câteva cifre din sondaje recente care sugerează că mogulul imobiliar nu este doar ceva trecător: Cifrele care indică susținerea față de Trump s-au întors la 180 grade. Chiar înainte ca Donald Trump să își anunțe candidatura, la mijlocul lui iunie, un sondaj realizat de Universitatea din Monmouth arăta că doar doi din 10 republicani aveau o părere pozitivă despre mogulul imobiliar. Până la mijlocul lui iulie, procentul a urcat la 40%. La începutul lui august, era 52%. În prezent, șase din 10 republicani au o părere favorabilă despre Donald Trump. Aproximativ trei din 10 declară că au o părere negativă. Aceste cifre se mențin. Un sondaj realizat săptămâna trecută de Quinnipiac în Iowa a concluzionat că 60% dintre republicanii din regiune au o părere favorabilă despre Trump.
-Două treimi dintre alegătorii GOP ar fi fericiți dacă Trump ar câștiga cursa pentru nominalizare. Într-un sondaj realizat săptămâna trecută de CNN/ORC, 67% dintre republicani au declarat că ar fi „entuziasmați” sau „mulțumiți” dacă Trump ar câștiga cursa pentru nominalizare. Doar doi din 10 declară că ar fi „supărați” dacă Trump ar câștiga cursa pentru nominalizare. Doar Ben Carson generează aproximativ același nivel de entuziasm ca Trump (43% declară că ar fi „entuziasmați” față de 40% care declară același lucru despre Trump). Cel mai aproape în ceea ce privește entuziasmul? Marco Rubio, cu doar 21%.
-De partea cealaltă, 47% dintre alegătorii republicani afirmă că ar fi „nemulțumiți” sau „supărați” dacă favoritul Jeb Bush câștigă cursa pentru nominalizare. Majoritatea republicanilor nu consideră temperamentul lui Trump o problemă. Deși Donald Trump a fost puternic criticat pentru insultele aduse și stilul său bombastic, 52% dintre alegătorii republicani la nivel național consideră că mogulul imobiliar are temperamentul potrivit pentru a fi președinte, conform sondajului realizat luni de ABC News/Washington Post. Regăsim aceleași cifre în statul Iowa, unde tot 52% dintre republicani cred că Trump are personalitatea potrivită pentru a fi conducător, conform sondajului realizat săptămâna trecută de Quinnipiac.
-Totuși, 44% sunt de părere că nu are personalitatea necesară pentru a acționa eficient și aproape șase din 10 independenți afirmă că temperamentul său nu are ce căuta la Casa Albă, conform ABC/Post. Alegătorii republicani se obișnuiesc cu ideea. Atunci când iau atitudinea de intelectuali, alegătorii republicani consideră că Trump este autentic. Conform unui sondaj realizat săptămâna trecută de CNN/ORC, la întrebarea cine are cele mai multe șanse să câștige cursa pentru nominalizare GOP, patru din 10 au declarat că Trump. Situația s-a schimbat față de finalul lui iulie, când patru din 10 ar fi pariat pe Jeb Bush. Informare completă: în trecut, alegătorii GOP nu au citit foarte bine viitorul.
-În aceeași perioadă a ultimelor alegeri, patru din 10 republicani l-au ales pe Rick Perry în cursa pentru nominalizare, față de doar 28% pentru Mitt Romney. Însă, aceste cifre arată că majoritatea alegătorilor GOP consideră plauzibilă campania lui Trump. Chiar dacă republicanii sau repliat spre un alt candidat. Trump încă se află în fruntea tuturor. Unele voci spun că situația divizată va contribui probabil la victoria lui Trump, în timp ce susținerea contra lui Trump se va împărți la mai mult de doisprezece candidați. Însă un sondaj derulat la începutul lui septembrie de Universitatea din Monmouth arată că, în situația ipotetică a unei colaborări între Trump și majoritatea celorlalți candidați republicani, aproape întotdeauna Trump va beneficia de susținerea majoritară.
-Trump se află la distanță de 13 puncte de Carly Fiorina, la 14 puncte de Marco Rubio, la 15 puncte de Walker, la 19 puncte de Jeb Bush și, în cele din urmă, la câte 33 de puncte față de Rand Paul, John Kasich și Chris Christie. Este aproape la egalitate cu Ted Cruz. Singurul candidat care îl învinge? Ben Carson l-ar învinge pe omul de afaceri cu 19 puncte într-o confruntare ipotetică de unu la unu. Majoritatea susținătorilor lui Donald Trump declară că s-au decis. Un nou sondaj realizat marți de CBS/NYT arată că peste jumătate dintre alegătorii care îl susțin pe Trump declară că nu își schimbă opțiunea de vot. Evident, se pot întâmpla multe în acest sens și nimeni nu poate spune că aceștia nu se vor răzgândi niciodată.
-46% afirmă că lasă portița deschisă posibilității de a-și schimba opțiunea. Cu toate acestea, cel mai important adversar al lui Trump este în prezent neurochirurgul Ben Carson, însă este de două ori mai probabil ca alegătorii care declară că s-au decis să voteze cu Trump. Șase din 10 republicani afirmă că sunt de acord cu Trump în problema imigrării. De când Donald Trump i-a numit pe imigranții din Mexic „violatori” în discursul de deschidere a campaniei sale, în urmă cu două luni, imigrarea a fost subiectul central în campania pentru 2016. Unii sunt îngrijorați că stilul bombastic al lui Trump va duce la o scindare între alegătorii hispanici importanți și Partidul Republican și va prejudicia eforturile de rebranding.
-Însă, conform sondajului realizat luni de ABC/Post, șase din 10 republicani afirmă că sunt de acord cu Trump în problema imigrării. Așa că, se pare că atâta timp cât problema imigrării rămâne în lumina reflectoarelor, la fel va rămâne și Doland Trump. Frustrarea față de autorități atinge noi culmi. Donald Trump și Ben Carson sunt acum susținuți de aproape jumătate dintre alegătorii republicani, în mare parte datorită statutului lor de outsideri. Conform sondajului realizat luni de ABC/Post, șase din 10 republicani afirmă că preferă un outsider politic în detrimentul cuiva cu experiență în guvernare. Oamenii sunt de asemenea supărați pe autoritățile de la Washington.
-Un sondaj derulat în urmă cu două săptămâni în Iowa de către Des Moines Register/Bloomberg arată că trei din patru republicani din Iowa sunt frustrați de prestația republicanilor din COngres, 54% declarându-se „nemulțumiți” iar 21% „nervoși la culme”. Jeremy Corbyn își face debutul la Prime Minister's Questions Încă de la alegerea sa, debutul domnului Corbyn la PMQs a fost îndelung așteptat Noul lider al Partidului Laburist, Jeremy Corbyn, își va face mai târziu debutul la Prime Minister's Questions, confruntându-se pentru prima dată cu David Cameron.
