diff --git a/docker/transformers-all-latest-gpu/Dockerfile b/docker/transformers-all-latest-gpu/Dockerfile index ce67f444c4d..75876dde53e 100644 --- a/docker/transformers-all-latest-gpu/Dockerfile +++ b/docker/transformers-all-latest-gpu/Dockerfile @@ -7,7 +7,7 @@ RUN apt update RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg RUN python3 -m pip install --no-cache-dir --upgrade pip -ARG REF=master +ARG REF=main RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF RUN python3 -m pip install --no-cache-dir -e ./transformers[dev,onnxruntime] diff --git a/docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile b/docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile index f20763e7c82..9ef0ac46238 100644 --- a/docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile +++ b/docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile @@ -7,7 +7,7 @@ RUN apt -y update RUN apt install -y libaio-dev RUN python3 -m pip install --no-cache-dir --upgrade pip -ARG REF=master +ARG REF=main RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF RUN python3 -m pip install --no-cache-dir -e ./transformers[testing,deepspeed] diff --git a/docker/transformers-pytorch-gpu/Dockerfile b/docker/transformers-pytorch-gpu/Dockerfile index 32bbd5cb063..d1828190b14 100644 --- a/docker/transformers-pytorch-gpu/Dockerfile +++ b/docker/transformers-pytorch-gpu/Dockerfile @@ -7,7 +7,7 @@ RUN apt update RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg RUN python3 -m pip install --no-cache-dir --upgrade pip -ARG REF=master +ARG REF=main RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing] diff --git a/docker/transformers-pytorch-tpu/Dockerfile b/docker/transformers-pytorch-tpu/Dockerfile index 860cffddc0f..b61f4add514 100644 --- a/docker/transformers-pytorch-tpu/Dockerfile +++ b/docker/transformers-pytorch-tpu/Dockerfile @@ -1,7 +1,7 @@ FROM google/cloud-sdk:slim # Build args. -ARG GITHUB_REF=refs/heads/master +ARG GITHUB_REF=refs/heads/main # TODO: This Dockerfile installs pytorch/xla 3.6 wheels. There are also 3.7 # wheels available; see below. diff --git a/docker/transformers-tensorflow-gpu/Dockerfile b/docker/transformers-tensorflow-gpu/Dockerfile index 4c210b21f95..a05ace7d08e 100644 --- a/docker/transformers-tensorflow-gpu/Dockerfile +++ b/docker/transformers-tensorflow-gpu/Dockerfile @@ -7,7 +7,7 @@ RUN apt update RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg RUN python3 -m pip install --no-cache-dir --upgrade pip -ARG REF=master +ARG REF=main RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-tensorflow,testing] diff --git a/docs/source/benchmarks.mdx b/docs/source/benchmarks.mdx index 3d97d725ce5..244112001f5 100644 --- a/docs/source/benchmarks.mdx +++ b/docs/source/benchmarks.mdx @@ -23,7 +23,7 @@ and memory complexity of Transformer models. Let's take a look at how 🤗 Transformers models can be benchmarked, best practices, and already available benchmarks. -A notebook explaining in more detail how to benchmark 🤗 Transformers models can be found [here](https://github.com/huggingface/notebooks/tree/master/examples/benchmark.ipynb). +A notebook explaining in more detail how to benchmark 🤗 Transformers models can be found [here](https://github.com/huggingface/notebooks/tree/main/examples/benchmark.ipynb). ## How to benchmark 🤗 Transformers models diff --git a/docs/source/custom_datasets.mdx b/docs/source/custom_datasets.mdx index 45ba23fce26..7a0c4e3bc2b 100644 --- a/docs/source/custom_datasets.mdx +++ b/docs/source/custom_datasets.mdx @@ -43,8 +43,8 @@ whether a review is positive or negative. For a more in-depth example of how to fine-tune a model for text classification, take a look at the corresponding -[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb) -or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification-tf.ipynb). +[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb) +or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb). @@ -228,8 +228,8 @@ such as a person, location, or organization. In this example, learn how to fine- For a more in-depth example of how to fine-tune a model for token classification, take a look at the corresponding -[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb) -or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification-tf.ipynb). +[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb) +or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb). @@ -472,8 +472,8 @@ given a question. In this example, learn how to fine-tune a model on the [SQuAD] For a more in-depth example of how to fine-tune a model for question answering, take a look at the corresponding -[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb) -or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering-tf.ipynb). +[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb) +or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb). diff --git a/docs/source/model_doc/barthez.mdx b/docs/source/model_doc/barthez.mdx index 4d8743c9f87..f1969e8e942 100644 --- a/docs/source/model_doc/barthez.mdx +++ b/docs/source/model_doc/barthez.mdx @@ -38,7 +38,7 @@ This model was contributed by [moussakam](https://huggingface.co/moussakam). The ### Examples - BARThez can be fine-tuned on sequence-to-sequence tasks in a similar way as BART, check: - [examples/pytorch/summarization/](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization/README.md). + [examples/pytorch/summarization/](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization/README.md). ## BarthezTokenizer diff --git a/docs/source/tasks/audio_classification.mdx b/docs/source/tasks/audio_classification.mdx index 183bfe4c1d5..e239461762f 100644 --- a/docs/source/tasks/audio_classification.mdx +++ b/docs/source/tasks/audio_classification.mdx @@ -142,6 +142,6 @@ At this point, only three steps remain: -For a more in-depth example of how to fine-tune a model for audio classification, take a look at the corresponding [PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/audio_classification.ipynb). +For a more in-depth example of how to fine-tune a model for audio classification, take a look at the corresponding [PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/audio_classification.ipynb). \ No newline at end of file diff --git a/docs/source/tasks/image_classification.mdx b/docs/source/tasks/image_classification.mdx index 7646feb55c1..0ca317e79d6 100644 --- a/docs/source/tasks/image_classification.mdx +++ b/docs/source/tasks/image_classification.mdx @@ -169,6 +169,6 @@ At this point, only three steps remain: -For a more in-depth example of how to fine-tune a model for image classification, take a look at the corresponding [PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/image_classification.ipynb). +For a more in-depth example of how to fine-tune a model for image classification, take a look at the corresponding [PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb). \ No newline at end of file diff --git a/docs/source/tasks/language_modeling.mdx b/docs/source/tasks/language_modeling.mdx index d79be859ef1..b3b6dd75301 100644 --- a/docs/source/tasks/language_modeling.mdx +++ b/docs/source/tasks/language_modeling.mdx @@ -412,7 +412,7 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin For a more in-depth example of how to fine-tune a model for causal language modeling, take a look at the corresponding -[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) -or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling-tf.ipynb). +[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb) +or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb). \ No newline at end of file diff --git a/docs/source/tasks/question_answering.mdx b/docs/source/tasks/question_answering.mdx index 61f81cb3e1e..d5df758db3a 100644 --- a/docs/source/tasks/question_answering.mdx +++ b/docs/source/tasks/question_answering.mdx @@ -267,7 +267,7 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin For a more in-depth example of how to fine-tune a model for question answering, take a look at the corresponding -[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb) -or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering-tf.ipynb). +[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb) +or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb). \ No newline at end of file diff --git a/docs/source/tasks/sequence_classification.mdx b/docs/source/tasks/sequence_classification.mdx index 0908848b9a1..97c98bb8882 100644 --- a/docs/source/tasks/sequence_classification.mdx +++ b/docs/source/tasks/sequence_classification.mdx @@ -208,7 +208,7 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin For a more in-depth example of how to fine-tune a model for text classification, take a look at the corresponding -[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb) -or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification-tf.ipynb). +[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb) +or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb). \ No newline at end of file diff --git a/docs/source/tasks/summarization.mdx b/docs/source/tasks/summarization.mdx index 7083cdce4da..c750e473282 100644 --- a/docs/source/tasks/summarization.mdx +++ b/docs/source/tasks/summarization.mdx @@ -217,7 +217,7 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin For a more in-depth example of how to fine-tune a model for summarization, take a look at the corresponding -[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization.ipynb) -or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization-tf.ipynb). +[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization.ipynb) +or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization-tf.ipynb). \ No newline at end of file diff --git a/docs/source/tasks/token_classification.mdx b/docs/source/tasks/token_classification.mdx index ff26b3af94b..03cd304898b 100644 --- a/docs/source/tasks/token_classification.mdx +++ b/docs/source/tasks/token_classification.mdx @@ -266,7 +266,7 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin For a more in-depth example of how to fine-tune a model for token classification, take a look at the corresponding -[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb) -or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification-tf.ipynb). +[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb) +or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb). \ No newline at end of file diff --git a/docs/source/tasks/translation.mdx b/docs/source/tasks/translation.mdx index 26723241a11..b3ecec6e1ce 100644 --- a/docs/source/tasks/translation.mdx +++ b/docs/source/tasks/translation.mdx @@ -219,7 +219,7 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin