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/debugging.mdx

@@ -17,7 +17,7 @@ specific language governing permissions and limitations under the License.
 When training or inferencing with `DistributedDataParallel` and multiple GPU, if you run into issue of inter-communication between processes and/or nodes, you can use the following script to diagnose network issues.
 
 ```bash
-wget https://raw.githubusercontent.com/huggingface/transformers/master/scripts/distributed/torch-distributed-gpu-test.py
+wget https://raw.githubusercontent.com/huggingface/transformers/main/scripts/distributed/torch-distributed-gpu-test.py
 ```
 
 For example to test how 2 GPUs interact do:

docs/source/index.mdx

@@ -82,7 +82,7 @@ conversion utilities for the following models.
 1. **[DeiT](model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
 1. **[DETR](model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
 1. **[DialoGPT](model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
-1. **[DistilBERT](model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation) and a German version of DistilBERT.
+1. **[DistilBERT](model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) and a German version of DistilBERT.
 1. **[DPR](model_doc/dpr)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 1. **[EncoderDecoder](model_doc/encoder-decoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
 1. **[ELECTRA](model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
@@ -150,15 +150,15 @@ conversion utilities for the following models.
 1. **[VisualBERT](model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
 1. **[WavLM](model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
 1. **[Wav2Vec2](model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
-1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/master/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
-1. **[XGLM](https://huggingface.co/docs/master/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
+1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/main/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
+1. **[XGLM](https://huggingface.co/docs/main/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
 1. **[XLM](model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
 1. **[XLM-ProphetNet](model_doc/xlm-prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
 1. **[XLM-RoBERTa](model_doc/xlm-roberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
 1. **[XLM-RoBERTa-XL](model_doc/xlm-roberta-xl)** (from Facebook AI), released together with the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
 1. **[XLNet](model_doc/xlnet)** (from Google/CMU) released with the paper [​XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
 1. **[XLSR-Wav2Vec2](model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
-1. **[XLS-R](https://huggingface.co/docs/master/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
+1. **[XLS-R](https://huggingface.co/docs/main/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
 1. **[YOSO](model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
 
 

docs/source/installation.mdx

@@ -84,7 +84,7 @@ Install 🤗 Transformers from source with the following command:
 pip install git+https://github.com/huggingface/transformers
 ```
 
-This command installs the bleeding edge `master` version rather than the latest `stable` version. The `master` version is useful for staying up-to-date with the latest developments. For instance, if a bug has been fixed since the last official release but a new release hasn't been rolled out yet. However, this means the `master` version may not always be stable. We strive to keep the `master` version operational, and most issues are usually resolved within a few hours or a day. If you run into a problem, please open an [Issue](https://github.com/huggingface/transformers/issues) so we can fix it even sooner!
+This command installs the bleeding edge `main` version rather than the latest `stable` version. The `main` version is useful for staying up-to-date with the latest developments. For instance, if a bug has been fixed since the last official release but a new release hasn't been rolled out yet. However, this means the `main` version may not always be stable. We strive to keep the `main` version operational, and most issues are usually resolved within a few hours or a day. If you run into a problem, please open an [Issue](https://github.com/huggingface/transformers/issues) so we can fix it even sooner!
 
 Check if 🤗 Transformers has been properly installed by running the following command:
 
@@ -96,7 +96,7 @@ python -c "from transformers import pipeline; print(pipeline('sentiment-analysis
 
 You will need an editable install if you'd like to:
 
-* Use the `master` version of the source code.
+* Use the `main` version of the source code.
 * Contribute to 🤗 Transformers and need to test changes in the code.
 
 Clone the repository and install 🤗 Transformers with the following commands:
@@ -122,7 +122,7 @@ cd ~/transformers/
 git pull
 ```
 
-Your Python environment will find the `master` version of 🤗 Transformers on the next run.
+Your Python environment will find the `main` version of 🤗 Transformers on the next run.
 
