a14b055b65
* Pass datasets trust_remote_code * Pass trust_remote_code in more tests * Add trust_remote_dataset_code arg to some tests * Revert "Temporarily pin datasets upper version to fix CI" This reverts commitb7672826ca. * Pass trust_remote_code in librispeech_asr_dummy docstrings * Revert "Pin datasets<2.20.0 for examples" This reverts commit833fc17a3e. * Pass trust_remote_code to all examples * Revert "Add trust_remote_dataset_code arg to some tests" to research_projects * Pass trust_remote_code to tests * Pass trust_remote_code to docstrings * Fix flax examples tests requirements * Pass trust_remote_dataset_code arg to tests * Replace trust_remote_dataset_code with trust_remote_code in one example * Fix duplicate trust_remote_code * Replace args.trust_remote_dataset_code with args.trust_remote_code * Replace trust_remote_dataset_code with trust_remote_code in parser * Replace trust_remote_dataset_code with trust_remote_code in dataclasses * Replace trust_remote_dataset_code with trust_remote_code arg
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:
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.
Note that here we used default
generatearguments, using arguments specific forxsumdataset should give better ROUGE scores.