Rename master to main for notebooks links and leftovers (#16397)

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
 

docs/source/custom_datasets.mdx

@@ -43,8 +43,8 @@ whether a review is positive or negative.
 <Tip>
 
 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).
 
 </Tip>
 
@@ -228,8 +228,8 @@ such as a person, location, or organization. In this example, learn how to fine-
 <Tip>
 
 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).
 
 </Tip>
 
@@ -472,8 +472,8 @@ given a question. In this example, learn how to fine-tune a model on the [SQuAD]
 <Tip>
 
 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).
 
 </Tip>
 

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

docs/source/tasks/audio_classification.mdx

@@ -142,6 +142,6 @@ At this point, only three steps remain:
 
 <Tip>
 
-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).
 
 </Tip>

docs/source/tasks/image_classification.mdx

@@ -169,6 +169,6 @@ At this point, only three steps remain:
 
 <Tip>
 
-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).
 
 </Tip>

docs/source/tasks/language_modeling.mdx

@@ -412,7 +412,7 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin
 <Tip>
 
 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).
 
 </Tip>

docs/source/tasks/question_answering.mdx

@@ -267,7 +267,7 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin
 <Tip>
 
 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).
 
 </Tip>

docs/source/tasks/sequence_classification.mdx

@@ -208,7 +208,7 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin
 <Tip>
 
 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).
 
 </Tip>

docs/source/tasks/summarization.mdx

@@ -217,7 +217,7 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin
 <Tip>
 
 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).
 
 </Tip>

docs/source/tasks/token_classification.mdx

@@ -266,7 +266,7 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin
 <Tip>
 
 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).
 
 </Tip>

docs/source/tasks/translation.mdx

@@ -219,7 +219,7 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin
 <Tip>
 
 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).
 
 </Tip>

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.