Updates the default branch from master to main (#16326)

* Updates the default branch from master to main

* Links from `master` to `main`

* Typo

* Update examples/flax/README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/testing.mdx

@@ -34,7 +34,7 @@ There are 2 test suites in the repository:
      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
-     `master`. It only runs if a commit on `master` has updated the code in one of the following folders: `src`,
+     `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
@@ -1161,9 +1161,9 @@ pytest tests/test_logging.py -W error::UserWarning --pdb
 To trigger a self-push workflow CI job, you must:
 
 1. Create a new branch on `transformers` origin (not a fork!).
-2. The branch name has to start with either `ci_` or `ci-` (`master` triggers it too, but we can't do PRs on
-   `master`). It also gets triggered only for specific paths - you can find the up-to-date definition in case it
-   changed since this document has been written [here](https://github.com/huggingface/transformers/blob/master/.github/workflows/self-push.yml) under *push:*
+2. The branch name has to start with either `ci_` or `ci-` (`main` triggers it too, but we can't do PRs on
+   `main`). It also gets triggered only for specific paths - you can find the up-to-date definition in case it
+   changed since this document has been written [here](https://github.com/huggingface/transformers/blob/main/.github/workflows/self-push.yml) under *push:*
 3. Create a PR from this branch.
 4. Then you can see the job appear [here](https://github.com/huggingface/transformers/actions/workflows/self-push.yml). It may not run right away if there
    is a backlog.

docs/source/training.mdx

@@ -368,7 +368,7 @@ Just like how you need to add an evaluation function to [`Trainer`], you need to
 
 For more fine-tuning examples, refer to:
 
-- [🤗 Transformers Examples](https://github.com/huggingface/transformers/tree/master/examples) includes scripts
+- [🤗 Transformers Examples](https://github.com/huggingface/transformers/tree/main/examples) includes scripts
   to train common NLP tasks in PyTorch and TensorFlow.
 
 - [🤗 Transformers Notebooks](notebooks) contains various notebooks on how to fine-tune a model for specific tasks in PyTorch and TensorFlow.

docs/source_es/training.mdx

@@ -361,7 +361,7 @@ De la misma manera que necesitas añadir una función de evaluación al [`Traine
 
 Para más ejemplos de fine-tuning consulta:
 
-- [🤗 Transformers Examples](https://github.com/huggingface/transformers/tree/master/examples) incluye scripts 
+- [🤗 Transformers Examples](https://github.com/huggingface/transformers/tree/main/examples) incluye scripts
   para entrenar tareas comunes de NLP en PyTorch y TensorFlow.
 
 - [🤗 Transformers Notebooks](notebooks) contiene varios notebooks sobre cómo aplicar fine-tuning a un modelo para tareas específicas en PyTorch y TensorFlow.

examples/README.md

@@ -15,9 +15,9 @@ limitations under the License.
 
 # Examples
 
-We host a wide range of example scripts for multiple learning frameworks. Simply choose your favorite: [TensorFlow](https://github.com/huggingface/transformers/tree/master/examples/tensorflow), [PyTorch](https://github.com/huggingface/transformers/tree/master/examples/pytorch) or [JAX/Flax](https://github.com/huggingface/transformers/tree/master/examples/flax).
+We host a wide range of example scripts for multiple learning frameworks. Simply choose your favorite: [TensorFlow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow), [PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch) or [JAX/Flax](https://github.com/huggingface/transformers/tree/main/examples/flax).
 
-We also have some [research projects](https://github.com/huggingface/transformers/tree/master/examples/research_projects), as well as some [legacy examples](https://github.com/huggingface/transformers/tree/master/examples/legacy). Note that unlike the main examples these are not actively maintained, and may require specific older versions of dependencies in order to run. 
+We also have some [research projects](https://github.com/huggingface/transformers/tree/main/examples/research_projects), as well as some [legacy examples](https://github.com/huggingface/transformers/tree/main/examples/legacy). Note that unlike the main examples these are not actively maintained, and may require specific older versions of dependencies in order to run. 
 
 While we strive to present as many use cases as possible, the example scripts are just that - examples. It is expected that they won't work out-of-the box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs. To help you with that, most of the examples fully expose the preprocessing of the data, allowing you to tweak and edit them as required.
 

