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* Convert PretrainedConfig doc to Markdown * Use syntax * Add necessary doc files (#14496) * Doc fixes (#14499) * Fixes for the new front * Convert DETR file for table * Title is needed * Simplify a bit * Even simpler * Remove imports * Fix typo in toctree (#14516) * Fix checkpoints badge * Update versions.yml format (#14517) * Doc new front github actions (#14512) * Doc new front github actions * Fix docstring * Fix feature extraction utils import (#14515) * Address Julien's comments * Push to doc-builder * Ready for merge * Remove old build and deploy * Doc misc fixes (#14583) * Rm versions.yml from doc * Fix converting.rst * Rm pretrained_models from toctree * Fix index links (#14567) * Fix links in README * Localized READMEs * Fix copy script * Fix find doc script * Update README_ko.md Co-authored-by: Julien Chaumond <julien@huggingface.co> Co-authored-by: Julien Chaumond <julien@huggingface.co> * Adapt build command to new CLI tools (#14578) * Fix typo * Fix doc interlinks (#14589) * Convert PretrainedConfig doc to Markdown * Use syntax * Rm pattern <[a-z]+(.html).*> * Rm huggingface.co/transformers/master * Rm .html * Rm .html from index.mdx * Rm .html from model_summary.rst * Update index.mdx rm html * Update remove .html * Fix inner doc links * Fix interlink in preprocssing.rst * Update pr_checks Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Convert PretrainedConfig doc to Markdown * Use syntax * Add necessary doc files (#14496) * Doc fixes (#14499) * Fixes for the new front * Convert DETR file for table * Title is needed * Simplify a bit * Even simpler * Remove imports * Fix checkpoints badge * Fix typo in toctree (#14516) * Update versions.yml format (#14517) * Doc new front github actions (#14512) * Doc new front github actions * Fix docstring * Fix feature extraction utils import (#14515) * Address Julien's comments * Push to doc-builder * Ready for merge * Remove old build and deploy * Doc misc fixes (#14583) * Rm versions.yml from doc * Fix converting.rst * Rm pretrained_models from toctree * Fix index links (#14567) * Fix links in README * Localized READMEs * Fix copy script * Fix find doc script * Update README_ko.md Co-authored-by: Julien Chaumond <julien@huggingface.co> Co-authored-by: Julien Chaumond <julien@huggingface.co> * Adapt build command to new CLI tools (#14578) * Fix typo * Fix doc interlinks (#14589) * Convert PretrainedConfig doc to Markdown * Use syntax * Rm pattern <[a-z]+(.html).*> * Rm huggingface.co/transformers/master * Rm .html * Rm .html from index.mdx * Rm .html from model_summary.rst * Update index.mdx rm html * Update remove .html * Fix inner doc links * Fix interlink in preprocssing.rst * Update pr_checks Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Styling Co-authored-by: Mishig Davaadorj <mishig.davaadorj@coloradocollege.edu> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Julien Chaumond <julien@huggingface.co>
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89 lines
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<!---
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Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# 🤗 Transformers Notebooks
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You can find here a list of the official notebooks provided by Hugging Face.
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Also, we would like to list here interesting content created by the community.
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If you wrote some notebook(s) leveraging 🤗 Transformers and would like be listed here, please open a
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Pull Request so it can be included under the Community notebooks.
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## Hugging Face's notebooks 🤗
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### Documentation notebooks
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You can open any page of the documentation as a notebook in colab (there is a button directly on said pages) but they are also listed here if you need to:
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| Notebook | Description | |
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| [Quicktour of the library](https://github.com/huggingface/notebooks/blob/master/transformers_doc/quicktour.ipynb) | A presentation of the various APIs in Transformers | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/quicktour.ipynb) |
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| [Summary of the tasks](https://github.com/huggingface/notebooks/blob/master/transformers_doc/task_summary.ipynb) | How to run the models of the Transformers library task by task | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/task_summary.ipynb) |
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| [Preprocessing data](https://github.com/huggingface/notebooks/blob/master/transformers_doc/preprocessing.ipynb) | How to use a tokenizer to preprocess your data | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/preprocessing.ipynb) |
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| [Fine-tuning a pretrained model](https://github.com/huggingface/notebooks/blob/master/transformers_doc/training.ipynb) | How to use the Trainer to fine-tune a pretrained model | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/training.ipynb) |
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| [Summary of the tokenizers](https://github.com/huggingface/notebooks/blob/master/transformers_doc/tokenizer_summary.ipynb) | The differences between the tokenizers algorithm | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/tokenizer_summary.ipynb) |
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| [Multilingual models](https://github.com/huggingface/notebooks/blob/master/transformers_doc/multilingual.ipynb) | How to use the multilingual models of the library | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/multilingual.ipynb) |
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| [Fine-tuning with custom datasets](https://github.com/huggingface/notebooks/blob/master/transformers_doc/custom_datasets.ipynb) | How to fine-tune a pretrained model on various tasks | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/custom_datasets.ipynb) |
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### PyTorch Examples
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| Notebook | Description | |
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| [Train your tokenizer](https://github.com/huggingface/notebooks/blob/master/examples/tokenizer_training.ipynb) | How to train and use your very own tokenizer |[](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/tokenizer_training.ipynb) |
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| [Train your language model](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling_from_scratch.ipynb) | How to easily start using transformers | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling_from_scratch.ipynb) |
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| [How to fine-tune a model on text classification](https://github.com/huggingface/notebooks/blob/master/examples/text_classification.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on any GLUE task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb)|
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| [How to fine-tune a model on language modeling](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling.ipynb)|
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| [How to fine-tune a model on token classification](https://github.com/huggingface/notebooks/blob/master/examples/token_classification.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS). | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb)|
