---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: facebook/bart-base
metrics:
- rouge
model-index:
- name: bart-base-lora-summarization-medical
  results: []
datasets:
- mystic-leung/medical_cord19
language:
- en
pipeline_tag: summarization
---

<!-- 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. -->

# bart-base-lora-summarization-medical

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the 'mystic-leung/medical_cord19' dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4119
- Rouge1: 0.4304
- Rouge2: 0.2352
- Rougel: 0.3663
- Rougelsum: 0.3660
- Gen Len: 18.1767

## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.5079        | 1.0   | 6250  | 2.121959        | 0.4263 | 0.2290 | 0.3597 | 0.3594    | 18.3300 |
| 2.4566        | 2.0   | 12500 | 2.084411        | 0.4267 | 0.2312 | 0.3622 | 0.3618    | 18.2773 |
| 2.4242        | 3.0   | 18750 | 2.061557        | 0.4311 | 0.2358 | 0.3660 | 0.3656    | 18.1307 |
| 2.4058        | 4.0   | 25000 | 2.053182        | 0.4316 | 0.2367 | 0.3660 | 0.3659    | 18.1753 |
| 2.4119        | 5.0   | 31250 | 2.052128        | 0.4304 | 0.2352 | 0.3663 | 0.3660    | 18.1767 |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1