Ahmadzei's picture
added 3 more tables for large emb model
5fa1a76
raw
history blame contribute delete
710 Bytes
Use the following arguments to truncate the dataset to a maximum number of samples:
max_train_samples
max_eval_samples
max_predict_samples
python examples/pytorch/summarization/run_summarization.py \
--model_name_or_path google-t5/t5-small \
--max_train_samples 50 \
--max_eval_samples 50 \
--max_predict_samples 50 \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate
Not all example scripts support the max_predict_samples argument.