---
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-51
  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-51

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: 2.1339
- Rouge1: 0.4189
- Rouge2: 0.2237
- Rougel: 0.356
- Rougelsum: 0.3558
- Gen Len: 18.191

## 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: 51
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.7175        | 1.0   | 1250 | 2.2013          | 0.4107 | 0.219  | 0.35   | 0.3496    | 17.772  |
| 2.5872        | 2.0   | 2500 | 2.1674          | 0.4158 | 0.2228 | 0.3536 | 0.3531    | 18.005  |
| 2.5979        | 3.0   | 3750 | 2.1502          | 0.415  | 0.2215 | 0.351  | 0.3509    | 18.111  |
| 2.5579        | 4.0   | 5000 | 2.1428          | 0.4178 | 0.226  | 0.3548 | 0.3549    | 18.143  |
| 2.5332        | 5.0   | 6250 | 2.1350          | 0.4172 | 0.2243 | 0.3546 | 0.3542    | 18.149  |
| 2.5331        | 6.0   | 7500 | 2.1339          | 0.4189 | 0.2237 | 0.356  | 0.3558    | 18.191  |


### Framework versions

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