 ## Install with conda
 

docs/source/main_classes/deepspeed.mdx

@@ -1761,7 +1761,7 @@ In your report please always include:
 
 5. Unless it's impossible please always use a standard dataset that we can use and not something custom.
 
-6. If possible try to use one of the existing [examples](https://github.com/huggingface/transformers/tree/master/examples/pytorch) to reproduce the problem with.
+6. If possible try to use one of the existing [examples](https://github.com/huggingface/transformers/tree/main/examples/pytorch) to reproduce the problem with.
 
 Things to consider:
 
@@ -1985,7 +1985,7 @@ train_batch_size = 1 * world_size
 # - which params should remain on gpus - the larger the value the smaller the offload size
 #
 # For indepth info on Deepspeed config see
-# https://huggingface.co/docs/transformers/master/main_classes/deepspeed
+# https://huggingface.co/docs/transformers/main/main_classes/deepspeed
 
 # keeping the same format as json for consistency, except it uses lower case for true/false
 # fmt: off

docs/source/main_classes/processors.mdx

@@ -82,7 +82,7 @@ This library hosts the processor to load the XNLI data:
 
 Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
 
-An example using these processors is given in the [run_xnli.py](https://github.com/huggingface/transformers/tree/master/examples/legacy/text-classification/run_xnli.py) script.
+An example using these processors is given in the [run_xnli.py](https://github.com/huggingface/transformers/tree/main/examples/legacy/text-classification/run_xnli.py) script.
 
 
 ## SQuAD
@@ -156,4 +156,4 @@ features = squad_convert_examples_to_features(
 )
 ```
 
-Another example using these processors is given in the [run_squad.py](https://github.com/huggingface/transformers/tree/master/examples/legacy/question-answering/run_squad.py) script.
+Another example using these processors is given in the [run_squad.py](https://github.com/huggingface/transformers/tree/main/examples/legacy/question-answering/run_squad.py) script.

docs/source/model_doc/bart.mdx

@@ -38,7 +38,7 @@ This model was contributed by [sshleifer](https://huggingface.co/sshleifer). The
 ### Examples
 
 - Examples and scripts for fine-tuning BART and other models for sequence to sequence tasks can be found in
-  [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).
 - An example of how to train [`BartForConditionalGeneration`] with a Hugging Face `datasets`
   object can be found in this [forum discussion](https://discuss.huggingface.co/t/train-bart-for-conditional-generation-e-g-summarization/1904).
 - [Distilled checkpoints](https://huggingface.co/models?search=distilbart) are described in this [paper](https://arxiv.org/abs/2010.13002).

docs/source/model_doc/bigbird_pegasus.mdx

@@ -46,7 +46,7 @@ Tips:
 - Sequence length must be divisible by block size.
 - Current implementation supports only **ITC**.
 - Current implementation doesn't support **num_random_blocks = 0**.
-- BigBirdPegasus uses the [PegasusTokenizer](https://github.com/huggingface/transformers/blob/master/src/transformers/models/pegasus/tokenization_pegasus.py).
+- BigBirdPegasus uses the [PegasusTokenizer](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pegasus/tokenization_pegasus.py).
 