For a more in-depth example of how to fine-tune a model for translation, take a look at the corresponding -[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/translation.ipynb) -or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/translation-tf.ipynb). +[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation.ipynb) +or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation-tf.ipynb). \ No newline at end of file diff --git a/docs/source/testing.mdx b/docs/source/testing.mdx index da64bcdc02d..a5e2268092e 100644 --- a/docs/source/testing.mdx +++ b/docs/source/testing.mdx @@ -23,21 +23,21 @@ There are 2 test suites in the repository: ## How transformers are tested 1. Once a PR is submitted it gets tested with 9 CircleCi jobs. Every new commit to that PR gets retested. These jobs - are defined in this [config file](https://github.com/huggingface/transformers-doc2mdx/tree/master/.circleci/config.yml), so that if needed you can reproduce the same + are defined in this [config file](https://github.com/huggingface/transformers/tree/main/.circleci/config.yml), so that if needed you can reproduce the same environment on your machine. These CI jobs don't run `@slow` tests. 2. There are 3 jobs run by [github actions](https://github.com/huggingface/transformers/actions): - - [torch hub integration](https://github.com/huggingface/transformers-doc2mdx/tree/master/.github/workflows/github-torch-hub.yml): checks whether torch hub + - [torch hub integration](https://github.com/huggingface/transformers/tree/main/.github/workflows/github-torch-hub.yml): checks whether torch hub integration works. - - [self-hosted (push)](https://github.com/huggingface/transformers-doc2mdx/tree/master/.github/workflows/self-push.yml): runs fast tests on GPU only on commits on + - [self-hosted (push)](https://github.com/huggingface/transformers/tree/main/.github/workflows/self-push.yml): runs fast tests on GPU only on commits on `main`. It only runs if a commit on `main` has updated the code in one of the following folders: `src`, `tests`, `.github` (to prevent running on added model cards, notebooks, etc.) - - [self-hosted runner](https://github.com/huggingface/transformers-doc2mdx/tree/master/.github/workflows/self-scheduled.yml): runs normal and slow tests on GPU in + - [self-hosted runner](https://github.com/huggingface/transformers/tree/main/.github/workflows/self-scheduled.yml): runs normal and slow tests on GPU in `tests` and `examples`: ```bash @@ -473,8 +473,8 @@ spawns a normal process that then spawns off multiple workers and manages the IO Here are some tests that use it: -- [test_trainer_distributed.py](https://github.com/huggingface/transformers-doc2mdx/tree/master/tests/test_trainer_distributed.py) -- [test_deepspeed.py](https://github.com/huggingface/transformers-doc2mdx/tree/master/tests/deepspeed/test_deepspeed.py) +- [test_trainer_distributed.py](https://github.com/huggingface/transformers/tree/main/tests/test_trainer_distributed.py) +- [test_deepspeed.py](https://github.com/huggingface/transformers/tree/main/tests/deepspeed/test_deepspeed.py) To jump right into the execution point, search for the `execute_subprocess_async` call in those tests. @@ -930,7 +930,7 @@ slow models to do qualitative testing. To see the use of these simply look for * grep tiny tests examples ``` -Here is a an example of a [script](https://github.com/huggingface/transformers-doc2mdx/tree/master/scripts/fsmt/fsmt-make-tiny-model.py) that created the tiny model +Here is a an example of a [script](https://github.com/huggingface/transformers/tree/main/scripts/fsmt/fsmt-make-tiny-model.py) that created the tiny model [stas/tiny-wmt19-en-de](https://huggingface.co/stas/tiny-wmt19-en-de). You can easily adjust it to your specific model's architecture. diff --git a/examples/flax/README.md b/examples/flax/README.md index bf2c2be4596..074aaa292ce 100644 --- a/examples/flax/README.md +++ b/examples/flax/README.md @@ -26,9 +26,9 @@ The following table lists all of our examples on how to use 🤗 Transformers wi | Task | Example model | Example dataset | 🤗 Datasets | Colab |---|---|---|:---:|:---:| -| [**`causal-language-modeling`**](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling) | GPT2 | OSCAR | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/causal_language_modeling_flax.ipynb) -| [**`masked-language-modeling`**](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling) | RoBERTa | OSCAR | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/masked_language_modeling_flax.ipynb) -| [**`text-classification`**](https://github.com/huggingface/transformers/tree/main/examples/flax/text-classification) | BERT | GLUE | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification_flax.ipynb) +| [**`causal-language-modeling`**](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling) | GPT2 | OSCAR | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/causal_language_modeling_flax.ipynb) +| [**`masked-language-modeling`**](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling) | RoBERTa | OSCAR | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/masked_language_modeling_flax.ipynb) +| [**`text-classification`**](https://github.com/huggingface/transformers/tree/main/examples/flax/text-classification) | BERT | GLUE | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_flax.ipynb) ## Intro: JAX and Flax diff --git a/examples/flax/language-modeling/README.md b/examples/flax/language-modeling/README.md index 0c8826c30dc..79f60117881 100644 --- a/examples/flax/language-modeling/README.md +++ b/examples/flax/language-modeling/README.md @@ -123,7 +123,7 @@ This should take less than 18 hours. Training statistics can be accessed on [tfhub.dev](https://tensorboard.dev/experiment/GdYmdak2TWeVz0DDRYOrrg). For a step-by-step walkthrough of how to do masked language modeling in Flax, please have a -look at [this](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/masked_language_modeling_flax.ipynb) google colab. +look at [this](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/masked_language_modeling_flax.ipynb) google colab. ## Causal language modeling @@ -224,7 +224,7 @@ This should take less than ~21 hours. Training statistics can be accessed on [tfhub.de](https://tensorboard.dev/experiment/2zEhLwJ0Qp2FAkI3WVH9qA). For a step-by-step walkthrough of how to do causal language modeling in Flax, please have a -look at [this](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/causal_language_modeling_flax.ipynb) google colab. +look at [this](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/causal_language_modeling_flax.ipynb) google colab. ## T5-like span-masked language modeling diff --git a/examples/pytorch/README.md b/examples/pytorch/README.md index 13fe1bde8ea..4990ba489d6 100644 --- a/examples/pytorch/README.md +++ b/examples/pytorch/README.md @@ -32,18 +32,18 @@ Coming soon! | Task | Example datasets | Trainer support | 🤗 Accelerate | 🤗 Datasets | Colab |---|---|:---:|:---:|:---:|:---:| -| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling) | WikiText-2 | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) -| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/multiple-choice) | SWAG | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/multiple_choice.ipynb) -| [**`question-answering`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering) | SQuAD | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb) -| [**`summarization`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization) | XSum | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization.ipynb) -| [**`text-classification`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification) | GLUE | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb) -| [**`text-generation`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-generation) | - | n/a | - | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb) -| [**`token-classification`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/token-classification) | CoNLL NER | ✅ |✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb) -| [**`translation`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/translation) | WMT | ✅ | ✅ |✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/translation.ipynb) -| [**`speech-recognition`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition) | TIMIT | ✅ | - |✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/speech_recognition.ipynb) -| [**`multi-lingual speech-recognition`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition) | Common Voice | ✅ | - |✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/multi_lingual_speech_recognition.ipynb) -| [**`audio-classification`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification) | SUPERB KS | ✅ | - |✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/audio_classification.ipynb) -| [**`image-classification`**](https://github.com/huggingface/notebooks/blob/master/examples/image_classification.ipynb) | CIFAR-10 | ✅ | - |✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/image_classification.ipynb) +| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling) | WikiText-2 | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb) +| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/multiple-choice) | SWAG | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb) +| [**`question-answering`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering) | SQuAD | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb) +| [**`summarization`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization) | XSum | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization.ipynb) +| [**`text-classification`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification) | GLUE | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb) +| [**`text-generation`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-generation) | - | n/a | - | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/main/notebooks/02_how_to_generate.ipynb) +| [**`token-classification`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/token-classification) | CoNLL NER | ✅ |✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb) +| [**`translation`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/translation) | WMT | ✅ | ✅ |✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation.ipynb) +| [**`speech-recognition`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition) | TIMIT | ✅ | - |✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/speech_recognition.ipynb) +| [**`multi-lingual speech-recognition`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition) | Common Voice | ✅ | - |✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multi_lingual_speech_recognition.ipynb) +| [**`audio-classification`**](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification) | SUPERB KS | ✅ | - |✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/audio_classification.ipynb) +| [**`image-classification`**](https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb) | CIFAR-10 | ✅ | - |✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb) ## Running quick tests diff --git a/notebooks/README.md b/notebooks/README.md index cd284323e4b..38b4b60d934 100644 --- a/notebooks/README.md +++ b/notebooks/README.md @@ -31,51 +31,51 @@ You can open any page of the documentation as a notebook in colab (there is a bu | Notebook | Description | | | |:----------|:-------------|:-------------|------:| -| [Quicktour of the library](https://github.com/huggingface/notebooks/blob/master/transformers_doc/quicktour.ipynb) | A presentation of the various APIs in Transformers |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/quicktour.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/transformers_doc/quicktour.ipynb)| -| [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 |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/task_summary.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/transformers_doc/task_summary.ipynb)| -| [Preprocessing data](https://github.com/huggingface/notebooks/blob/master/transformers_doc/preprocessing.ipynb) | How to use a tokenizer to preprocess your data |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/preprocessing.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/transformers_doc/preprocessing.ipynb)| -| [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 |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/training.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/transformers_doc/training.ipynb)| -| [Summary of the tokenizers](https://github.com/huggingface/notebooks/blob/master/transformers_doc/tokenizer_summary.ipynb) | The differences between the tokenizers algorithm |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/tokenizer_summary.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/transformers_doc/tokenizer_summary.ipynb)| -| [Multilingual models](https://github.com/huggingface/notebooks/blob/master/transformers_doc/multilingual.ipynb) | How to use the multilingual models of the library |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/multilingual.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/transformers_doc/multilingual.ipynb)| -| [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 |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/custom_datasets.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/transformers_doc/custom_datasets.ipynb)| +| [Quicktour of the library](https://github.com/huggingface/notebooks/blob/main/transformers_doc/quicktour.ipynb) | A presentation of the various APIs in Transformers |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/quicktour.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/quicktour.ipynb)| +| [Summary of the tasks](https://github.com/huggingface/notebooks/blob/main/transformers_doc/task_summary.ipynb) | How to run the models of the Transformers library task by task |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/task_summary.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/task_summary.ipynb)| +| [Preprocessing data](https://github.com/huggingface/notebooks/blob/main/transformers_doc/preprocessing.ipynb) | How to use a tokenizer to preprocess your data |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/preprocessing.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/preprocessing.ipynb)| +| [Fine-tuning a pretrained model](https://github.com/huggingface/notebooks/blob/main/transformers_doc/training.ipynb) | How to use the Trainer to fine-tune a pretrained model |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/training.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/training.ipynb)| +| [Summary of the tokenizers](https://github.com/huggingface/notebooks/blob/main/transformers_doc/tokenizer_summary.ipynb) | The differences between the tokenizers algorithm |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/tokenizer_summary.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/tokenizer_summary.ipynb)| +| [Multilingual models](https://github.com/huggingface/notebooks/blob/main/transformers_doc/multilingual.ipynb) | How to use the multilingual models of the library |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/multilingual.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/multilingual.ipynb)| +| [Fine-tuning with custom datasets](https://github.com/huggingface/notebooks/blob/main/transformers_doc/custom_datasets.ipynb) | How to fine-tune a pretrained model on various tasks |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/custom_datasets.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/custom_datasets.ipynb)| ### PyTorch Examples | Notebook | Description | | | |:----------|:-------------|:-------------|------:| -| [Train your tokenizer](https://github.com/huggingface/notebooks/blob/master/examples/tokenizer_training.ipynb) | How to train and use your very own tokenizer |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/tokenizer_training.