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| [How to fine-tune a model on question answering](https://github.com/huggingface/notebooks/blob/master/examples/question_answering.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on SQUAD. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb)|
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| [How to fine-tune a model on multiple choice](https://github.com/huggingface/notebooks/blob/master/examples/multiple_choice.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on SWAG. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/multiple_choice.ipynb)|
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| [How to fine-tune a model on translation](https://github.com/huggingface/notebooks/blob/master/examples/translation.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on WMT. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/translation.ipynb)|
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| [How to fine-tune a model on summarization](https://github.com/huggingface/notebooks/blob/master/examples/summarization.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on XSUM. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization.ipynb)|
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| [How to fine-tune a speech recognition model in English](https://github.com/huggingface/notebooks/blob/master/examples/speech_recognition.ipynb)| Show how to preprocess the data and fine-tune a pretrained Speech model on TIMIT | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/speech_recognition.ipynb)|
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| [How to fine-tune a speech recognition model in any language](https://github.com/huggingface/notebooks/blob/master/examples/multi_lingual_speech_recognition.ipynb)| Show how to preprocess the data and fine-tune a multi-lingually pretrained speech model on Common Voice | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/multi_lingual_speech_recognition.ipynb)|
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| [How to fine-tune a model on audio classification](https://github.com/huggingface/notebooks/blob/master/examples/audio_classification.ipynb)| Show how to preprocess the data and fine-tune a pretrained Speech model on Keyword Spotting | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/audio_classification.ipynb)|
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| [How to train a language model from scratch](https://github.com/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)| Highlight all the steps to effectively train Transformer model on custom data | [](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)|
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| [How to generate text](https://github.com/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)| How to use different decoding methods for language generation with transformers | [](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)|
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| [How to export model to ONNX](https://github.com/huggingface/notebooks/blob/master/examples/onnx-export.ipynb) | Highlight how to export and run inference workloads through ONNX |
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| [How to use Benchmarks](https://github.com/huggingface/transformers/notebooks/blob/master/examples/benchmark.ipynb) | How to benchmark models with transformers | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/benchmark.ipynb)|
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| [Reformer](https://github.com/huggingface/blog/blob/master/notebooks/03_reformer.ipynb) | How Reformer pushes the limits of language modeling | [](https://colab.research.google.com/github/patrickvonplaten/blog/blob/master/notebooks/03_reformer.ipynb)|
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### TensorFlow Examples
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| Notebook | Description | |
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| [Train your tokenizer](https://github.com/huggingface/notebooks/blob/master/examples/tokenizer_training.ipynb) | How to train and use your very own tokenizer |[](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/tokenizer_training.ipynb) |
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| [Train your language model](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling_from_scratch-tf.ipynb) | How to easily start using transformers | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling_from_scratch-tf.ipynb) |
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| [How to fine-tune a model on text classification](https://github.com/huggingface/notebooks/blob/master/examples/text_classification-tf.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on any GLUE task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification-tf.ipynb)|
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| [How to fine-tune a model on language modeling](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling-tf.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling-tf.ipynb)|
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| [How to fine-tune a model on token classification](https://github.com/huggingface/notebooks/blob/master/examples/token_classification-tf.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS). | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification-tf.ipynb)|
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| [How to fine-tune a model on question answering](https://github.com/huggingface/notebooks/blob/master/examples/question_answering-tf.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on SQUAD. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering-tf.ipynb)|
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| [How to fine-tune a model on multiple choice](https://github.com/huggingface/notebooks/blob/master/examples/multiple_choice-tf.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on SWAG. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/multiple_choice-tf.ipynb)|
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| [How to fine-tune a model on translation](https://github.com/huggingface/notebooks/blob/master/examples/translation-tf.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on WMT. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/translation-tf.ipynb)|
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| [How to fine-tune a model on summarization](https://github.com/huggingface/notebooks/blob/master/examples/summarization-tf.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on XSUM. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization-tf.ipynb)|
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### Optimum notebooks
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🤗 [Optimum](https://github.com/huggingface/optimum) is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on targeted hardwares.
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| Notebook | Description | |
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| [How to quantize a model for text classification](https://github.com/huggingface/notebooks/blob/master/examples/text_classification_quantization_inc.ipynb) | Show how to apply [Intel Neural Compressor (INC)](https://github.com/intel/neural-compressor) quantization on a model for any GLUE task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification_quantization_inc.ipynb)|
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## Community notebooks:
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More notebooks developed by the community are available [here](community#community-notebooks).
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