 The original code can be found [here](https://github.com/google-research/bigbird).
 

docs/source/model_doc/distilbert.mdx

@@ -43,7 +43,7 @@ Tips:
   necessary though, just let us know if you need this option.
 
 This model was contributed by [victorsanh](https://huggingface.co/victorsanh). This model jax version was
-contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation).
+contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation).
 
 
 ## DistilBertConfig

docs/source/model_doc/marian.mdx

@@ -48,8 +48,8 @@ Translations should be similar, but not identical to output in the test set link
 
 - Since Marian models are smaller than many other translation models available in the library, they can be useful for
   fine-tuning experiments and integration tests.
-- [Fine-tune on GPU](https://github.com/huggingface/transformers/blob/master/examples/research_projects/seq2seq-distillation/train_distil_marian_enro_teacher.sh)
-- [Fine-tune on GPU with pytorch-lightning](https://github.com/huggingface/transformers/blob/master/examples/research_projects/seq2seq-distillation/train_distil_marian_no_teacher.sh)
+- [Fine-tune on GPU](https://github.com/huggingface/transformers/blob/main/examples/research_projects/seq2seq-distillation/train_distil_marian_enro_teacher.sh)
+- [Fine-tune on GPU with pytorch-lightning](https://github.com/huggingface/transformers/blob/main/examples/research_projects/seq2seq-distillation/train_distil_marian_no_teacher.sh)
 
 ## Multilingual Models
 

docs/source/model_doc/pegasus.mdx

@@ -43,8 +43,8 @@ All the [checkpoints](https://huggingface.co/models?search=pegasus) are fine-tun
 
 ### Examples
 
-- [Script](https://github.com/huggingface/transformers/tree/master/examples/research_projects/seq2seq-distillation/finetune_pegasus_xsum.sh) to fine-tune pegasus
-  on the XSUM dataset. Data download instructions at [examples/pytorch/summarization/](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization/README.md).
+- [Script](https://github.com/huggingface/transformers/tree/main/examples/research_projects/seq2seq-distillation/finetune_pegasus_xsum.sh) to fine-tune pegasus
+  on the XSUM dataset. Data download instructions at [examples/pytorch/summarization/](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization/README.md).
 - FP16 is not supported (help/ideas on this appreciated!).
 - The adafactor optimizer is recommended for pegasus fine-tuning.
 

docs/source/model_doc/retribert.mdx

@@ -19,7 +19,7 @@ Question Answering](https://yjernite.github.io/lfqa.html). RetriBERT is a small
 pair of BERT encoders with lower-dimension projection for dense semantic indexing of text.
 
 This model was contributed by [yjernite](https://huggingface.co/yjernite). Code to train and use the model can be
-found [here](https://github.com/huggingface/transformers/tree/master/examples/research-projects/distillation).
+found [here](https://github.com/huggingface/transformers/tree/main/examples/research-projects/distillation).
 
 
 ## RetriBertConfig

docs/source/model_doc/t5.mdx

@@ -104,7 +104,7 @@ language modeling head on top of the decoder.
   loss = model(input_ids=input_ids, labels=labels).loss
   ```
 
