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Merge pull request #13 from microsoft/raviskolli/ort
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.circleci/config.yml

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keys:
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- v0.4-deploy_doc-{{ checksum "setup.py" }}
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- v0.4-{{ checksum "setup.py" }}
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- run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
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- run: pip install --upgrade pip
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- run: pip install ."[docs]"
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- save_cache:
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key: v0.4-deploy_doc-{{ checksum "setup.py" }}
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docker:
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- image: circleci/python:3.6
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resource_class: medium
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environment:
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TRANSFORMERS_IS_CI: yes
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parallelism: 1
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steps:
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- checkout

.circleci/deploy.sh

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deploy_doc "c5d6a28" v4.4.1
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deploy_doc "6bc89ed" v4.4.2
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deploy_doc "4906a29" v4.5.0
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deploy_doc "4bae96e" # v4.5.1 Latest stable release
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deploy_doc "4bae96e" v4.5.1
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deploy_doc "25dee4a" # v4.6.0 Latest stable release

.github/conda/meta.yaml

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- pip
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- numpy >=1.17
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- dataclasses
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- importlib_metadata
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- huggingface_hub
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- packaging
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- filelock
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- requests
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- python
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- numpy >=1.17
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- dataclasses
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- importlib_metadata
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- huggingface_hub
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- packaging
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- filelock
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- requests

.github/workflows/model-templates.yml

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name: Model templates runner
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on:
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push:
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branches:
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- master
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pull_request:
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paths:
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- "src/**"
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- name: Install dependencies
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pip install --upgrade pip
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sudo apt -y update && sudo apt install -y libsndfile1-dev
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pip install .[dev]
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- name: Create model files
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make style
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python utils/check_table.py --fix_and_overwrite
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python utils/check_dummies.py --fix_and_overwrite
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python utils/check_copies.py --fix_and_overwrite
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- name: Run all non-slow tests
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.github/workflows/release-conda.yml

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with:
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auto-update-conda: true
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auto-activate-base: false
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python-version: 3.8
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activate-environment: "build-transformers"
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channels: huggingface
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.github/workflows/self-push.yml

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runs-on: [self-hosted, docker-gpu, single-gpu]
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timeout-minutes: 120
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options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
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TF_NUM_INTRAOP_THREADS: 8
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python -m pytest -n 2 --dist=loadfile --make-reports=tests_tf_gpu tests
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python -m pytest -n 1 --dist=loadfile --make-reports=tests_tf_gpu tests
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- name: Failure short reports
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if: ${{ always() }}
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timeout-minutes: 120
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options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
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python -m pytest -n 2 --dist=loadfile --make-reports=tests_tf_multi_gpu tests
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python -m pytest -n 1 --dist=loadfile --make-reports=tests_tf_multi_gpu tests
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- name: Failure short reports
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if: ${{ always() }}
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- name: Install dependencies
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apt -y update && apt install -y libaio-dev
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apt -y update && apt install -y libaio-dev
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python utils/notification_service.py push
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python utils/notification_service.py push

.github/workflows/self-scheduled.yml

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README.md

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docs/source/_static/js/custom.js

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// These two things need to be updated at each release for the version selector.
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// Last stable version
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const stableVersion = "v4.5.1"
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const stableVersion = "v4.6.0"
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// Dictionary doc folder to label. The last stable version should have an empty key.
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const versionMapping = {
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"master": "master",
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"": "v4.5.0/v4.5.1 (stable)",
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"": "v4.6.0 (stable)",
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"v4.5.1": "v4.5.0/v4.5.1",
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"v4.4.2": "v4.4.0/v4.4.1/v4.4.2",
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"v4.3.3": "v4.3.0/v4.3.1/v4.3.2/v4.3.3",
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"v4.2.2": "v4.2.0/v4.2.1/v4.2.2",

docs/source/community.md

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|[Fine-tune BART for summarization in two languages with Trainer class](https://github.com/elsanns/xai-nlp-notebooks/blob/master/fine_tune_bart_summarization_two_langs.ipynb) | How to fine-tune BART for summarization in two languages with Trainer class | [Eliza Szczechla](https://github.com/elsanns) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/elsanns/xai-nlp-notebooks/blob/master/fine_tune_bart_summarization_two_langs.ipynb)|
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|[Evaluate Big Bird on Trivia QA](https://github.com/patrickvonplaten/notebooks/blob/master/Evaluating_Big_Bird_on_TriviaQA.ipynb) | How to evaluate BigBird on long document question answering on Trivia QA | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Evaluating_Big_Bird_on_TriviaQA.ipynb)|
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| [Create video captions using Wav2Vec2](https://github.com/Muennighoff/ytclipcc/blob/main/wav2vec_youtube_captions.ipynb) | How to create YouTube captions from any video by transcribing the audio with Wav2Vec | [Niklas Muennighoff](https://github.com/Muennighoff) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Muennighoff/ytclipcc/blob/main/wav2vec_youtube_captions.ipynb) |
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| [Fine-tune the Vision Transformer on CIFAR-10 using PyTorch Lightning](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_PyTorch_Lightning.ipynb) | How to fine-tune the Vision Transformer (ViT) on CIFAR-10 using HuggingFace Transformers, Datasets and PyTorch Lightning | [Niels Rogge](https://github.com/nielsrogge) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_PyTorch_Lightning.ipynb) |
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| [Fine-tune the Vision Transformer on CIFAR-10 using the 🤗 Trainer](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_the_%F0%9F%A4%97_Trainer.ipynb) | How to fine-tune the Vision Transformer (ViT) on CIFAR-10 using HuggingFace Transformers, Datasets and the 🤗 Trainer | [Niels Rogge](https://github.com/nielsrogge) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_the_%F0%9F%A4%97_Trainer.ipynb) |
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| [Evaluate LUKE on Open Entity, an entity typing dataset](https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_open_entity.ipynb) | How to evaluate *LukeForEntityClassification* on the Open Entity dataset | [Ikuya Yamada](https://github.com/ikuyamada) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_open_entity.ipynb) |
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| [Evaluate LUKE on TACRED, a relation extraction dataset](https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_tacred.ipynb) | How to evaluate *LukeForEntityPairClassification* on the TACRED dataset | [Ikuya Yamada](https://github.com/ikuyamada) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_tacred.ipynb) |
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| [Evaluate LUKE on CoNLL-2003, an important NER benchmark](https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_conll_2003.ipynb) | How to evaluate *LukeForEntitySpanClassification* on the CoNLL-2003 dataset | [Ikuya Yamada](https://github.com/ikuyamada) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_conll_2003.ipynb) |
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| [Evaluate BigBird-Pegasus on PubMed dataset](https://github.com/vasudevgupta7/bigbird/blob/main/notebooks/bigbird_pegasus_evaluation.ipynb) | How to evaluate *BigBirdPegasusForConditionalGeneration* on PubMed dataset | [Vasudev Gupta](https://github.com/vasudevgupta7) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vasudevgupta7/bigbird/blob/main/notebooks/bigbird_pegasus_evaluation.ipynb) |

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