---
base_model: google/pegasus-large
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: LifeSciencePegasusLargeModel
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# LifeSciencePegasusLargeModel

This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.6523
- Rouge1: 44.7761
- Rouge2: 12.6726
- Rougel: 29.0847
- Rougelsum: 40.7566
- Bertscore Precision: 77.9283
- Bertscore Recall: 81.5854
- Bertscore F1: 79.7092
- Bleu: 0.0886
- Gen Len: 225.7220

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Bertscore Precision | Bertscore Recall | Bertscore F1 | Bleu   | Gen Len  |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 6.2586        | 0.2643 | 300  | 6.0453          | 40.1947 | 11.1082 | 26.9714 | 36.2747   | 76.6344             | 80.8385          | 78.6731      | 0.0775 | 225.7220 |
| 6.0213        | 0.5286 | 600  | 5.7899          | 43.2445 | 12.1722 | 28.4564 | 39.1524   | 77.5194             | 81.3755          | 79.3945      | 0.0856 | 225.7220 |
| 5.9018        | 0.7929 | 900  | 5.6523          | 44.7761 | 12.6726 | 29.0847 | 40.7566   | 77.9283             | 81.5854          | 79.7092      | 0.0886 | 225.7220 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.2.1
- Tokenizers 0.19.1