-  If you're interested in pre-training T5 on a new corpus, check out the [run_t5_mlm_flax.py](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling) script in the Examples
+  If you're interested in pre-training T5 on a new corpus, check out the [run_t5_mlm_flax.py](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling) script in the Examples
   directory.
 
 - Supervised training
@@ -143,7 +143,7 @@ language modeling head on top of the decoder.
   In addition, we must make sure that padding token id's of the `labels` are not taken into account by the loss
   function. In PyTorch and Tensorflow, this can be done by replacing them with -100, which is the `ignore_index`
   of the `CrossEntropyLoss`. In Flax, one can use the `decoder_attention_mask` to ignore padded tokens from
-  the loss (see the [Flax summarization script](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization) for details). We also pass
+  the loss (see the [Flax summarization script](https://github.com/huggingface/transformers/tree/main/examples/flax/summarization) for details). We also pass
   `attention_mask` as additional input to the model, which makes sure that padding tokens of the inputs are
   ignored. The code example below illustrates all of this.
 
@@ -272,13 +272,13 @@ If you'd like a faster training and inference performance, install [apex](https:
 
 T5 is supported by several example scripts, both for pre-training and fine-tuning.
 
-- pre-training: the [run_t5_mlm_flax.py](https://github.com/huggingface/transformers/blob/master/examples/flax/language-modeling/run_t5_mlm_flax.py)
-  script allows you to further pre-train T5 or pre-train T5 from scratch on your own data. The [t5_tokenizer_model.py](https://github.com/huggingface/transformers/blob/master/examples/flax/language-modeling/t5_tokenizer_model.py)
+- pre-training: the [run_t5_mlm_flax.py](https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/run_t5_mlm_flax.py)
+  script allows you to further pre-train T5 or pre-train T5 from scratch on your own data. The [t5_tokenizer_model.py](https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/t5_tokenizer_model.py)
   script allows you to further train a T5 tokenizer or train a T5 Tokenizer from scratch on your own data. Note that
   Flax (a neural network library on top of JAX) is particularly useful to train on TPU hardware.
 
-- fine-tuning: T5 is supported by the official summarization scripts ([PyTorch](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization), [Tensorflow](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization), and [Flax](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization)) and translation scripts
-  ([PyTorch](https://github.com/huggingface/transformers/tree/master/examples/pytorch/translation) and [Tensorflow](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/translation)). These scripts allow
+- fine-tuning: T5 is supported by the official summarization scripts ([PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization), [Tensorflow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/summarization), and [Flax](https://github.com/huggingface/transformers/tree/main/examples/flax/summarization)) and translation scripts
+  ([PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch/translation) and [Tensorflow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/translation)). These scripts allow
   you to easily fine-tune T5 on custom data for summarization/translation.
 
 ## T5Config

docs/source/model_doc/visual_bert.mdx

@@ -56,7 +56,7 @@ appropriately for the textual and visual parts.
 The [`BertTokenizer`] is used to encode the text. A custom detector/feature extractor must be used
 to get the visual embeddings. The following example notebooks show how to use VisualBERT with Detectron-like models:
 
-- [VisualBERT VQA demo notebook](https://github.com/huggingface/transformers/tree/master/examples/research_projects/visual_bert) : This notebook
+- [VisualBERT VQA demo notebook](https://github.com/huggingface/transformers/tree/main/examples/research_projects/visual_bert) : This notebook
   contains an example on VisualBERT VQA.
 
 - [Generate Embeddings for VisualBERT (Colab Notebook)](https://colab.research.google.com/drive/1bLGxKdldwqnMVA5x4neY7-l_8fKGWQYI?usp=sharing) : This notebook contains

docs/source/model_doc/vit_mae.mdx

@@ -32,7 +32,7 @@ Tips:
 
 - MAE (masked auto encoding) is a method for self-supervised pre-training of Vision Transformers (ViTs). The pre-training objective is relatively simple:
 by masking a large portion (75%) of the image patches, the model must reconstruct raw pixel values. One can use [`ViTMAEForPreTraining`] for this purpose.
-- An example Python script that illustrates how to pre-train [`ViTMAEForPreTraining`] from scratch can be found [here](https://github.com/huggingface/transformers/tree/master/examples/pytorch/image-pretraining).
+- An example Python script that illustrates how to pre-train [`ViTMAEForPreTraining`] from scratch can be found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining).
 One can easily tweak it for their own use case.
 - A notebook that illustrates how to visualize reconstructed pixel values with [`ViTMAEForPreTraining`] can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/ViTMAE/ViT_MAE_visualization_demo.ipynb).
 - After pre-training, one "throws away" the decoder used to reconstruct pixels, and one uses the encoder for fine-tuning/linear probing. This means that after