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/master/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/master/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/master/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/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)
 
 ## Intro: JAX and Flax
 
@@ -66,7 +66,7 @@ Porting models from PyTorch to JAX/Flax is an ongoing effort.
 Feel free to reach out if you are interested in contributing a model in JAX/Flax -- we'll 
 be adding a guide for porting models from PyTorch in the upcoming few weeks.
 
-For a complete overview of models that are supported in JAX/Flax, please have a look at [this](https://huggingface.co/transformers/master/index.html#supported-frameworks) table.
+For a complete overview of models that are supported in JAX/Flax, please have a look at [this](https://huggingface.co/transformers/main/index.html#supported-frameworks) table.
 
 Over 3000 pretrained checkpoints are supported in JAX/Flax as of May 2021.
 Click [here](https://huggingface.co/models?filter=jax) to see the full list on the 🤗 hub.

examples/flax/language-modeling/README.md

@@ -249,7 +249,7 @@ cd ./norwegian-t5-base
 
 In the first step, we train a tokenizer to efficiently process the text input for the model. 
 We make use of the [tokenizers](https://github.com/huggingface/tokenizers) library to train 
-a sentencepiece unigram tokenizer as shown in [t5_tokenizer_model.py](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling/t5_tokenizer_model.py) 
+a sentencepiece unigram tokenizer as shown in [t5_tokenizer_model.py](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling/t5_tokenizer_model.py) 
 which is heavily inspired from [yandex-research/DeDLOC's tokenizer model](https://github.com/yandex-research/DeDLOC/blob/5c994bc64e573702a9a79add3ecd68b38f14b548/sahajbert/tokenizer/tokenizer_model.py) .
 
 The tokenizer is trained on the complete Norwegian dataset of OSCAR

examples/flax/question-answering/README.md

@@ -16,7 +16,7 @@ limitations under the License.
 
 # Question Answering examples
 
-Based on the script [`run_qa.py`](https://github.com/huggingface/transformers/blob/master/examples/flax/question-answering/run_qa.py).
+Based on the script [`run_qa.py`](https://github.com/huggingface/transformers/blob/main/examples/flax/question-answering/run_qa.py).
 
 **Note:** This script only works with models that have a fast tokenizer (backed by the 🤗 Tokenizers library) as it
 uses special features of those tokenizers. You can check if your favorite model has a fast tokenizer in

examples/flax/text-classification/README.md

@@ -18,7 +18,7 @@ limitations under the License.
 
 ## GLUE tasks
 
-Based on the script [`run_flax_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/flax/text-classification/run_flax_glue.py).
+Based on the script [`run_flax_glue.py`](https://github.com/huggingface/transformers/blob/main/examples/flax/text-classification/run_flax_glue.py).
 
 Fine-tuning the library models for sequence classification on the GLUE benchmark: [General Language Understanding
 Evaluation](https://gluebenchmark.com/). This script can fine-tune any of the models on the [hub](https://huggingface.co/models)  and can also be used for a 
@@ -85,7 +85,7 @@ website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/f
 ### Runtime evaluation
 
 We also ran each task once on a single V100 GPU, 8 V100 GPUs, and 8 Cloud v3 TPUs and report the
-overall training time below. For comparison we ran Pytorch's [run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) on a single GPU (last column).
+overall training time below. For comparison we ran Pytorch's [run_glue.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py) on a single GPU (last column).
 
 
 | Task  | TPU v3-8  | 8 GPU      | [1 GPU](https://tensorboard.dev/experiment/mkPS4Zh8TnGe1HB6Yzwj4Q)  | 1 GPU (Pytorch) |

examples/legacy/seq2seq/README.md

@@ -17,7 +17,7 @@ limitations under the License.
 # Sequence-to-Sequence Training and Evaluation
 
 This directory contains examples for finetuning and evaluating transformers on summarization and translation tasks.
-For deprecated `bertabs` instructions, see [`bertabs/README.md`](https://github.com/huggingface/transformers/blob/master/examples/research_projects/bertabs/README.md).
+For deprecated `bertabs` instructions, see [`bertabs/README.md`](https://github.com/huggingface/transformers/blob/main/examples/research_projects/bertabs/README.md).
 
 ### Supported Architectures
 
@@ -73,7 +73,7 @@ export DATA_DIR=${PWD}/wmt_en_de
 #### FSMT datasets (wmt)
 
 Refer to the scripts starting with `eval_` under:
-https://github.com/huggingface/transformers/tree/master/scripts/fsmt
+https://github.com/huggingface/transformers/tree/main/scripts/fsmt
 
 #### Pegasus (multiple datasets)
 

examples/legacy/token-classification/README.md

 ## Token classification
 
-Based on the scripts [`run_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/legacy/token-classification/run_ner.py).
+Based on the scripts [`run_ner.py`](https://github.com/huggingface/transformers/blob/main/examples/legacy/token-classification/run_ner.py).
 
