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* Migrate metric to Evaluate library in tf examples Currently tensorflow examples use `load_metric` function from Datasets library , commit migrates function call to `load` function to Evaluate library. Fix for #18306 * Migrate metric to Evaluate library in tf examples Currently tensorflow examples use `load_metric` function from Datasets library , commit migrates function call to `load` function to Evaluate library. Fix for #18306 * Migrate `metric` to Evaluate for all tf examples Currently tensorflow examples use `load_metric` function from Datasets library , commit migrates function call to `load` function to Evaluate library. |
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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