docs/source/multilingual.mdx

@@ -73,7 +73,7 @@ Now you can pass the `input_ids` and language embedding to the model:
 >>> outputs = model(input_ids, langs=langs)
 ```
 
-The [run_generation.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-generation/run_generation.py) script can generate text with language embeddings using the `xlm-clm` checkpoints.
+The [run_generation.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-generation/run_generation.py) script can generate text with language embeddings using the `xlm-clm` checkpoints.
 
 ### XLM without language embeddings
 

docs/source/parallelism.mdx

@@ -356,7 +356,7 @@ One very important aspect is that FlexFlow is designed for optimizing DNN parall
 
 So the promise is very attractive - it runs a 30min simulation on the cluster of choice and it comes up with the best strategy to utilise this specific environment. If you add/remove/replace any parts it'll run and re-optimize the plan for that. And then you can train. A different setup will have its own custom optimization.
 
-🤗 Transformers status: not yet integrated. We already have our models FX-trace-able via [transformers.utils.fx](https://github.com/huggingface/transformers/blob/master/src/transformers/utils/fx.py), which is a prerequisite for FlexFlow, so someone needs to figure out what needs to be done to make FlexFlow work with our models.
+🤗 Transformers status: not yet integrated. We already have our models FX-trace-able via [transformers.utils.fx](https://github.com/huggingface/transformers/blob/main/src/transformers/utils/fx.py), which is a prerequisite for FlexFlow, so someone needs to figure out what needs to be done to make FlexFlow work with our models.
 
 
 ## Which Strategy To Use When

docs/source/pr_checks.mdx

@@ -125,7 +125,7 @@ Additional checks concern PRs that add new models, mainly that:
 - All models are properly tested (performed by `utils/check_repo.py`)
 
 <!-- TODO Sylvain, add the following
-- All models are added to the main README, inside the master doc
+- All models are added to the main README, inside the main doc
 - All checkpoints used actually exist on the Hub
 
 -->

docs/source/run_scripts.mdx

@@ -12,15 +12,15 @@ specific language governing permissions and limitations under the License.
 
 # Train with a script
 
-Along with the 🤗 Transformers [notebooks](./noteboks/README), there are also example scripts demonstrating how to train a model for a task with [PyTorch](https://github.com/huggingface/transformers/tree/master/examples/pytorch), [TensorFlow](https://github.com/huggingface/transformers/tree/master/examples/tensorflow), or [JAX/Flax](https://github.com/huggingface/transformers/tree/master/examples/flax).
+Along with the 🤗 Transformers [notebooks](./noteboks/README), there are also example scripts demonstrating how to train a model for a task with [PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch), [TensorFlow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow), or [JAX/Flax](https://github.com/huggingface/transformers/tree/main/examples/flax).
 
-You will also find scripts we've used in our [research projects](https://github.com/huggingface/transformers/tree/master/examples/research_projects) and [legacy examples](https://github.com/huggingface/transformers/tree/master/examples/legacy) which are mostly community contributed. These scripts are not actively maintained and require a specific version of 🤗 Transformers that will most likely be incompatible with the latest version of the library.
+You will also find scripts we've used in our [research projects](https://github.com/huggingface/transformers/tree/main/examples/research_projects) and [legacy examples](https://github.com/huggingface/transformers/tree/main/examples/legacy) which are mostly community contributed. These scripts are not actively maintained and require a specific version of 🤗 Transformers that will most likely be incompatible with the latest version of the library.
 
 The example scripts are not expected to work out-of-the-box on every problem, and you may need to adapt the script to the problem you're trying to solve. To help you with this, most of the scripts fully expose how data is preprocessed, allowing you to edit it as necessary for your use case.
 
 For any feature you'd like to implement in an example script, please discuss it on the [forum](https://discuss.huggingface.co/) or in an [issue](https://github.com/huggingface/transformers/issues) before submitting a Pull Request. While we welcome bug fixes, it is unlikely we will merge a Pull Request that adds more functionality at the cost of readability.
 
-This guide will show you how to run an example summarization training script in [PyTorch](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) and [TensorFlow](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization). All examples are expected to work with both frameworks unless otherwise specified.