 The following examples are covered in this section:
 

examples/pytorch/README.md

@@ -32,17 +32,17 @@ Coming soon!
 
 | Task | Example datasets | Trainer support | 🤗 Accelerate | 🤗 Datasets | Colab
 |---|---|:---:|:---:|:---:|:---:|
-| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/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/master/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/master/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/master/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/master/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/master/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/master/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/master/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/master/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/master/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/master/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)
+| [**`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)
 
 
@@ -123,7 +123,7 @@ training with PyTorch 1.6.0 or latest, or by installing the [Apex](https://githu
 versions. Just add the flag `--fp16` to your command launching one of the scripts mentioned above!
 
 Using mixed precision training usually results in 2x-speedup for training with the same final results (as shown in
-[this table](https://github.com/huggingface/transformers/tree/master/examples/text-classification#mixed-precision-training)
+[this table](https://github.com/huggingface/transformers/tree/main/examples/text-classification#mixed-precision-training)
 for text classification).
 
 ## Running on TPUs
@@ -134,7 +134,7 @@ When using PyTorch, we support TPUs thanks to `pytorch/xla`. For more context an
 very detailed [pytorch/xla README](https://github.com/pytorch/xla/blob/master/README.md).
 
 In this repo, we provide a very simple launcher script named
-[xla_spawn.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/xla_spawn.py) that lets you run our
+[xla_spawn.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/xla_spawn.py) that lets you run our
 example scripts on multiple TPU cores without any boilerplate. Just pass a `--num_cores` flag to this script, then your
 regular training script with its arguments (this is similar to the `torch.distributed.launch` helper for
 `torch.distributed`):

examples/pytorch/audio-classification/README.md

@@ -19,9 +19,9 @@ limitations under the License.
 The following examples showcase how to fine-tune `Wav2Vec2` for audio classification using PyTorch.
 
 Speech recognition models that have been pretrained in unsupervised fashion on audio data alone, 
-*e.g.* [Wav2Vec2](https://huggingface.co/transformers/master/model_doc/wav2vec2.html), 
-[HuBERT](https://huggingface.co/transformers/master/model_doc/hubert.html), 
-[XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html), have shown to require only 
+*e.g.* [Wav2Vec2](https://huggingface.co/transformers/main/model_doc/wav2vec2.html), 
+[HuBERT](https://huggingface.co/transformers/main/model_doc/hubert.html), 
+[XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html), have shown to require only 
 very little annotated data to yield good performance on speech classification datasets.
 
 ## Single-GPU 

examples/pytorch/language-modeling/README.md

@@ -24,7 +24,7 @@ objectives in our [model summary](https://huggingface.co/transformers/model_summ
 
 There are two sets of scripts provided. The first set leverages the Trainer API. The second set with `no_trainer` in the suffix uses a custom training loop and leverages the 🤗 Accelerate library . Both sets use the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
 
-**Note:** The old script `run_language_modeling.py` is still available [here](https://github.com/huggingface/transformers/blob/master/examples/legacy/run_language_modeling.py).
+**Note:** The old script `run_language_modeling.py` is still available [here](https://github.com/huggingface/transformers/blob/main/examples/legacy/run_language_modeling.py).
 
 The following examples, will run on datasets hosted on our [hub](https://huggingface.co/datasets) or with your own
 text files for training and validation. We give examples of both below.

examples/pytorch/multiple-choice/README.md

@@ -41,7 +41,7 @@ eval_loss = 0.44457291918821606
 
 ## With Accelerate
 
-Based on the script [run_swag_no_trainer.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/multiple-choice/run_swag_no_trainer.py).
+Based on the script [run_swag_no_trainer.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/multiple-choice/run_swag_no_trainer.py).
 
 Like `run_swag.py`, this script allows you to fine-tune any of the models on the [hub](https://huggingface.co/models) (as long as its architecture as a `ForMultipleChoice` version in the library) on
 the SWAG dataset or your own data in a csv or a JSON file. The main difference is that this

examples/pytorch/question-answering/README.md

@@ -21,17 +21,17 @@ like SQuAD.
 