-Dl Corbyn va adresa primele dintre cele șase întrebări la care are dreptul la scurt timp după prânz; prestația sa va fi probabil analizată îndeaproape de mass-media și parlamentarii laburiști. În cadrul aparițiilor săptămânale, el a cerut „mai puțin teatru și mai multe fapte”. A declarat de asemenea că poate renunța la câteva participări și că le cedează colegilor săi. Confruntarea va fi primul test parlamentar al Dl Corbyn în poziție de lider, venind după ce a numit un „cabinet fantomă” și după discursul pe care l-a ținut marți la congresul anual TUC.
-Între timp, decizia liderului Partidului laburist de a păstra tăcerea la rostirea imnului național în cadrul unei slujbe ținute marți cu ocazia aniversării a 75 de ani de la Bătălia Angliei a atras critici din partea unor parlamentari conservatori și a ținut prima pagină a ziarelor. Decizia domnului Corbyn de a nu cânta imnul național a atras atenția Un purtător de cuvânt al Dl Corbyn a declarat că acesta „a păstrat tăcerea în mod respectuos” și a recunoscut „eroismul Forțelor aeriene britanice în Bătălia Angliei.”
-Însă un membru al cabinetului fantomă al Dl Corbyn, Owen Smith, a declarat pentru emisiunea Two's Newsnight transmisă de BBC că i-ar fi recomandat liderului laburist să cânte imnul național „indiferent” de credința sa că monarhia ar trebui abolită. În jur de doisprezece miniștri din cabinetul fantomă au refuzat să facă parte din echipa de frunte a Dl Corbyn, argumentând prin diferențe de opinie legate de economie, apărare și externe, în timp ce mai puțin de o șesime din partidul parlamentar l-a susținut ca lider. Corespondentul politic al BBC, Robin Brant, declară că diferențele de politică „se cumulează” în Partidul Laburist după numirea domnului Corbyn referitor la poziția sa față de Uniunea Europeană și limita de beneficii.
-Dl Corbyn a declarat la conferința TUC că Partidul Laburist va aduce modificări prin care se va elimina integral ideea limitării. Câteva ore mai târziu, Dl Smith, Ministrul Muncii și Pensiilor, a declarat că partidul „este foarte clar” în opoziția exclusivă față de planurile guvernului de a reduce nivelul „cap” de la 26.000 lire la 23.000 lire. Dl Corbyn va fi al cincilea lider laburist cu care se confruntă David Cameron la tribună în ultimul deceniu, de când a preluat conducerea Partidului Conservator. Liderul laburist, care a promis o abordare diferită a politicii, spune că are idei „din surse externe” pentru întrebări pe care să i le adreseze Domnului Cameron și că a primit peste 30.000 de sugestii.
-Parlamentarul Islington North a afirmat că PMQs implică un nivel de confruntare prea înalt și că se va abține de la replici și atacuri, angajându-se să se concentreze în schimb pe probleme serioase precum sărăcia, inegalitatea și provocările cu care se confruntă tinerii. Dl Corbyn a declarat că Angela Eagle, Ministrul de finanțe, îi va ține locul la PMQs atunci când el nu poate participa - de exemplu atunci când Dl Cameron se deplasează în străinătate. A exprimat de asemenea ideea că va permite altor colegi să ia cuvântul ocazional, spunând că l-a abordat pe Președintele Camerei Deputaților, John Bercow, pentru a discuta acest aspect.
-În 2005, când a preluat conducerea, Dl Cameron a declarat că dorește să renunțe la stilul politic „Punch and Judy” asociat adesea cu PMQs însă a recunoscut câțiva ani mai târziu că nu a reușit în demersul său. De la prima transmisie, în 1990, PMQs a fost considerată un barometru cheie al raționamentului unui lider, al modului în care acesta conduce Camera Deputaților și a poziției sale în rândul colegilor parlamentari, deși criticii afirmă a ca devenit o caricatură și că are nevoie de o reformare profundă. „Cadru în Joburg”: Tineri fără adăpost beneficiază de cursuri de fotografie Este dificil să fii un om fără adăpost în Johannesburg.
-Însă un grup de oameni care au trăit pe străzi în copilărie au găsit un mod de a învăța o meserie și de a-și câștiga traiul. „I was shot în Joburg” este un studio non-profit care îi învață pe tinerii fără adăpost să facă fotografii ale zonelor în care trăiesc și să câștige bani din asta. BBC News s-a întâlnit cu unul dintre primii absolvenți ai proiectului. Șeful JD Sports spune că salariile mai mari ar putea dăuna extinderii Președintele JD Sports, Peter Cowgill, declară că o creștere a salariului minim în Marea Britanie ar putea însemna „o putere de cumpărare mai mare în buzunarele potențialilor consumatori.” Este însă puțin probabil ca respectiva putere de cumpărare să depășească costurile mai mari pentru forța de muncă în cadrul firmei, afirmă el.
-Costurile ar putea avea impact asupra planurilor de extindere ale JD Sports, a adăugat el, ceea ce ar putea însemna mai puține locuri de muncă noi. Thanasi Kokkinakis susținut de președintele Tennis Australia, Steve Healy Thanasi Kokkinakis ar merita să fie lăudat și nu criticat pentru comportamentul său. Thanasi Kokkinakis a fost victimă colaterală în „furtuna” creată în jurul prietenului său, Nick Kyrgios, iar comportamentul său merită mai degrabă cuvinte de laudă și nu critică, în opinia președintelui Tennis Australia, Steve Healy.
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-Membership of Parliament: see Minutes Approval of Minutes of previous sitting: see Minutes Membership of Parliament: see Minutes Verification of credentials: see Minutes Documents received: see Minutes Written statements and oral questions (tabling): see Minutes Petitions: see Minutes Texts of agreements forwarded by the Council: see Minutes Action taken on Parliament's resolutions: see Minutes Agenda for next sitting: see Minutes Closure of sitting (The sitting was closed at 7.45 p.m.)
