---
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 |