 ## Trainer-based scripts
 
-The [`run_qa.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py),
-[`run_qa_beam_search.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa_beam_search.py) and [`run_seq2seq_qa.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py) leverage the 🤗 [Trainer](https://huggingface.co/transformers/main_classes/trainer.html) for fine-tuning.
+The [`run_qa.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa.py),
+[`run_qa_beam_search.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa_beam_search.py) and [`run_seq2seq_qa.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_seq2seq_qa.py) leverage the 🤗 [Trainer](https://huggingface.co/transformers/main_classes/trainer.html) for fine-tuning.
 
 ### Fine-tuning BERT on SQuAD1.0
 
-The [`run_qa.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py) script
+The [`run_qa.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa.py) script
 allows to fine-tune any model from our [hub](https://huggingface.co/models) (as long as its architecture has a `ForQuestionAnswering` version in the library) on a question-answering dataset (such as SQuAD, or any other QA dataset available in the `datasets` library, or your own csv/jsonlines files) as long as they are structured the same way as SQuAD. You might need to tweak the data processing inside the script if your data is structured differently.
 
 **Note:** This script only works with models that have a fast tokenizer (backed by the 🤗 Tokenizers library) as it
 uses special features of those tokenizers. You can check if your favorite model has a fast tokenizer in
-[this table](https://huggingface.co/transformers/index.html#supported-frameworks), if it doesn't you can still use the old version of the script which can be found [here](https://github.com/huggingface/transformers/tree/master/examples/legacy/question-answering).
+[this table](https://huggingface.co/transformers/index.html#supported-frameworks), if it doesn't you can still use the old version of the script which can be found [here](https://github.com/huggingface/transformers/tree/main/examples/legacy/question-answering).
 
 Note that if your dataset contains samples with no possible answers (like SQuAD version 2), you need to pass along the flag `--version_2_with_negative`.
 
@@ -61,7 +61,7 @@ exact_match = 81.22
 
 ### Fine-tuning XLNet with beam search on SQuAD
 
-The [`run_qa_beam_search.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa_beam_search.py) script is only meant to fine-tune XLNet, which is a special encoder-only Transformer model. The example code below fine-tunes XLNet on the SQuAD1.0 and SQuAD2.0 datasets.
+The [`run_qa_beam_search.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa_beam_search.py) script is only meant to fine-tune XLNet, which is a special encoder-only Transformer model. The example code below fine-tunes XLNet on the SQuAD1.0 and SQuAD2.0 datasets.
 
 #### Command for SQuAD1.0:
 
@@ -104,7 +104,7 @@ python run_qa_beam_search.py \
 
 ### Fine-tuning T5 on SQuAD2.0
 
-The [`run_seq2seq_qa.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py) script is meant for encoder-decoder (also called seq2seq) Transformer models, such as T5 or BART. These
+The [`run_seq2seq_qa.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_seq2seq_qa.py) script is meant for encoder-decoder (also called seq2seq) Transformer models, such as T5 or BART. These
 models are generative, rather than discriminative. This means that they learn to generate the correct answer, rather than predicting the start and end position of the tokens of the answer.
 
 This example code fine-tunes T5 on the SQuAD2.0 dataset.

examples/pytorch/speech-pretraining/README.md

@@ -19,9 +19,9 @@ limitations under the License.
 
 ## Wav2Vec2 Speech Pre-Training
 
-The script [`run_speech_wav2vec2_pretraining_no_trainer.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py) can be used to pre-train a [Wav2Vec2](https://huggingface.co/transformers/model_doc/wav2vec2.html?highlight=wav2vec2) model from scratch.
+The script [`run_speech_wav2vec2_pretraining_no_trainer.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py) can be used to pre-train a [Wav2Vec2](https://huggingface.co/transformers/model_doc/wav2vec2.html?highlight=wav2vec2) model from scratch.
 
-In the script [`run_speech_wav2vec2_pretraining_no_trainer`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py), a Wav2Vec2 model is pre-trained on audio data alone using [Wav2Vec2's contrastive loss objective](https://arxiv.org/abs/2006.11477).
+In the script [`run_speech_wav2vec2_pretraining_no_trainer`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py), a Wav2Vec2 model is pre-trained on audio data alone using [Wav2Vec2's contrastive loss objective](https://arxiv.org/abs/2006.11477).
 
 The following examples show how to fine-tune a `"base"`-sized Wav2Vec2 model as well as a `"large"`-sized Wav2Vec2 model using [`accelerate`](https://github.com/huggingface/accelerate).
 

examples/pytorch/speech-recognition/README.md

@@ -34,10 +34,10 @@ limitations under the License.
 