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/tokenizer_training.ipynb)| -| [Train your language model](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling_from_scratch.ipynb) | How to easily start using transformers |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling_from_scratch.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/language_modeling_from_scratch.ipynb)| -| [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. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb)| -| [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. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/language_modeling.ipynb)| -| [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). | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb)| -| [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. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb)| -| [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. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/multiple_choice.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/multiple_choice.ipynb)| -| [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. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/translation.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/translation.ipynb)| -| [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. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/summarization.ipynb)| -| [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 | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/speech_recognition.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/speech_recognition.ipynb)| -| [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 | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/multi_lingual_speech_recognition.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/multi_lingual_speech_recognition.ipynb)| -| [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 | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/audio_classification.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/audio_classification.ipynb)| -| [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 | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)| -| [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 | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)| -| [How to generate text (with constraints)](https://github.com/huggingface/blog/blob/master/notebooks/53_constrained_beam_search.ipynb)| How to guide language generation with user-provided constraints | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/53_constrained_beam_search.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/blog/blob/master/notebooks/53_constrained_beam_search.ipynb)| -| [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 | -| [How to use Benchmarks](https://github.com/huggingface/notebooks/blob/master/examples/benchmark.ipynb)| How to benchmark models with transformers | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/benchmark.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/benchmark.ipynb)| -| [Reformer](https://github.com/huggingface/blog/blob/master/notebooks/03_reformer.ipynb)| How Reformer pushes the limits of language modeling | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/blog/blob/master/notebooks/03_reformer.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/patrickvonplaten/blog/blob/master/notebooks/03_reformer.ipynb)| -| [How to fine-tune a model on image classification](https://github.com/huggingface/notebooks/blob/master/examples/image_classification.ipynb) | Show how to preprocess the data and fine-tune any pretrained Vision model on Image Classification | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/image_classification.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/image_classification.ipynb)| +| [Train your tokenizer](https://github.com/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb) | How to train and use your very own tokenizer |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb)| +| [Train your language model](https://github.com/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch.ipynb) | How to easily start using transformers |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch.ipynb)| +| [How to fine-tune a model on text classification](https://github.com/huggingface/notebooks/blob/main/examples/text_classification.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on any GLUE task. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb)| +| [How to fine-tune a model on language modeling](https://github.com/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)| +| [How to fine-tune a model on token classification](https://github.com/huggingface/notebooks/blob/main/examples/token_classification.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS). | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb)| +| [How to fine-tune a model on question answering](https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on SQUAD. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb)| +| [How to fine-tune a model on multiple choice](https://github.com/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on SWAG. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb)| +| [How to fine-tune a model on translation](https://github.com/huggingface/notebooks/blob/main/examples/translation.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on WMT. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/translation.ipynb)| +| [How to fine-tune a model on summarization](https://github.com/huggingface/notebooks/blob/main/examples/summarization.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on XSUM. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/summarization.ipynb)| +| [How to fine-tune a speech recognition model in English](https://github.com/huggingface/notebooks/blob/main/examples/speech_recognition.ipynb)| Show how to preprocess the data and fine-tune a pretrained Speech model on TIMIT | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/speech_recognition.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/speech_recognition.ipynb)| +| [How to fine-tune a speech recognition model in any language](https://github.com/huggingface/notebooks/blob/main/examples/multi_lingual_speech_recognition.ipynb)| Show how to preprocess the data and fine-tune a multi-lingually pretrained speech model on Common Voice | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multi_lingual_speech_recognition.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/multi_lingual_speech_recognition.ipynb)| +| [How to fine-tune a model on audio classification](https://github.com/huggingface/notebooks/blob/main/examples/audio_classification.ipynb)| Show how to preprocess the data and fine-tune a pretrained Speech model on Keyword Spotting | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/audio_classification.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/audio_classification.ipynb)| +| [How to train a language model from scratch](https://github.com/huggingface/blog/blob/main/notebooks/01_how_to_train.ipynb)| Highlight all the steps to effectively train Transformer model on custom data | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/main/notebooks/01_how_to_train.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/blog/blob/main/notebooks/01_how_to_train.ipynb)| +| [How to generate text](https://github.com/huggingface/blog/blob/main/notebooks/02_how_to_generate.ipynb)| How to use different decoding methods for language generation with transformers | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/main/notebooks/02_how_to_generate.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/blog/blob/main/notebooks/02_how_to_generate.ipynb)| +| [How to generate text (with constraints)](https://github.com/huggingface/blog/blob/main/notebooks/53_constrained_beam_search.ipynb)| How to guide language generation with user-provided constraints | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/main/notebooks/53_constrained_beam_search.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/blog/blob/main/notebooks/53_constrained_beam_search.ipynb)| +| [How to export model to ONNX](https://github.com/huggingface/notebooks/blob/main/examples/onnx-export.ipynb)| Highlight how to export and run inference workloads through ONNX | +| [How to use Benchmarks](https://github.com/huggingface/notebooks/blob/main/examples/benchmark.ipynb)| How to benchmark models with transformers | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/benchmark.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/benchmark.ipynb)| +| [Reformer](https://github.com/huggingface/blog/blob/main/notebooks/03_reformer.ipynb)| How Reformer pushes the limits of language modeling | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/blog/blob/main/notebooks/03_reformer.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/patrickvonplaten/blog/blob/main/notebooks/03_reformer.ipynb)| +| [How to fine-tune a model on image classification](https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb) | Show how to preprocess the data and fine-tune any pretrained Vision model on Image Classification | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb)| ### TensorFlow Examples | Notebook | Description | | | |:----------|:-------------|:-------------|------:| -| [Train your tokenizer](https://github.com/huggingface/notebooks/blob/master/examples/tokenizer_training.ipynb) | How to train and use your very own tokenizer |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/tokenizer_training.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/tokenizer_training.ipynb)| -| [Train your language model](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling_from_scratch-tf.ipynb) | How to easily start using transformers |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling_from_scratch-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/language_modeling_from_scratch-tf.ipynb)| -| [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. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/text_classification-tf.ipynb)| -| [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. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/language_modeling-tf.ipynb)| -| [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). | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/token_classification-tf.ipynb)| -| [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. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/question_answering-tf.ipynb)| -| [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. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/multiple_choice-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/multiple_choice-tf.ipynb)| -| [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. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/translation-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/translation-tf.ipynb)| -| [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. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/summarization-tf.ipynb)| +| [Train your tokenizer](https://github.com/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb) | How to train and use your very own tokenizer |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb)| +| [Train your language model](https://github.com/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch-tf.ipynb) | How to easily start using transformers |[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch-tf.ipynb)| +| [How to fine-tune a model on text classification](https://github.com/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on any GLUE task. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb)| +| [How to fine-tune a model on language modeling](https://github.com/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb)| +| [How to fine-tune a model on token classification](https://github.com/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS). | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb)| +| [How to fine-tune a model on question answering](https://github.com/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on SQUAD. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb)| +| [How to fine-tune a model on multiple choice](https://github.com/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on SWAG. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb)| +| [How to fine-tune a model on translation](https://github.com/huggingface/notebooks/blob/main/examples/translation-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on WMT. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/translation-tf.ipynb)| +| [How to fine-tune a model on summarization](https://github.com/huggingface/notebooks/blob/main/examples/summarization-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on XSUM. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization-tf.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/summarization-tf.ipynb)| ### Optimum notebooks @@ -83,8 +83,8 @@ You can open any page of the documentation as a notebook in colab (there is a bu | Notebook | Description | | | |:----------|:-------------|:-------------|------:| -| [How to quantize a model with ONNX Runtime for text classification](https://github.com/huggingface/notebooks/blob/master/examples/text_classification_quantization_ort.ipynb)| Show how to apply static and dynamic quantization on a model using [ONNX Runtime](https://github.com/microsoft/onnxruntime) for any GLUE task. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification_quantization_ort.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/text_classification_quantization_ort.ipynb)| -| [How to quantize a model with Intel Neural Compressor for text classification](https://github.com/huggingface/notebooks/blob/master/examples/text_classification_quantization_inc.ipynb)| Show how to apply static, dynamic and aware training quantization on a model using [Intel Neural Compressor (INC)](https://github.com/intel/neural-compressor) for any GLUE task. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification_quantization_inc.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/examples/text_classification_quantization_inc.ipynb)| +| [How to quantize a model with ONNX Runtime for text classification](https://github.com/huggingface/notebooks/blob/main/examples/text_classification_quantization_ort.ipynb)| Show how to apply static and dynamic quantization on a model using [ONNX Runtime](https://github.com/microsoft/onnxruntime) for any GLUE task. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_quantization_ort.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification_quantization_ort.ipynb)| +| [How to quantize a model with Intel Neural Compressor for text classification](https://github.com/huggingface/notebooks/blob/main/examples/text_classification_quantization_inc.ipynb)| Show how to apply static, dynamic and aware training quantization on a model using [Intel Neural Compressor (INC)](https://github.com/intel/neural-compressor) for any GLUE task. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_quantization_inc.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification_quantization_inc.ipynb)| ## Community notebooks: diff --git a/utils/release.py b/utils/release.py index 77c5f764870..5a9c15f6ae0 100644 --- a/utils/release.py +++ b/utils/release.py @@ -66,7 +66,7 @@ def global_version_update(version, patch=False): update_version_in_examples(version) -def clean_master_ref_in_model_list(): +def clean_main_ref_in_model_list(): """Replace the links from main doc tp stable doc in the model list of the README.""" # If the introduction or the conclusion of the list change, the prompts may need to be updated. _start_prompt = "🤗 Transformers currently provides the following architectures" @@ -124,7 +124,7 @@ def pre_release_work(patch=False): global_version_update(version, patch=patch) if not patch: print("Cleaning main README") - clean_master_ref_in_model_list() + clean_main_ref_in_model_list() def post_release_work():