+This guide will show you how to run an example summarization training script in [PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization) and [TensorFlow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/summarization). All examples are expected to work with both frameworks unless otherwise specified.
 
 ## Setup
 

docs/source/serialization.mdx

@@ -615,9 +615,9 @@ deployment on Inf1. The Neuron SDK provides:
 
 #### Implications
 
-Transformers Models based on the [BERT (Bidirectional Encoder Representations from Transformers)](https://huggingface.co/docs/transformers/master/model_doc/bert)
-architecture, or its variants such as [distilBERT](https://huggingface.co/docs/transformers/master/model_doc/distilbert)
- and [roBERTa](https://huggingface.co/docs/transformers/master/model_doc/roberta)
+Transformers Models based on the [BERT (Bidirectional Encoder Representations from Transformers)](https://huggingface.co/docs/transformers/main/model_doc/bert)
+architecture, or its variants such as [distilBERT](https://huggingface.co/docs/transformers/main/model_doc/distilbert)
+ and [roBERTa](https://huggingface.co/docs/transformers/main/model_doc/roberta)
  will run best on Inf1 for non-generative tasks such as Extractive Question Answering,
  Sequence Classification, Token Classification. Alternatively, text generation
 tasks can be adapted to run on Inf1, according to this [AWS Neuron MarianMT tutorial](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/src/examples/pytorch/transformers-marianmt.html).
@@ -633,7 +633,7 @@ Using AWS Neuron to convert models requires the following dependencies and envir
 
 #### Converting a Model for AWS Neuron
 
-Using the same script as in [Using TorchScript in Python](https://huggingface.co/docs/transformers/master/en/serialization#using-torchscript-in-python)
+Using the same script as in [Using TorchScript in Python](https://huggingface.co/docs/transformers/main/en/serialization#using-torchscript-in-python)
 to trace a "BertModel", you import `torch.neuron` framework extension to access
 the components of the Neuron SDK through a Python API.
 

docs/source/task_summary.mdx

@@ -27,9 +27,9 @@ checkpoints are usually pre-trained on a large corpus of data and fine-tuned on
 following:
 
 - Not all models were fine-tuned on all tasks. If you want to fine-tune a model on a specific task, you can leverage
-  one of the *run_$TASK.py* scripts in the [examples](https://github.com/huggingface/transformers/tree/master/examples) directory.
+  one of the *run_$TASK.py* scripts in the [examples](https://github.com/huggingface/transformers/tree/main/examples) directory.
 - Fine-tuned models were fine-tuned on a specific dataset. This dataset may or may not overlap with your use-case and
-  domain. As mentioned previously, you may leverage the [examples](https://github.com/huggingface/transformers/tree/master/examples) scripts to fine-tune your model, or you may
+  domain. As mentioned previously, you may leverage the [examples](https://github.com/huggingface/transformers/tree/main/examples) scripts to fine-tune your model, or you may
   create your own training script.
 
 In order to do an inference on a task, several mechanisms are made available by the library:
@@ -54,7 +54,7 @@ This would produce random output.
 
 Sequence classification is the task of classifying sequences according to a given number of classes. An example of
 sequence classification is the GLUE dataset, which is entirely based on that task. If you would like to fine-tune a
-model on a GLUE sequence classification task, you may leverage the [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification/run_glue.py), [run_tf_glue.py](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification/run_tf_glue.py), [run_tf_text_classification.py](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification/run_tf_text_classification.py) or [run_xnli.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification/run_xnli.py) scripts.
+model on a GLUE sequence classification task, you may leverage the [run_glue.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification/run_glue.py), [run_tf_glue.py](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification/run_tf_glue.py), [run_tf_text_classification.py](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification/run_tf_text_classification.py) or [run_xnli.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification/run_xnli.py) scripts.
 
 Here is an example of using pipelines to do sentiment analysis: identifying if a sequence is positive or negative. It
 leverages a fine-tuned model on sst2, which is a GLUE task.
@@ -170,8 +170,8 @@ is paraphrase: 6%
 
 Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
 question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune a
-model on a SQuAD task, you may leverage the [run_qa.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/question-answering/run_qa.py) and
-[run_tf_squad.py](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/question-answering/run_tf_squad.py)
