---
base_model: facebook/bart-base
library_name: peft
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: bart-base-summarization-medical-48
  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. -->

# bart-base-summarization-medical-48

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1260
- Rouge1: 0.4187
- Rouge2: 0.2233
- Rougel: 0.3553
- Rougelsum: 0.3545
- Gen Len: 18.201

## 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: 48
- 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: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.6968        | 1.0   | 1250 | 2.1990          | 0.4139 | 0.2206 | 0.353  | 0.3525    | 17.88   |
| 2.6029        | 2.0   | 2500 | 2.1650          | 0.415  | 0.2192 | 0.351  | 0.3503    | 18.142  |
| 2.5682        | 3.0   | 3750 | 2.1438          | 0.4162 | 0.2188 | 0.35   | 0.3495    | 18.151  |
| 2.5281        | 4.0   | 5000 | 2.1297          | 0.4189 | 0.223  | 0.3559 | 0.3553    | 18.287  |
| 2.5228        | 5.0   | 6250 | 2.1269          | 0.4175 | 0.2228 | 0.3551 | 0.3545    | 18.157  |
| 2.542         | 6.0   | 7500 | 2.1260          | 0.4187 | 0.2233 | 0.3553 | 0.3545    | 18.201  |


### Framework versions

- PEFT 0.12.0
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1