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---
datasets:
- pszemraj/scientific_lay_summarisation-plos-norm
language:
- en
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=4248,
training_loss=2.172659089111788,
metrics={'train_runtime': 3371.7912,
'train_samples_per_second': 17.633,
'train_steps_per_second': 1.26,
'total_flos': 1.2884303701396685e+17,
'train_loss': 2.172659089111788,
'epoch': 3.0}
# Training Results
| Epoch | Training Loss | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len |
|:----- |:------------ |:--------------- |:-------- | :------- |:-------- |:--------- |:-------- |:--------- |
| 1 | 2.318000 | 2.079500 | 0.128100 | 0.046700 | 0.104200 | 0.104200 | 0.001100 | 20.000000 |
| 2 | 2.130000 | 2.043523 | 0.130200 | 0.047400 | 0.105400 | 0.105300 | 0.001300 | 20.000000 |
| 3 | 2.047100 | 2.034664 | 0.130700 | 0.047800 | 0.105900 | 0.105900 | 0.001300 | 20.000000 | |