+model on a SQuAD task, you may leverage the [run_qa.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering/run_qa.py) and
+[run_tf_squad.py](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering/run_tf_squad.py)
 scripts.
 
 
@@ -335,7 +335,7 @@ Masked language modeling is the task of masking tokens in a sequence with a mask
 fill that mask with an appropriate token. This allows the model to attend to both the right context (tokens on the
 right of the mask) and the left context (tokens on the left of the mask). Such a training creates a strong basis for
 downstream tasks requiring bi-directional context, such as SQuAD (question answering, see [Lewis, Lui, Goyal et al.](https://arxiv.org/abs/1910.13461), part 4.2). If you would like to fine-tune a model on a masked language modeling
-task, you may leverage the [run_mlm.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/language-modeling/run_mlm.py) script.
+task, you may leverage the [run_mlm.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling/run_mlm.py) script.
 
 Here is an example of using pipelines to replace a mask from a sequence:
 
@@ -465,7 +465,7 @@ This prints five sequences, with the top 5 tokens predicted by the model.
 Causal language modeling is the task of predicting the token following a sequence of tokens. In this situation, the
 model only attends to the left context (tokens on the left of the mask). Such a training is particularly interesting
 for generation tasks. If you would like to fine-tune a model on a causal language modeling task, you may leverage the
-[run_clm.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/language-modeling/run_clm.py) script.
+[run_clm.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling/run_clm.py) script.
 
 Usually, the next token is predicted by sampling from the logits of the last hidden state the model produces from the
 input sequence.
@@ -647,7 +647,7 @@ generation blog post [here](https://huggingface.co/blog/how-to-generate).
 Named Entity Recognition (NER) is the task of classifying tokens according to a class, for example, identifying a token
 as a person, an organisation or a location. An example of a named entity recognition dataset is the CoNLL-2003 dataset,
 which is entirely based on that task. If you would like to fine-tune a model on an NER task, you may leverage the
-[run_ner.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/token-classification/run_ner.py) script.
+[run_ner.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/token-classification/run_ner.py) script.
 
 Here is an example of using pipelines to do named entity recognition, specifically, trying to identify tokens as
 belonging to one of 9 classes:
@@ -800,12 +800,12 @@ illustrated below:
 ## Summarization
 
 Summarization is the task of summarizing a document or an article into a shorter text. If you would like to fine-tune a
-model on a summarization task, you may leverage the [run_summarization.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization/run_summarization.py)
+model on a summarization task, you may leverage the [run_summarization.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization/run_summarization.py)
 script.
 
 An example of a summarization dataset is the CNN / Daily Mail dataset, which consists of long news articles and was
 created for the task of summarization. If you would like to fine-tune a model on a summarization task, various
-approaches are described in this [document](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization/README.md).
+approaches are described in this [document](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization/README.md).
 
 Here is an example of using the pipelines to do summarization. It leverages a Bart model that was fine-tuned on the CNN
 / Daily Mail data set.
@@ -901,11 +901,11 @@ between 1999 and 2002.
 ## Translation
 
 Translation is the task of translating a text from one language to another. If you would like to fine-tune a model on a
-translation task, you may leverage the [run_translation.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/translation/run_translation.py) script.
+translation task, you may leverage the [run_translation.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/translation/run_translation.py) script.
 
 An example of a translation dataset is the WMT English to German dataset, which has sentences in English as the input
 data and the corresponding sentences in German as the target data. If you would like to fine-tune a model on a
-translation task, various approaches are described in this [document](https://github.com/huggingface/transformers/tree/master/examples/pytorch/translation/README.md).
+translation task, various approaches are described in this [document](https://github.com/huggingface/transformers/tree/main/examples/pytorch/translation/README.md).
 
 Here is an example of using the pipelines to do translation. It leverages a T5 model that was only pre-trained on a
 multi-task mixture dataset (including WMT), yet, yielding impressive translation results.