-Election of Vice-Presidents of the European Parliament (deadline for submitting nominations): see Minutes (The sitting was suspended at 12.40 p.m. and resumed at 3.00 p.m.) Election of Quaestors of the European Parliament (deadline for submitting nominations): see Minutes (The sitting was suspended at 3.25 p.m. and resumed at 6.00 p.m.) Agenda for next sitting: see Minutes Closure of sitting (The sitting was closed at 6.15 p.m.) Opening of the sitting (The sitting was opened at 9.35 a.m.) Documents received: see Minutes Approval of Minutes of previous sitting: see Minutes Membership of Parliament: see Minutes
-Membership of committees (deadline for tabling amendments): see Minutes (The sitting was suspended at 7 p.m. and resumed at 9 p.m.) Agenda for next sitting: see Minutes Closure of sitting (The sitting was suspended at 23.25 p.m.) Documents received: see Minutes Communication of Council common positions: see Minutes (The sitting was suspended at 11.35 a.m. and resumed for voting time at noon) Approval of Minutes of previous sitting: see Minutes Committee of Inquiry into the crisis of the Equitable Life Assurance Society (extension of mandate): see Minutes
-Announcement by the President: see Minutes 1. Membership of committees (vote) 2. Amendment of the ACP-EC Partnership Agreement (vote) 4. Certification of train drivers operating locomotives and trains on the railway system in the Community (vote) 6. Law applicable to non-contractual obligations ("ROME II") (vote) 8. Seventh and eighth annual reports on arms exports (vote) Corrections to votes and voting intentions: see Minutes Membership of committees and delegations: see Minutes Request for waiver of parliamentary immunity: see Minutes Decisions concerning certain documents: see Minutes
-Written statements for entry in the register (Rule 116): see Minutes Forwarding of texts adopted during the sitting: see Minutes Dates for next sittings: see Minutes Adjournment of the session I declare the session of the European Parliament adjourned. (The sitting was closed at 1 p.m.) Approval of Minutes of previous sitting: see Minutes Membership of Parliament: see Minutes Request for the defence of parliamentary immunity: see Minutes Appointments to committees (proposal by the Conference of Presidents): see Minutes Documents received: see Minutes Texts of agreements forwarded by the Council: see Minutes
-Action taken on Parliament's resolutions: see Minutes Oral questions and written statements (tabling): see Minutes Written statements (Rule 116): see Minutes Agenda: see Minutes 1. Appointments to parliamentary committees (vote): see Minutes Voting time Agenda for next sitting: see Minutes Closure of sitting (The sitting was closed at 12 midnight) Opening of the sitting (The sitting was opened at 09.05) Documents received: see Minutes Approval of Minutes of previous sitting: see Minutes 1. Protection of passengers against displaced luggage (vote) 2.
-Approval of motor vehicles with regard to the forward field of vision of the driver (vote) 3. EC-Korea Agreement on scientific and technological cooperation (vote) 4. Mainstreaming sustainability in development cooperation policies (vote) 5. Draft Amending Budget No 1/2007 (vote) 7. EC-Gabon Fisheries Partnership (vote) 10. Limitation periods in cross-border disputes involving personal injuries and fatal accidents (vote) 12. Strategy for a strengthened partnership with the Pacific Islands (vote) 13. The European private company statute (vote) That concludes the vote.
-Corrections to votes and voting intentions: see Minutes Assignment conferred on a Member: see Minutes Membership of committees and delegations: see Minutes Decisions concerning certain documents: see Minutes Forwarding of texts adopted during the sitting: see Minutes Dates for next sittings: see Minutes
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-Componenţa Parlamentului: a se vedea procesul-verbal Aprobarea procesului-verbal al şedinţei precedente: a se vedea procesul-verbal Componenţa Parlamentului: a se vedea procesul-verbal Verificarea prerogativelor: a se vedea procesul-verbal Depunere de documente: a se vedea procesul-verbal Declaraţii scrise şi întrebări orale (depunere): consultaţi procesul-verbal Petiţii: a se vedea procesul-verbal Transmiterea de către Consiliu a textelor acordurilor: a se vedea procesul-verbal Cursul dat rezoluţiilor Parlamentului: a se vedea procesul-verbal Ordinea de zi a următoarei şedinţe: a se vedea procesul-verbal Ridicarea şedinţei (Se levanta la sesión a las 19.45 horas)
-Alegerea vicepreşedinţilor Parlamentului European (termenul de depunere a candidaturilor): consultaţi procesul-verbal (Die Sitzung wird um 12.40 Uhr unterbrochen und um 15.00 Uhr wiederaufgenommen). Alegerea chestorilor Parlamentului European (termenul de depunere a candidaturilor): consultaţi procesul-verbal (Die Sitzung wird um 15.25 Uhr unterbrochen und um 18.00 Uhr wiederaufgenommen). Ordinea de zi a următoarei şedinţe: a se vedea procesul-verbal Ridicarea şedinţei (Die Sitzung wird um 18.15 Uhr geschlossen.) Deschiderea şedinţei (Die Sitzung wird um 9.35 Uhr eröffnet.) Depunerea documentelor: a se vedea procesul-verbal Aprobarea procesului-verbal al şedinţei precedente: a se vedea procesul-verbal Componenţa Parlamentului: a se vedea procesul-verbal
-Componenţa comisiilor (termenul de depunere a amendamentelor): consultaţi procesul-verbal (La seduta, sospesa alle 19.00, è ripresa alle 21.00) Ordinea de zi a următoarei şedinţe: a se vedea procesul-verbal Ridicarea şedinţei (Die Sitzung wird um 23.25 Uhr geschlossen.) Depunerea documentelor: a se vedea procesul-verbal Comunicarea poziţiilor comune ale Parlamentului: a se vedea procesul-verbal (La séance, suspendue à 11h35 dans l'attente de l'Heure des votes, est reprise à midi) Aprobarea procesului-verbal al şedinţei precedente: a se vedea procesul-verbal Comisia de anchetă privind criza societăţii de asigurări "Equitable Life” (prelungirea mandatului): consultaţi procesul-verbal
-Comunicarea Preşedintelui: consultaţi procesul-verbal 1. Componenţa comisiilor (vot) 2. Modificarea Acordului de parteneriat ACP-CE ("Acordul de la Cotonou”) (vot) 4. Certificarea mecanicilor de locomotivă care conduc locomotive şi trenuri în sistemul feroviar comunitar (vot) 6. Legea aplicabilă obligaţiilor necontractuale ("Roma II”) (vot) 8. Al şaptelea şi al optulea raport anual privind exportul de armament (vot) Corectările voturilor şi intenţiile de vot: a se vedea procesul-verbal Componenţa comisiilor şi a delegaţiilor: a se vedea procesul-verbal Cerere de ridicare a imunităţii parlamentare: consultaţi procesul-verbal Decizii privind anumite documente: a se vedea procesul-verbal
-Declaraţii scrise înscrise în registru (articolul 116 din Regulamentul de procedură): a se vedea procesul-verbal Transmiterea textelor adoptate în cursul prezentei şedinţe: a se vedea procesul-verbal Calendarul următoarelor şedinţe: a se vedea procesul-verbal Întreruperea sesiunii Dichiaro interrotta la sessione del Parlamento europeo. (La seduta è tolta alle 13.00) Aprobarea procesului-verbal al şedinţei precedente: a se vedea procesul-verbal Componenţa Parlamentului: a se vedea procesul-verbal Cerere de apărare a imunităţii parlamentare: consultaţi procesul-verbal Numiri în comisii (propunerea Conferinţei preşedinţilor): consultaţi procesul-verbal Depunerea documentelor: a se vedea procesul-verbal Transmiterea de către Consiliu a textelor acordurilor: a se vedea procesul-verbal
-Continuări ale rezoluţiilor Parlamentului: consultaţi procesul-verbal Declaraţii scrise şi întrebări orale (depunere): consultaţi procesul-verbal Declaraţii scrise (articolul 116 din Regulamentul de procedură) Ordinea de zi: a se vedea procesul-verbal 1. Numiri în comisiile parlamentare (vot): consultaţi procesul-verbal Timpul afectat votului Ordinea de zi a următoarei şedinţe: a se vedea procesul-verbal Ridicarea şedinţei (La seduta è tolta alle 24.00) Deschiderea şedinţei (The sitting was opened at 09.05) Depunerea documentelor: a se vedea procesul-verbal Aprobarea procesului-verbal al şedinţei precedente: a se vedea procesul-verbal 1. Protecţia pasagerilor împotriva deplasării bagajelor (vot) 2.
