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* Finished QA example * Dodge a merge conflict * Update text classification and LM examples * Update NER example * New Keras metrics WIP, fix NER example * Update NER example * Update MC, summarization and translation examples * Add XLA warnings when shapes are variable * Make sure batch_size is consistently scaled by num_replicas * Add PushToHubCallback to all models * Add docs links for KerasMetricCallback * Add docs links for prepare_tf_dataset and jit_compile * Correct inferred model names * Don't assume the dataset has 'lang' * Don't assume the dataset has 'lang' * Write metrics in text classification * Add 'framework' to TrainingArguments and TFTrainingArguments * Export metrics in all examples and add tests * Fix training args for Flax * Update command line args for translation test * make fixup * Fix accidentally running other tests in fp16 * Remove do_train/do_eval from run_clm.py * Remove do_train/do_eval from run_mlm.py * Add tensorflow tests to circleci * Fix circleci * Update examples/tensorflow/language-modeling/run_mlm.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update examples/tensorflow/test_tensorflow_examples.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update examples/tensorflow/translation/run_translation.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update examples/tensorflow/token-classification/run_ner.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Fix save path for tests * Fix some model card kwargs * Explain the magical -1000 * Actually enable tests this time * Skip text classification PR until we fix shape inference * make fixup Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> |
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| .. | ||
| README.md | ||
| requirements.txt | ||
| run_summarization.py | ||
Summarization example
This script shows an example of training a summarization model 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.
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.
Example command
python run_summarization.py \
--model_name_or_path facebook/bart-base \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 16 \
--num_train_epochs 3 \
--do_train \
--do_eval