# What does this PR do?

This updates the top-level readme files say the project has been tested with Python 3.8+ (instead of 3.7+) and PyTorch 1.10+ (instead of 1.9+). Versions prior to those are no longer supported as of [v4.31.0](https://github.com/huggingface/transformers/releases/tag/v4.31.0). It also updates some other versions in those lists, that had become out of date earlier. The non-English readme files were less up to date than the English readme file `README.md`.

I allowed my editor to remove trailing whitespace, since it does not appear to have been intentional. Rendered Markdown does not appear changed. After editing all seven READMEs, running `make fix-copies` (required to pass CI) propagated this whitespace removal to (just) one of the `index.md` files, which is why that is also changed. However, I understand if whitespace removal may be viewed as best done separately; if requested, I'd be pleased to modify this PR to retain the trailing whitespace.

## Rationale

My reasoning is similar as the reasoning that was given for the previous update to these versions in #24307. As of [**v4.31.0**](https://github.com/huggingface/transformers/releases/tag/v4.31.0), 🤗 Transformers has dropped support for Python 3.7 (#24091) and for PyTorch 1.9 (#24080). Because of that:

- If the version ranges noted in the readme files are not changed, some users are likely to be mislead into expecting new releases of 🤗 Transformers to support those versions.
- A lesser issue is that the claim that this repository is tested on all those versions will gradually become inaccurate as further contributions are made to the repository, now that those versions are not supported.

## Before submitting

- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case.

<!-- I'm sure who, if anyone, I should ping for this. I may edit in a ping later.

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

Update tested versions in READMEs. # What does this PR do?

This updates the top-level readme files say the project has been tested with Python 3.8+ (instead of 3.7+) and PyTorch 1.10+ (instead of 1.9+). Versions prior to those are no longer supported as of [v4.31.0](https://github.com/huggingface/transformers/releases/tag/v4.31.0). It also updates some other versions in those lists, that had become out of date earlier. The non-English readme files were less up to date than the English readme file `README.md`.

I allowed my editor to remove trailing whitespace, since it does not appear to have been intentional. Rendered Markdown does not appear changed. After editing all seven READMEs, running `make fix-copies` (required to pass CI) propagated this whitespace removal to (just) one of the `index.md` files, which is why that is also changed. However, I understand if whitespace removal may be viewed as best done separately; if requested, I'd be pleased to modify this PR to retain the trailing whitespace.

## Rationale

My reasoning is similar as the reasoning that was given for the previous update to these versions in #24307. As of [**v4.31.0**](https://github.com/huggingface/transformers/releases/tag/v4.31.0), 🤗 Transformers has dropped support for Python 3.7 (#24091) and for PyTorch 1.9 (#24080). Because of that:

- If the version ranges noted in the readme files are not changed, some users are likely to be mislead into expecting new releases of 🤗 Transformers to support those versions.
- A lesser issue is that the claim that this repository is tested on all those versions will gradually become inaccurate as further contributions are made to the repository, now that those versions are not supported.

## Before submitting

- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case.

<!-- I'm sure who, if anyone, I should ping for this. I may edit in a ping later.

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

<!---
Copyright 2021 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->

# Language modelling examples

This folder contains some scripts showing examples of *language model pre-training* with the 🤗 Transformers library.
For straightforward use-cases you may be able to use these scripts without modification, although we have also
included comments in the code to indicate areas that you may need to adapt to your own projects. The two scripts
have almost identical arguments, but they differ in the type of LM they train - a causal language model (like GPT) or a 
masked language model (like BERT). Masked language models generally train more quickly and perform better when 
fine-tuned on new tasks with a task-specific output head, like text classification. However, their ability to generate
text is weaker than causal language models.

## Pre-training versus fine-tuning

These scripts can be used to both *pre-train* a language model completely from scratch, as well as to *fine-tune*
a language model on text from your domain of interest. To start with an existing pre-trained language model you
can use the `--model_name_or_path` argument, or to train from scratch you can use the `--model_type` argument
to indicate the class of model architecture to initialize.

### Multi-GPU and TPU usage

By default, these scripts use a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs
can also be used by passing the name of the TPU resource with the `--tpu` argument.

## run_mlm.py

This script trains a masked language model.

### Example command
```
python run_mlm.py \
--model_name_or_path distilbert-base-cased \
--output_dir output \
--dataset_name wikitext \
--dataset_config_name wikitext-103-raw-v1
```

When using a custom dataset, the validation file can be separately passed as an input argument. Otherwise some split (customizable) of training data is used as validation.
```
python run_mlm.py \
--model_name_or_path distilbert-base-cased \
--output_dir output \
--train_file train_file_path
```

## run_clm.py

This script trains a causal language model.

### Example command
```
python run_clm.py \
--model_name_or_path distilgpt2 \
--output_dir output \
--dataset_name wikitext \
--dataset_config_name wikitext-103-raw-v1
```

When using a custom dataset, the validation file can be separately passed as an input argument. Otherwise some split (customizable) of training data is used as validation.

```
python run_clm.py \
--model_name_or_path distilgpt2 \
--output_dir output \
--train_file train_file_path
```

<!---
Copyright 2021 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->

# Language modelling examples

This folder contains some scripts showing examples of *language model pre-training* with the 🤗 Transformers library.
For straightforward use-cases you may be able to use these scripts without modification, although we have also
included comments in the code to indicate areas that you may need to adapt to your own projects. The two scripts
have almost identical arguments, but they differ in the type of LM they train - a causal language model (like GPT) or a 
masked language model (like BERT). Masked language models generally train more quickly and perform better when 
fine-tuned on new tasks with a task-specific output head, like text classification. However, their ability to generate
text is weaker than causal language models.

## Pre-training versus fine-tuning

These scripts can be used to both *pre-train* a language model completely from scratch, as well as to *fine-tune*
a language model on text from your domain of interest. To start with an existing pre-trained language model you
can use the `--model_name_or_path` argument, or to train from scratch you can use the `--model_type` argument
to indicate the class of model architecture to initialize.

### Multi-GPU and TPU usage

By default, these scripts use a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs
can also be used by passing the name of the TPU resource with the `--tpu` argument.

## run_mlm.py

This script trains a masked language model.

### Example command
```
python run_mlm.py \
--model_name_or_path distilbert-base-cased \
--output_dir output \
--dataset_name wikitext \
--dataset_config_name wikitext-103-raw-v1
```

When using a custom dataset, the validation file can be separately passed as an input argument. Otherwise some split (customizable) of training data is used as validation.
```
python run_mlm.py \
--model_name_or_path distilbert-base-cased \
--output_dir output \
--train_file train_file_path
```

## run_clm.py

This script trains a causal language model.

### Example command
```
python run_clm.py \
--model_name_or_path distilgpt2 \
--output_dir output \
--dataset_name wikitext \
--dataset_config_name wikitext-103-raw-v1
```

When using a custom dataset, the validation file can be separately passed as an input argument. Otherwise some split (customizable) of training data is used as validation.

```
python run_clm.py \
--model_name_or_path distilgpt2 \
--output_dir output \
--train_file train_file_path
```
