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* add readme * update readme and add requirements * Update examples/flax/summarization/README.md Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
67 lines
2.8 KiB
Markdown
67 lines
2.8 KiB
Markdown
# Summarization (Seq2Seq model) training examples
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The following example showcases how to finetune a sequence-to-sequence model for summarization
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using the JAX/Flax backend.
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JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU.
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Models written in JAX/Flax are **immutable** and updated in a purely functional
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way which enables simple and efficient model parallelism.
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`run_summarization_flax.py` is a lightweight example of how to download and preprocess a dataset from the 🤗 Datasets library or use your own files (jsonlines or csv), then fine-tune one of the architectures above on it.
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For custom datasets in `jsonlines` format please see: https://huggingface.co/docs/datasets/loading_datasets.html#json-files and you also will find examples of these below.
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Let's start by creating a model repository to save the trained model and logs.
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Here we call the model `"bart-base-xsum"`, but you can change the model name as you like.
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You can do this either directly on [huggingface.co](https://huggingface.co/new) (assuming that
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you are logged in) or via the command line:
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```
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huggingface-cli repo create bart-base-xsum
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```
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Next we clone the model repository to add the tokenizer and model files.
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```
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git clone https://huggingface.co/<your-username>/bart-base-xsum
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```
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To ensure that all tensorboard traces will be uploaded correctly, we need to
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track them. You can run the following command inside your model repo to do so.
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```
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cd bart-base-xsum
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git lfs track "*tfevents*"
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```
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Great, we have set up our model repository. During training, we will automatically
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push the training logs and model weights to the repo.
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Next, let's add a symbolic link to the `run_summarization_flax.py`.
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```bash
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export MODEL_DIR="./bart-base-xsum"
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ln -s ~/transformers/examples/flax/summarization/run_summarization_flax.py run_summarization_flax.py
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```
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### Train the model
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Next we can run the example script to train the model:
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```bash
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python run_summarization_flax.py \
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--output_dir ${MODEL_DIR} \
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--model_name_or_path facebook/bart-base \
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--tokenizer_name facebook/bart-base \
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--dataset_name="xsum" \
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--do_train --do_eval --do_predict --predict_with_generate \
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--num_train_epochs 6 \
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--learning_rate 5e-5 --warmup_steps 0 \
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--per_device_train_batch_size 64 \
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--per_device_eval_batch_size 64 \
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--overwrite_output_dir \
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--max_source_length 512 --max_target_length 64 \
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--push_to_hub
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```
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This should finish in 37min, with validation loss and ROUGE2 score of 1.7785 and 17.01 respectively after 6 epochs. training statistics can be accessed on [tfhub.de](https://tensorboard.dev/experiment/OcPfOIgXRMSJqYB4RdK2tA/#scalars).
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> Note that here we used default `generate` arguments, using arguments specific for `xsum` dataset should give better ROUGE scores.
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