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---
datasets:
- multi_news
metrics:
- bleu
- rouge
pipeline_tag: summarization
---
# Hyperparameters
learning_rate=2e-5
per_device_train_batch_size=14
per_device_eval_batch_size=14
weight_decay=0.01
save_total_limit=3
num_train_epochs=3
predict_with_generate=True
fp16=True
# Training Output
global_step=7710
training_loss=2.1297076629757417
metrics={'train_runtime': 6059.0418,
'train_samples_per_second': 17.813,
'train_steps_per_second': 1.272,
'total_flos': 2.3389776681055027e+17,
'train_loss': 2.1297076629757417,
'epoch': 3.0}
# Training Results
| Epoch | Training Loss | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len |
|:----- |:------------ |:--------------- |:-------- | :------- |:-------- |:--------- |:-------- |:--------- |
| 1 | 2.223100 | 2.038599 | 0.147400 | 0.054800 | 0.113500 | 0.113500 | 0.001400 | 20.000000 |
| 2 | 2.078100 | 2.009619 | 0.152900 | 0.057800 | 0.117000 | 0.117000 | 0.001600 | 20.000000 |
| 3 | 1.989000 | 2.006006 | 0.152900 | 0.057300 | 0.116700 | 0.116700 | 0.001700 | 20.000000 | |