 ## Connectionist Temporal Classification
 
-The script [`run_speech_recognition_ctc.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py) can be used to fine-tune any pretrained [Connectionist Temporal Classification Model](https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCTC) for automatic speech 
+The script [`run_speech_recognition_ctc.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py) can be used to fine-tune any pretrained [Connectionist Temporal Classification Model](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForCTC) for automatic speech 
 recognition on one of the [official speech recognition datasets](https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition) or a custom dataset.
 
-Speech recognition models that have been pretrained in unsupervised fashion on audio data alone, *e.g.* [Wav2Vec2](https://huggingface.co/transformers/master/model_doc/wav2vec2.html), [HuBERT](https://huggingface.co/transformers/master/model_doc/hubert.html), [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html), have shown to require only 
+Speech recognition models that have been pretrained in unsupervised fashion on audio data alone, *e.g.* [Wav2Vec2](https://huggingface.co/transformers/main/model_doc/wav2vec2.html), [HuBERT](https://huggingface.co/transformers/main/model_doc/hubert.html), [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html), have shown to require only 
 very little annotated data to yield good performance on automatic speech recognition datasets.
 
 In the script [`run_speech_recognition_ctc`], we first create a vocabulary from all unique characters of both the training data and evaluation data. Then, we preprocesses the speech recognition dataset, which includes correct resampling, normalization and padding. Finally, the pretrained speech recognition model is fine-tuned on the annotated speech recognition datasets using CTC loss.
@@ -58,7 +58,7 @@ If the environment variable is not set, the training script might freeze, *i.e.*
 
 ### Single GPU CTC
 
-The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using a single GPU in half-precision.
+The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using a single GPU in half-precision.
 
 ```bash
 python run_speech_recognition_ctc.py \
@@ -93,7 +93,7 @@ of **0.35**.
 
 ### Multi GPU CTC
 
-The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using 8 GPUs in half-precision.
+The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using 8 GPUs in half-precision.
 
 ```bash
 python -m torch.distributed.launch \
@@ -131,7 +131,7 @@ of **0.36**.
 ### Multi GPU CTC with Dataset Streaming
 
 The following command shows how to use [Dataset Streaming mode](https://huggingface.co/docs/datasets/dataset_streaming.html)
-to fine-tune [XLS-R](https://huggingface.co/transformers/master/model_doc/xls_r.html) 
+to fine-tune [XLS-R](https://huggingface.co/transformers/main/model_doc/xls_r.html) 
 on [Common Voice](https://huggingface.co/datasets/common_voice) using 4 GPUs in half-precision.
 
 Streaming mode imposes several constraints on training:
@@ -245,11 +245,11 @@ they can serve as a baseline to improve upon.
 
 ## Sequence to Sequence
 
-The script [`run_speech_recognition_seq2seq.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) can be used to fine-tune any [Speech Sequence-to-Sequence Model](https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForSpeechSeq2Seq) for automatic speech 
+The script [`run_speech_recognition_seq2seq.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) can be used to fine-tune any [Speech Sequence-to-Sequence Model](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForSpeechSeq2Seq) for automatic speech 
 recognition on one of the [official speech recognition datasets](https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition) or a custom dataset.
 
-A very common use case is to leverage a pretrained speech [encoding model](https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModel),
-*e.g.* [Wav2Vec2](https://huggingface.co/transformers/master/model_doc/wav2vec2.html), [HuBERT](https://huggingface.co/transformers/master/model_doc/hubert.html), [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html) with a pretrained [text decoding model](https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModel), *e.g.* [Bart](https://huggingface.co/docs/transformers/master/en/model_doc/bart#transformers.BartForCausalLM) to create a [SpeechEnocderDecoderModel](https://huggingface.co/docs/transformers/master/en/model_doc/speechencoderdecoder#speech-encoder-decoder-models).
+A very common use case is to leverage a pretrained speech [encoding model](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModel),
+*e.g.* [Wav2Vec2](https://huggingface.co/transformers/main/model_doc/wav2vec2.html), [HuBERT](https://huggingface.co/transformers/main/model_doc/hubert.html), [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) with a pretrained [text decoding model](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModel), *e.g.* [Bart](https://huggingface.co/docs/transformers/main/en/model_doc/bart#transformers.BartForCausalLM) to create a [SpeechEnocderDecoderModel](https://huggingface.co/docs/transformers/main/en/model_doc/speechencoderdecoder#speech-encoder-decoder-models).
 Consequently, the warm-started Speech-Encoder-Decoder model can be fine-tuned in 
 this script.
 