-Omologarea vehiculelor cu motor cu privire la câmpul de vizibilitate înainte al conducătorului auto (vot) 3. Acordul CE-Coreea de cooperare ştiinţifică şi tehnologică (vot) 4. Integrarea durabilităţii în politicile de cooperare pentru dezvoltare (vot) 5. Proiect de buget rectificativ nr.1/2007 (vot) 7. Acordul de parteneriat în domeniul pescuitului între Comunitatea Europeană şi Republica Gaboneză (vot) 10. Termenele de prescripţie aplicabile în cadrul litigiilor transfrontaliere cu privire la vătămările corporale şi accidentele mortale (vot) 12. Relaţiile UE cu insulele din Pacific: Strategie pentru un parteneriat consolidat (vot) 13. Statutul societăţii private europene (vot) Damit ist die Abstimmungsstunde beendet.
-Corectările voturilor şi intenţiile de vot: a se vedea procesul-verbal Misiune încredinţată unui deputat: consultaţi procesul-verbal Componenţa comisiilor şi a delegaţiilor: a se vedea procesul-verbal Decizii privind anumite documente: a se vedea procesul-verbal Transmiterea textelor adoptate în cursul prezentei şedinţe: a se vedea procesul-verbal Calendarul următoarelor şedinţe: a se vedea procesul-verbal
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-Brazil's Former Presidential Chief-of-Staff to Stand Trial A federal judge on Tuesday accepted the charges filed against Brazil's former presidential chief of staff for his alleged involvement in a massive corruption scheme at state-owned oil company Petrobras. The federal prosecutor's office said Jose Dirceu will face trial on the corruption, racketeering and money laundering charges filed earlier this month. Fourteen other people will also be tried, including Joao Vaccari Neto, the former treasurer of Brazil's governing Workers' Party and Renato de Souza Duque, Petrobras' former head of corporate services.
-Dirceu is the most senior member of the ruling Workers' Party to be taken into custody in connection with the scheme. Dirceu served as former President Luiz Inacio Lula da Silva's chief of staff between 2003 and 2005. He was arrested early August in his home, where he already was under house arrest serving an 11-year sentence for his involvement in a cash-for-votes scheme in Congress more than 10 years ago. Prosecutors have said that Dirceu masterminded the kickback scheme at Petrobras, accepted bribes while in office and continued to receive payments from contractors after he was jailed in late 2013 for the vote-buying scandal.
-According to prosecutors, the scheme at Petrobras involved roughly $2 billion in bribes and other illegal funds. Some of that money was allegedly funneled back to campaign coffers of the ruling party and its allies. It also allegedly included the payment of bribes to Petrobras executives in return for inflated contracts. 'Miraculous' recovery for Peshawar massacre schoolboy A teenager paralysed after being shot four times in Pakistan's deadliest terror attack has made a "miraculous" recovery following treatment in the UK. Muhammad Ibrahim Khan, 13, had been told by doctors in Pakistan that he would never walk again.
-At least 140 people, mostly children, were killed when gunmen stormed Peshawar's Army Public School last December. Muhammad, who arrived in London last month for surgery, is being discharged from hospital later. Exactly nine months ago, on an ordinary Tuesday morning, Muhammad sat in his first aid class listening to his teachers intently. At the same time seven gunmen disguised in security uniforms were entering the Army Public School. They were strapped with explosives and had one simple mission in mind: Kill every man, woman and child they came across. "I can't forget what happened that day," Muhammad says with a severe stare.
-We were sitting in the auditorium, we were asking questions... and then we heard heavy gunfire outside. The terrorists moved inside and they started killing - our teacher was burned alive. Muhammad described pulling four other pupils out of the auditorium as the carnage unfolded. He said he then heard his friend, Hamza calling to him. He said, 'oh brother save me'. I held his hand. That's when I was shot in the back, and he was shot in the head. Most of the people killed in the attack were pupils Hamza died in Muhammad's arms. Muhammad recalled blacking out after that, and the next thing he knew he was in a hospital bed, paralysed from the waist down.
-Doctors in Peshawar in northern Pakistan, and then Rawalpindi, close to the capital, told his family there was no treatment, and he would never walk again. "Seeing him I felt like my soul had left my body," says Muhammad's father, Sher Khan Those nine months were the hardest in my life. But Mr Khan and his wife, Sherbano, refused to believe that their cricket-mad son would never be able to use his legs again. They campaigned, and appealed for help on Pakistani TV, gaining the support of high profile people such as cricketer turned politician Imran Khan.
-Finally, they were able to raise the funds to bring Muhammad to the UK and provide him with treatment at London's private Harley Street Clinic. Consultant neurosurgeon Irfan Malik described Muhammad as "terrified" when he first arrived at the hospital. "He'd spent the last [few] months lying on a bed, unable to move side to side," says Mr Malik. He was weak, he had a pressure sore on his back. He wasn't in great shape. A vertebra at the base of Muhammad's spine was destroyed Muhammad was shot in his shoulder, his hip, and his back during the attack, damaging his lower spine - leading to paralysis.
-But during six hours of surgery, Mr Malik and his team were able to reattach nerve endings and reconstruct the damaged part of the spine. Even Mr Malik was surprised at what happened next. Exactly one week after the surgery Muhammad stood up and started taking steps and walking. We were not expecting to get that sort of excellent result. That was miraculous," he says. Less than two weeks after his operation, Muhammad is ready to leave hospital and start the long road to recovery. Muhammad has defied the odds and started to walk again He says he wants to build his strength and continue his education in the UK. But he says he is determined to return to Pakistan, join the army and help fight terrorism.
-"I feel like I have a second chance at life," he says as he shows off pictures he's drawn of guns scribbled out next to school books and pens Muhammad grows physically stronger every day but the psychological trauma he continues to endure is unimaginable. "My anger is not diminishing" he says. In my school little kids were killed. What was their crime? His mother, wiping a tear from her eye, caressed his head and said: "I can see my son walking again." He'll be able to get on with his normal life. 'Super Voice' 4G service from Three offers better signal Three is making use of a lower frequency 4G spectrum that can travel more widely
-Mobile phone provider Three has launched a UK service it says will improve reception inside buildings and in rural black spots. Its 4G Super Voice enables customers to make calls and send texts using a lower frequency spectrum. Other networks are looking into introducing the technology, known as Voice Over Long-Term Evolution (VoLTE). It currently works on only the Samsung Galaxy S5, but recent iPhone handsets will be added in the coming months. Three said up to 5.5 million customers would have access to the service by 2017.
