36 lines
1.7 KiB
Markdown
36 lines
1.7 KiB
Markdown
# Summarization (Seq2Seq model) training examples
|
|
|
|
The following example showcases how to finetune a sequence-to-sequence model for summarization
|
|
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.
|
|
|
|
`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.
|
|
|
|
For custom datasets in `jsonlines` format please see: https://huggingface.co/docs/datasets/loading_datasets#json-files and you also will find examples of these below.
|
|
|
|
### Train the model
|
|
Next we can run the example script to train the model:
|
|
|
|
```bash
|
|
python run_summarization_flax.py \
|
|
--output_dir ./bart-base-xsum \
|
|
--model_name_or_path facebook/bart-base \
|
|
--tokenizer_name facebook/bart-base \
|
|
--dataset_name="xsum" \
|
|
--do_train --do_eval --do_predict --predict_with_generate \
|
|
--num_train_epochs 6 \
|
|
--learning_rate 5e-5 --warmup_steps 0 \
|
|
--per_device_train_batch_size 64 \
|
|
--per_device_eval_batch_size 64 \
|
|
--overwrite_output_dir \
|
|
--max_source_length 512 --max_target_length 64 \
|
|
--push_to_hub
|
|
```
|
|
|
|
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).
|
|
|
|
> Note that here we used default `generate` arguments, using arguments specific for `xsum` dataset should give better ROUGE scores.
|