@@ -314,7 +314,7 @@ Having warm-started the speech-encoder-decoder model `<your-user-name>/wav2vec2-
 
 In the script [`run_speech_recognition_seq2seq`], we load the warm-started model, 
 the feature extractor, and the tokenizer, process a speech recognition dataset, 
-and then make use of the [`Seq2SeqTrainer`](https://huggingface.co/docs/transformers/master/en/main_classes/trainer#transformers.Seq2SeqTrainer).
+and then make use of the [`Seq2SeqTrainer`](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.Seq2SeqTrainer).
 Note that it is important to also align the decoder's vocabulary with 
 the speech transcriptions of the dataset. *E.g.* the [`Librispeech`](https://huggingface.co/datasets/librispeech_asr) has only captilized letters in the transcriptions,
 whereas BART was pretrained mostly on normalized text. Thus it is recommended to add
@@ -337,7 +337,7 @@ If the environment variable is not set, the training script might freeze, *i.e.*
 
 ### Single GPU Seq2Seq
 
-The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using a single GPU in half-precision.
+The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using a single GPU in half-precision.
 
 ```bash
 python run_speech_recognition_seq2seq.py \
@@ -379,7 +379,7 @@ cross-entropy loss of **0.405** and word error rate of **0.0728**.
 
 ### Multi GPU Seq2Seq
 
-The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using 8 GPUs in half-precision.
+The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using 8 GPUs in half-precision.
 
 ```bash
 python -m torch.distributed.launch \

examples/pytorch/summarization/README.md

@@ -18,8 +18,8 @@ limitations under the License.
 
 This directory contains examples for finetuning and evaluating transformers on summarization  tasks.
 Please tag @patil-suraj with any issues/unexpected behaviors, or send a PR!
-For deprecated `bertabs` instructions, see [`bertabs/README.md`](https://github.com/huggingface/transformers/blob/master/examples/research_projects/bertabs/README.md).
-For the old `finetune_trainer.py` and related utils, see [`examples/legacy/seq2seq`](https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq).
+For deprecated `bertabs` instructions, see [`bertabs/README.md`](https://github.com/huggingface/transformers/blob/main/examples/research_projects/bertabs/README.md).
+For the old `finetune_trainer.py` and related utils, see [`examples/legacy/seq2seq`](https://github.com/huggingface/transformers/blob/main/examples/legacy/seq2seq).
 
 ### Supported Architectures
 
@@ -137,7 +137,7 @@ And as with the CSV files, you can specify which values to select from the file,
 
 ## With Accelerate
 
-Based on the script [`run_summarization_no_trainer.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/summarization/run_summarization_no_trainer.py).
+Based on the script [`run_summarization_no_trainer.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/summarization/run_summarization_no_trainer.py).
 
 Like `run_summarization.py`, this script allows you to fine-tune any of the models supported on a
 summarization task, the main difference is that this

examples/pytorch/text-classification/README.md

@@ -18,7 +18,7 @@ limitations under the License.
 
 ## GLUE tasks
 
-Based on the script [`run_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py).
+Based on the script [`run_glue.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py).
 
 Fine-tuning the library models for sequence classification on the GLUE benchmark: [General Language Understanding
 Evaluation](https://gluebenchmark.com/). This script can fine-tune any of the models on the [hub](https://huggingface.co/models)
@@ -103,7 +103,7 @@ Using mixed precision training usually results in 2x-speedup for training with t
 
 ## PyTorch version, no Trainer
 
-Based on the script [`run_glue_no_trainer.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue_no_trainer.py).
+Based on the script [`run_glue_no_trainer.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue_no_trainer.py).
 
 Like `run_glue.py`, this script allows you to fine-tune any of the models on the [hub](https://huggingface.co/models) on a
 text classification task, either a GLUE task or your own data in a csv or a JSON file. The main difference is that this

examples/pytorch/text-generation/README.md

@@ -16,7 +16,7 @@ limitations under the License.
 
 ## Language generation
 
-Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-generation/run_generation.py).
+Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-generation/run_generation.py).
 
 Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL, XLNet, CTRL.
 A similar script is used for our official demo [Write With Transfomer](https://transformer.huggingface.co), where you