-Chief technology officer Bryn Jones said: "By the end of the year, one million of our customers will have access to better indoor coverage and be able to use their phones in more places than ever before." Stars prepare for panto season Pantomime season is big business for theatres up and down the UK, with many getting ready for this year's season now. Some of the biggest names in showbusiness now take part in the yuletide theatre. Matthew Kelly and Hayley Mills will be appearing in Cinderella - one as an ugly sister, the other as fairy godmother. They reveal their panto secrets to BBC Breakfast. Steven Wilson: 'If I don't do anything, I feel this creeping guilt'
-Steven Wilson was recently the big winner at the Progressive Music Awards Steven Wilson is often dubbed the hardest working musician in the world of progressive rock. The multi-talented musician won three prizes at this month's Progressive Music Awards in London, including album of the year for Hand. The Guardian's five-star review called it "a smart, soulful and immersive work of art." Since the 1980s, Wilson has been the driving force in a number of musical projects, the best known of which is the rock band Porcupine Tree. Now, ahead of two sell-out shows at the Royal Albert Hall, Wilson is releasing a vinyl-only double LP, Transience, to showcase the "more accessible" side of his solo output.
-He tells the BBC about his love of vinyl, his busy schedule and explains how comic actor Matt Berry came to be his support act. What does vinyl mean to you? I grew up at the very tail end of the vinyl era, and at the time, I remember, we couldn't wait for CD to come along because vinyl was so frustrating. You would buy the record, take it home, and it would have a scratch, and you would have to take it back again. I love CDs, and for some kinds of music - classical for example - it is better than vinyl. But the problem with the CD and digital downloads is that there's nothing you can really cherish or treasure. Owning vinyl is like having a beautiful painting hanging in your living room.
-It's something you can hold, pore over the lyrics and immerse yourself in the art work. I thought it was just a nostalgic thing, but it can't be if kids too young to remember vinyl are enjoying that kind of experience. Do you have a piece of vinyl that you treasure? The truth is I got rid of 100% of my vinyl in the 90s. All the vinyl I have is re-bought. I started off from the perspective that I wanted to recreate the collection I had when I was 15, but it's gone beyond that. The first record which I persuaded my parents to buy for me was Electric Light Orchestra's Out of the Blue.
-If I still had my original copy, it would have sentimental value, but, alas, it's in a charity shop somewhere. Steven Wilson hopes the album will be a doorway for potential new fans Why release your new compilation Transience on vinyl? It was originally conceived as an idea for Record Store Day, but we missed the boat on that. My record company had suggested I put together some of my shorter, more accessible songs. I got a bit obsessed by the idea to make something like "an introduction to Steven Wilson," and I was committed to it being a vinyl-only release. Anyone who buys the vinyl does also get a high-resolution download.
-Do you have a concern that the album won't show your work in a true light?
@@ -1,16 +0,0 @@
-Fostul șef al cabinetului prezidențial brazilian este adus în fața instanței Marți, un judecător federal a acceptat acuzațiile aduse împotriva fostului șef al cabinetului prezidențial brazilian pentru presupusa implicare a acestuia într-o schemă masivă de corupție privind compania petrolieră de stat Petrobras. Biroul procurorului federal a declarat că Jose Dirceu va fi trimis în judecată pentru acuzațiile de corupție, înșelătorie și spălare de bani aduse în această lună. Alte paisprezece persoane vor fi judecate, printre acestea numărându-se Joao Vaccari Neto, fostul trezorier al Partidului Muncitorilor, aflat la putere în Brazilia, și Renato de Souza Duque, fostul președinte al serviciilor pentru întreprinderi ale Petrobras.
-Dirceu este cel mai vechi membru al Partidului Muncitorilor aflat la guvernare luat în custodie pentru legăturile cu această schemă. Dirceu a servit ca șef de cabinet al fostului președinte Luiz Inacio Lula da Silva între 2003 și 2005. A fost arestat la începutul lui august de acasă, unde deja se afla sub arest la domiciliu, cu o pedeapsă de 11 ani pentru implicarea într-o schemă de cumpărare a voturilor în Congres cu peste 10 ani în urmă. Procurorii au declarat că Dirceu a dezvoltat schema de luare de mită de la Petrobras, a acceptat mită în timp ce se afla în funcție și a continuat să primească plăți de la antreprenori după ce a fost închis la sfârșitul lui 2013 pentru scandalul voturilor cumpărate.
-Conform procurorilor, schema de la Petrobras a implicat aproximativ 2 miliarde de dolari sub formă de mită și alte fonduri ilegale. O parte din acei bani s-ar fi întors în fondul de campanie al partidului aflat la guvernare și al aliaților acestora. De asemenea, ar fi inclus mită către directorii Petrobras în schimbul unor contracte umflate. Recuperarea „miraculoasă” a unui elev supraviețuitor al masacrului de la Peshawar Un adolescent paralizat după ce fusese împușcat de patru ori în cel mai cumplit atac terorist din Pakistan a reușit o recuperare „miraculoasă” după ce a urmat un tratament în Regatul Unit. Lui Mohamed Ibrahim Khan, în vârstă de 13 ani, doctorii din Pakistan îi spuseseră că nu va mai putea să meargă niciodată.
-Cel puțin 140 de persoane, majoritatea copii, au fost ucise când bărbați înarmați au atacat școala publică a armatei din Peshawar în luna decembrie a anului trecut. Mohamed, care a sosit la Londra luna trecută pentru operație, va fi externat mai târziu din spital. Exact cu nouă luni în urmă, într-o dimineață obișnuită de marți, Mohamed stătea la ora de primul ajutor și își asculta atent profesorii. Chiar atunci, șapte bărbați înarmați deghizați în uniformele agenților de pază intrau în școala publică a armatei. Purtau centuri cu explozivi și aveau de îndeplinit o misiune simplă: să îi ucidă pe toți bărbații, femeile și copiii care le ieșeau în cale. „Nu pot uita ce s-a întâmplat în acea zi”, spune Mohamed cu o privire aspră.
-Stăteam în amfiteatru, puneam întrebări... apoi am auzit focuri de armă afară. Teroriștii au intrat înăuntru și au început să ucidă. Profesorul nostru a fost ars de viu. Mohamed descrie cum a scos patru elevi din amfiteatru în timp ce se desfășura carnagiul. Apoi spune că și-a auzit prietenul, pe Hamza, strigându-l. Spunea „oh, frate, salvează-mă”. L-am ținut de mână. Atunci eu am fost împușcat în spate, iar el în cap. Cei mai mulți dintre cei uciși în atac erau elevi Hamza a murit în brațele lui Mohamed. Mohamed își amintește că imediat după asta a leșinat și că următorul lucru pe care l-a știut a fost că se afla pe un pat de spital, paralizat de la brâu în jos.
