# 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 It also updates some other versions in those lists, that had become out of date earlier.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 model training examples in streaming mode

The following examples showcase how to train a language model from scratch 
using the JAX/Flax backend.

JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU.
Models written in JAX/Flax are **immutable** and updated in a purely functional
way which enables simple and efficient model parallelism.

All of the following examples make use of [dataset streaming](https://huggingface.co/docs/datasets/master/dataset_streaming.html), therefore allowing to train models on massive datasets\
without ever having to download the full dataset.

## Masked language modeling

In the following, we demonstrate how to train a bi-directional transformer model 
using masked language modeling objective as introduced in [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805).
More specifically, we demonstrate how JAX/Flax and dataset streaming can be leveraged 
to pre-train [**`roberta-base`**](https://huggingface.co/roberta-base)
in English on a single TPUv3-8 pod for 10000 update steps.

The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.

Let's start by creating a model repository to save the trained model and logs.
Here we call the model `"english-roberta-base-dummy"`, but you can change the model name as you like.

You can do this either directly on [huggingface.co](https://huggingface.co/new) (assuming that
you are logged in) or via the command line:

```
huggingface-cli repo create english-roberta-base-dummy
```

Next we clone the model repository to add the tokenizer and model files.

```
git clone https://huggingface.co/<your-username>/english-roberta-base-dummy
```

To ensure that all tensorboard traces will be uploaded correctly, we need to 
track them. You can run the following command inside your model repo to do so.

```
cd english-roberta-base-dummy
git lfs track "*tfevents*"
```

Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.

Next, let's add a symbolic link to the `run_mlm_flax.py`.

```bash
export MODEL_DIR="./english-roberta-base-dummy"
ln -s ~/transformers/examples/research_projects/jax-projects/dataset-streaming/run_mlm_flax_stream.py ./
```

### Copy config and tokenizer of existing model

In this example, we will simply copy an existing config and tokenizer in English.
You can run the following code in a Python shell to do so.

```python
from transformers import RobertaTokenizerFast, RobertaConfig

model_dir = "./english-roberta-base-dummy"

tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
config = RobertaConfig.from_pretrained("roberta-base")

tokenizer.save_pretrained(model_dir)
config.save_pretrained(model_dir)
```

### Train model

Next we can run the example script to pretrain the model.
Compared to the default [`run_mlm_flax`](https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/run_mlm_flax.py), we introduced 4 new training settings:
- `num_train_steps` - how many update steps should be run.
- `num_eval_samples` - how many training samples should be taken for evaluation.
- `logging_steps` - at what rate should the training loss be logged.
- `eval_steps` - at what rate should evaluation be run.
10K update steps 

```bash
./run_mlm_flax_stream.py \
    --output_dir="${MODEL_DIR}" \
    --model_type="roberta" \
    --config_name="${MODEL_DIR}" \
    --tokenizer_name="${MODEL_DIR}" \
    --dataset_name="oscar" \
    --dataset_config_name="unshuffled_deduplicated_en" \
    --max_seq_length="128" \
    --per_device_train_batch_size="128" \
    --per_device_eval_batch_size="128" \
    --learning_rate="3e-4" \
    --warmup_steps="1000" \
    --overwrite_output_dir \
    --adam_beta1="0.9" \
    --adam_beta2="0.98" \
    --num_train_steps="10000" \
    --num_eval_samples="5000" \
    --logging_steps="250" \
    --eval_steps="1000" \
    --push_to_hub
```

<!---
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 model training examples in streaming mode

The following examples showcase how to train a language model from scratch 
using the JAX/Flax backend.

JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU.
Models written in JAX/Flax are **immutable** and updated in a purely functional
way which enables simple and efficient model parallelism.

All of the following examples make use of [dataset streaming](https://huggingface.co/docs/datasets/master/dataset_streaming.html), therefore allowing to train models on massive datasets\
without ever having to download the full dataset.

## Masked language modeling

In the following, we demonstrate how to train a bi-directional transformer model 
using masked language modeling objective as introduced in [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805).
More specifically, we demonstrate how JAX/Flax and dataset streaming can be leveraged 
to pre-train [**`roberta-base`**](https://huggingface.co/roberta-base)
in English on a single TPUv3-8 pod for 10000 update steps.

The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.

Let's start by creating a model repository to save the trained model and logs.
Here we call the model `"english-roberta-base-dummy"`, but you can change the model name as you like.

You can do this either directly on [huggingface.co](https://huggingface.co/new) (assuming that
you are logged in) or via the command line:

```
huggingface-cli repo create english-roberta-base-dummy
```

Next we clone the model repository to add the tokenizer and model files.

```
git clone https://huggingface.co/<your-username>/english-roberta-base-dummy
```

To ensure that all tensorboard traces will be uploaded correctly, we need to 
track them. You can run the following command inside your model repo to do so.

```
cd english-roberta-base-dummy
git lfs track "*tfevents*"
```

Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.

Next, let's add a symbolic link to the `run_mlm_flax.py`.

```bash
export MODEL_DIR="./english-roberta-base-dummy"
ln -s ~/transformers/examples/research_projects/jax-projects/dataset-streaming/run_mlm_flax_stream.py ./
```

### Copy config and tokenizer of existing model

In this example, we will simply copy an existing config and tokenizer in English.
You can run the following code in a Python shell to do so.

```python
from transformers import RobertaTokenizerFast, RobertaConfig

model_dir = "./english-roberta-base-dummy"

tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
config = RobertaConfig.from_pretrained("roberta-base")

tokenizer.save_pretrained(model_dir)
config.save_pretrained(model_dir)
```

### Train model

Next we can run the example script to pretrain the model.
Compared to the default [`run_mlm_flax`](https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/run_mlm_flax.py), we introduced 4 new training settings:
- `num_train_steps` - how many update steps should be run.
- `num_eval_samples` - how many training samples should be taken for evaluation.
- `logging_steps` - at what rate should the training loss be logged.
- `eval_steps` - at what rate should evaluation be run.
10K update steps 

```bash
./run_mlm_flax_stream.py \
    --output_dir="${MODEL_DIR}" \
    --model_type="roberta" \
    --config_name="${MODEL_DIR}" \
    --tokenizer_name="${MODEL_DIR}" \
    --dataset_name="oscar" \
    --dataset_config_name="unshuffled_deduplicated_en" \
    --max_seq_length="128" \
    --per_device_train_batch_size="128" \
    --per_device_eval_batch_size="128" \
    --learning_rate="3e-4" \
    --warmup_steps="1000" \
    --overwrite_output_dir \
    --adam_beta1="0.9" \
    --adam_beta2="0.98" \
    --num_train_steps="10000" \
    --num_eval_samples="5000" \
    --logging_steps="250" \
    --eval_steps="1000" \
    --push_to_hub
```