-Doctorii din Peshawar din nordul Pakistanului, apoi cei din Rawalpindi, aproape de capitală, i-au spus familiei sale că nu exista tratament și că nu va mai putea merge niciodată. „Când l-am văzut, am simțit cum îmi iese sufletul”, spune Sher Khan, tatăl lui Mohamed. Acele nouă luni au fost cele mai grele din viața mea. Însă Khan și soția lui, Sherbano, au refuzat să creadă că fiul lor atât de pasionat de crichet nu-și va mai putea folosi vreodată picioarele. Au făcut o campanie și au cerut ajutor de la televiziunea pakistaneză, atrăgând sprijinul unor oameni faimoși precum Imran Khan, jucător de crichet devenit politician.
-Într-un final, au reușit să strângă fonduri pentru a-l duce pe Mohamed în Regatul Unit și a-i oferi tratament la clinica privată Harley Street din Londra. Neurochirurgul consultant Irfan Malik l-a descris pe Mohamed drept „înspăimântat” când acesta a ajuns la spital. „Își petrecuse ultimele [câteva] luni zăcând în pat, fără să se poată mișca de pe o parte pe alta, spune Malik. Era slăbit, se pusese multă presiune pe spatele lui. Nu era într-o formă prea bună. O vertebră de la baza coloanei vertebrale a lui Mohamed fusese distrusă Mohamed fusese împușcat în umăr, în șold și în spate în timpul atacului, iar coloana vertebrală inferioară îi fusese distrusă, ducând la paralizie.
-Însă, în timpul unei operații care a durat șase ore, Malik și echipa lui au reușit să lege din nou terminațiile nervoase și să reconstruiască partea distrusă a coloanei. Chiar și Malik a fost surprins de ceea ce s-a întâmplat în continuare. Exact la o săptămână după operație, Mohamed s-a ridicat și a început să facă pași și să meargă. Nu ne așteptam la un rezultat atât de bun. A fost un miracol”, spune acesta. În mai puțin de două săptămâni de la operație, Mohamed este gata să părăsească spitalul și să înceapă procesul lung de recuperare. Mohamed a sfidat soarta și a început să meargă din nou Vrea să devină puternic și să își continue studiile în Regatul Unit. Însă este hotărât să revină în Pakistan, să se înroleze în armată și să lupte împotriva terorismului.
-„Simt că am încă o șansă la viață” spune el, arătând imaginile cu arme desenate de el lângă manuale școlare și stilouri Fizic, Mohamed devine tot mai puternic în fiecare zi, însă trauma psihologică prin care trece și acum este de neimaginat. „Furia mea nu a scăzut”, mărturisește el. În școala mea au fost uciși copii mici. Ce crimă au comis ei? Mama lui își șterge o lacrimă, îl mângâie pe creștet și spune: „Îmi văd fiul mergând din nou”. Va putea să-și continue firesc viața. Serviciul 4G „Super Voice” de la Three oferă semnal mai bun Three folosește un spectru 4G cu o frecvență mai joasă, care poate acoperi o zonă mai extinsă
-Furnizorul de telefonie mobilă Three a lansat în Regatul Unit un serviciu despre care spune că va îmbunătăți recepția în interiorul clădirilor și în zonele rurale fără semnal. Serviciul 4G Super Voice le permite clienților să efectueze apeluri și să trimită mesaje text folosind un spectru cu o frecvență mai joasă. Și alte rețele intenționează să introducă aceeași tehnologie, cunoscută ca „Voice Over Long-Term Evolution (VoLTE)”. Aceasta funcționează momentan doar cu Samsung Galaxy S5, însă telefoanele iPhone recente vor beneficia de ea în lunile următoare. Three menționează că până la 5,5 milioane de clienți vor avea acces la serviciu până în 2017.
-Responsabilul șef pentru tehnologie, Bryn Jones a declarat: „Până la sfârșitul anului, un milion dintre clienții noștri vor avea acces la o acoperire mai bună în interior și își vor putea folosi telefoanele în mai multe locuri ca până acum”. Vedetele se pregătesc pentru stagiunea de pantomimă Stagiunea de pantomimă este foarte importantă pentru teatrele din tot Regatul Unit, multe dintre ele pregătindu-se acum pentru stagiunea din acest an. Acum, la teatrul de Crăciun participă unele dintre numele cele mai mari din showbusiness. Matthew Kelly și Hayley Mills vor apărea în Cenușăreasa - primul în rolul uneia dintre surorile rele, iar a doua în rolul zânei. Aceștia dezvăluie secretele pantomimei lor la BBC Breakfast. Steven Wilson: „Dacă nu fac nimic, mă simt vinovat”
-Steven Wilson a fost desemnat recent drept marele câștigător al Progressive Music Awards Steven Wilson a fost numit de multe ori drept cel mai muncitor muzician din lumea rockului progresiv. Talentatul muzician a câștigat trei premii la Progressive Music Awards, care a avut loc luna aceasta la Londra, printre care și premiul pentru cel mai bun album al anului pentru Hand. În recenzia sa de cinci stele, The Guardian a numit albumul „o operă de artă inteligentă, expresivă și captivantă”. Încă din anii 1980, Wilson este motorul mai multor proiecte muzicale, cel mai cunoscut dintre acestea fiind trupa de rock Porcupine Tree. Acum, înainte de două spectacole cu casa închisă la Royal Albert Hall, Wilson lansează un dublu LP doar în format vinil, Transience, pentru a arăta latura „mai accesibilă” a activității sale solo.
-A povestit pentru BBC despre dragostea lui pentru viniluri și despre programul său încărcat și a explicat cum a ajuns actorul de comedie Matt Berry să îi deschidă spectacolele. Ce înseamnă vinil pentru tine? Am crescut chiar în perioada de sfârșit a erei vinilurilor și îmi amintesc că atunci abia așteptam apariția CD-ului, căci vinilul era atât de enervant. Cumpărai un disc, mergeai cu el acasă, avea o zgârietură și trebuia să îl aduci înapoi. Iubesc CD-urile, iar pentru anumite tipuri de muzică, de exemplu cea clasică, sunt mai bune decât vinilurile. Însă problema cu CD-urile și cu descărcările digitale este aceea că nu mai există nimic pe care să îl prețuiești cu adevărat. Să ai un vinil e ca și cum ai avea un tablou frumos agățat în sufragerie.
-E ceva ce poți ține în mână, în timp ce te lași absorbit de versuri și copleșit de actul artistic. Am crezut că e doar o chestie nostalgică, însă nu are cum să fie așa dacă unor puști prea tineri să-și amintească de viniluri le place acest gen de experiență. Ai vreun vinil la care ții în mod special? Recunosc că am scăpat de toate vinilurile în anii '90. Toate vinilurile pe care le am sunt cumpărate din nou. Am pornit de la ideea de a reface colecția pe care o aveam la 15 ani, însă am trecut de limita aceea. Primul disc pe care mi-am convins părinții să mi-l cumpere a fost Out of the Blue de la Electric Light Orchestra.
-Dacă aș mai fi avut încă exemplarul inițial, acesta ar fi avut valoare sentimentală, însă, din păcate, se află pe undeva printr-un magazin de caritate. Steven Wilson speră că albumul va fi o poartă către posibili fani noi De ce ți-ai lansat noua compilație Transience pe vinil? Aceasta a fost concepută inițial ca idee pentru Ziua magazinelor de discuri, însă am ratat ocazia. Casa mea de discuri sugerase să adun câteva dintre melodiile mele mai scurte și mai accesibile. Am ajuns să fiu ușor obsedat de ideea de a face ceva gen „introducere în muzica lui Steven Wilson” și am ținut neapărat ca proiectul să fie lansat doar pe vinil. Cine cumpără vinilul primește, de asemenea, și o variantă descărcată la rezoluție înaltă.
-Ești îngrijorat că albumul nu va arăta muzica ta în adevărata ei lumină?
@@ -8,20 +8,16 @@ from pathlib import Path
 from unittest.mock import patch

 import pytest
-import pytorch_lightning as pl
 import torch
-from pytest import param
 from torch.utils.data import DataLoader

-import lightning_base
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-from transformers.testing_utils import CaptureStderr, CaptureStdout, require_multigpu
+from transformers import AutoTokenizer
+from transformers.testing_utils import require_multigpu

 from .distillation import distill_main, evaluate_checkpoint
-from .finetune import SummarizationModule, main
-from .pack_dataset import pack_data_dir
+from .finetune import main
 from .run_eval import generate_summaries_or_translations, run_generate
-from .utils import Seq2SeqDataset, TranslationDataset, label_smoothed_nll_loss, lmap, load_json
+from .utils import SummarizationDataset, lmap, load_json


 logging.basicConfig(level=logging.DEBUG)
@@ -29,9 +25,7 @@ logging.basicConfig(level=logging.DEBUG)
 logger = logging.getLogger()
 CUDA_AVAILABLE = torch.cuda.is_available()
 CHEAP_ARGS = {
-    "label_smoothing": 0.2,
-    "early_stopping_patience": 2,
-    "logger_name": "default",
+    "logger": "default",
    "length_penalty": 0.5,
    "cache_dir": "",
    "task": "summarization",
@@ -45,7 +39,7 @@ CHEAP_ARGS = {
    "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
+    "fp16": CUDA_AVAILABLE,
    "no_teacher": False,
    "fp16_opt_level": "O1",
    "gpus": 1 if CUDA_AVAILABLE else 0,
@@ -53,7 +47,7 @@ CHEAP_ARGS = {
    "max_grad_norm": 1.0,
    "do_train": True,
    "do_predict": True,
-    "accumulate_grad_batches": 1,
+    "gradient_accumulation_steps": 1,
    "server_ip": "",
    "server_port": "",
    "seed": 42,
@@ -62,11 +56,10 @@ CHEAP_ARGS = {
    "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,
+    "num_train_epochs": 1,
    "train_batch_size": 2,
    "eval_batch_size": 2,
    "max_source_length": 12,
@@ -86,11 +79,11 @@ CHEAP_ARGS = {


 def _dump_articles(path: Path, articles: list):
-    content = "\n".join(articles)
-    Path(path).open("w").writelines(content)
+    with path.open("w") as f:
+        f.write("\n".join(articles))


-ARTICLES = [" Sam ate lunch today.", "Sams lunch ingredients."]
+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"
@@ -128,7 +121,7 @@ class TestSummarizationDistiller(unittest.TestCase):
        updates = dict(
            student_encoder_layers=2,
            student_decoder_layers=1,
-            max_epochs=4,
+            num_train_epochs=4,
            val_check_interval=0.25,
            alpha_hid=2.0,
            model_name_or_path="IGNORE_THIS_IT_DOESNT_GET_USED",
@@ -146,51 +139,6 @@ class TestSummarizationDistiller(unittest.TestCase):

        evaluate_checkpoint(ckpts[0], dest_dir=Path(tempfile.mkdtemp()))

-    def test_loss_fn(self):
-        model = AutoModelForSeq2SeqLM.from_pretrained(BART_TINY, return_dict=True)
-        input_ids, mask = model.dummy_inputs["input_ids"], model.dummy_inputs["attention_mask"]
-        target_ids = torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]], dtype=torch.long, device=model.device)
-        decoder_input_ids = target_ids[:, :-1].contiguous()  # Why this line?
-        lm_labels = target_ids[:, 1:].clone()  # why clone?
-        model_computed_loss = model(
-            input_ids, attention_mask=mask, decoder_input_ids=decoder_input_ids, labels=lm_labels, use_cache=False
-        ).loss
-
-        logits = model(input_ids, attention_mask=mask, decoder_input_ids=decoder_input_ids, use_cache=False).logits
-
-        lprobs = torch.nn.functional.log_softmax(logits, dim=-1)
-        smoothed_loss, nll_loss = label_smoothed_nll_loss(
-            lprobs, lm_labels, 0.1, ignore_index=model.config.pad_token_id
-        )
-        with self.assertRaises(AssertionError):
-            # TODO: understand why this breaks
-            self.assertEqual(nll_loss, model_computed_loss)
-
-    def test_distill_mbart(self):
-        updates = dict(
-            student_encoder_layers=2,
-            student_decoder_layers=1,
-            num_train_epochs=4,
-            val_check_interval=0.25,
-            alpha_hid=2.0,
-            task="translation",
-            model_name_or_path="IGNORE_THIS_IT_DOESNT_GET_USED",
-            tokenizer_name=MBART_TINY,
-            teacher=MBART_TINY,
-            src_lang="en_XX",
-            tgt_lang="ro_RO",
-        )
-        model = self._test_distiller_cli(updates, check_contents=False)
-
-        ckpts = list(Path(model.output_dir).glob("*.ckpt"))
-        self.assertEqual(1, len(ckpts))
-        transformer_ckpts = list(Path(model.output_dir).glob("**/*.bin"))
-        all_files = list(Path(model.output_dir).glob("best_tfmr/*"))
-        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(
@@ -205,11 +153,9 @@ class TestSummarizationDistiller(unittest.TestCase):

    def _test_distiller_cli(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,
+            num_train_epochs=2,
            alpha_mlm=0.2,
            alpha_ce=0.8,
            do_predict=True,
@@ -240,7 +186,7 @@ class TestSummarizationDistiller(unittest.TestCase):
        self.assertGreaterEqual(last_step_stats["val_avg_gen_time"], 0.01)
        self.assertGreaterEqual(1.0, last_step_stats["val_avg_gen_time"])
        self.assertIsInstance(last_step_stats[f"val_avg_{model.val_metric}"], float)
-        desired_n_evals = int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1)
+        desired_n_evals = int(args_d["num_train_epochs"] * (1 / args_d["val_check_interval"]) + 1)
        self.assertEqual(len(metrics["val"]), desired_n_evals)
        self.assertEqual(len(metrics["test"]), 1)
        return model
@@ -261,13 +207,11 @@ def test_run_eval_bart(model):


@pytest.mark.parametrize(
-    ["model"], [pytest.param(T5_TINY), pytest.param(BART_TINY), pytest.param(MBART_TINY), pytest.param(MARIAN_TINY)],
+    ["model"], [pytest.param(T5_TINY), pytest.param(BART_TINY), pytest.param(MBART_TINY), pytest.param(MARIAN_TINY)]
 )
 def test_finetune(model):
    args_d: dict = CHEAP_ARGS.copy()
    task = "translation" if model in [MBART_TINY, MARIAN_TINY] else "summarization"
-    args_d["label_smoothing"] = 0.1 if task == "translation" else 0
-
    tmp_dir = make_test_data_dir()
    output_dir = tempfile.mkdtemp(prefix="output_")
    args_d.update(
@@ -305,180 +249,22 @@ def test_finetune(model):
        assert bart.decoder.embed_tokens == bart.shared


-def test_finetune_extra_model_args():
-    args_d: dict = CHEAP_ARGS.copy()
-
-    task = "summarization"
-    tmp_dir = make_test_data_dir()
-
-    args_d.update(
-        data_dir=tmp_dir,
-        tokenizer_name=None,
-        train_batch_size=2,
-        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)
-    assert str(excinfo.value) == f"model config doesn't have a `{unsupported_param}` attribute"
-
-
-def test_finetune_lr_schedulers():
-    args_d: dict = CHEAP_ARGS.copy()
-
-    task = "summarization"
-    tmp_dir = make_test_data_dir()
-
-    model = BART_TINY
-    output_dir = tempfile.mkdtemp(prefix="output_1_")
-
-    args_d.update(
-        data_dir=tmp_dir,
-        model_name_or_path=model,
-        output_dir=output_dir,
-        tokenizer_name=None,
-        train_batch_size=2,
-        eval_batch_size=2,
-        do_predict=False,
-        task=task,
-        src_lang="en_XX",
-        tgt_lang="ro_RO",
-        freeze_encoder=True,
-        freeze_embeds=True,
-    )
-
-    # emulate finetune.py
-    parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
-    parser = SummarizationModule.add_model_specific_args(parser, os.getcwd())
-    args = {"--help": True}
-
-    # --help test
-    with pytest.raises(SystemExit) as excinfo:
-        with CaptureStdout() as cs:
-            args = parser.parse_args(args)
-        assert False, "--help is expected to sys.exit"
-    assert excinfo.type == SystemExit
-    expected = lightning_base.arg_to_scheduler_metavar
-    assert expected in cs.out, "--help is expected to list the supported schedulers"
-
-    # --lr_scheduler=non_existing_scheduler test
-    unsupported_param = "non_existing_scheduler"
-    args = {f"--lr_scheduler={unsupported_param}"}
-    with pytest.raises(SystemExit) as excinfo:
-        with CaptureStderr() as cs:
-            args = parser.parse_args(args)
-        assert False, "invalid argument is expected to sys.exit"
-    assert excinfo.type == SystemExit
-    expected = f"invalid choice: '{unsupported_param}'"
-    assert expected in cs.err, f"should have bailed on invalid choice of scheduler {unsupported_param}"
-
-    # --lr_scheduler=existing_scheduler test
-    supported_param = "cosine"
-    args_d1 = args_d.copy()
-    args_d1["lr_scheduler"] = supported_param
-    args = argparse.Namespace(**args_d1)
-    model = main(args)
-    assert getattr(model.hparams, "lr_scheduler") == supported_param, f"lr_scheduler={supported_param} shouldn't fail"
-
-
-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.parametrize(["tok_name"], [pytest.param(MBART_TINY), pytest.param(MARIAN_TINY)])
-def test_mbart_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  # Truncated
-    assert max_len_source > max_src_len
-    src_lang, tgt_lang = "ro_RO", "de_DE"  # NOT WHAT IT WAS TRAINED ON
-    train_dataset = TranslationDataset(
-        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,
-    )
-    dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
-    for batch in dataloader:
-        assert isinstance(batch, dict)
-        assert batch["attention_mask"].shape == batch["input_ids"].shape
-        # show that articles were trimmed.
-        assert batch["input_ids"].shape[1] == max_src_len
-        # show that targets are the same len
-        assert batch["decoder_input_ids"].shape[1] == max_tgt_len
-        if tok_name == MARIAN_TINY:
-            continue
-        # check language codes in correct place
-        assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
-        assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
-        assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
-        assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
-
-        break  # No need to test every batch
-
-
-@pytest.mark.parametrize(["tok"], [pytest.param(T5_TINY), pytest.param(BART_TINY), param(MARIAN_TINY)])
-def test_summarization_dataset_truncation(tok):
+@pytest.mark.parametrize(
+    ["tok"], [pytest.param(T5_TINY), pytest.param(BART_TINY), pytest.param(MBART_TINY), pytest.param(MARIAN_TINY)]
+)
+def test_dataset(tok):
    tokenizer = AutoTokenizer.from_pretrained(tok)
    tmp_dir = make_test_data_dir()
    max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
    max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
    trunc_target = 4
-    train_dataset = Seq2SeqDataset(
-        tokenizer, data_dir=tmp_dir, type_path="train", max_source_length=20, max_target_length=trunc_target,
+    train_dataset = SummarizationDataset(
+        tokenizer,
+        data_dir=tmp_dir,
+        type_path="train",
+        max_source_length=20,
+        max_target_length=trunc_target,
+        tgt_lang="ro_RO",
    )
    dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
    for batch in dataloader:
@@ -489,4 +275,3 @@ def test_summarization_dataset_truncation(tok):
        # show that targets were truncated
        assert batch["decoder_input_ids"].shape[1] == trunc_target  # Truncated
        assert max_len_target > trunc_target  # Truncated
-        break  # No need to test every batch
@@ -6,7 +6,7 @@ export GAS=1

 python finetune.py \
    --learning_rate=3e-5 \
-    --fp16 --fp16_opt_level=O1 \
+    --fp16 \
    --gpus 1 \
    --do_train \
    --do_predict \
@@ -20,5 +20,5 @@ python finetune.py \
    --tokenizer_name facebook/bart-large \
    --warmup_steps 500 \
    --output_dir distilbart-cnn-12-6 \
-    "$@"
+    $@

--- a/Show More
+++ b/Show More