---
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_on_cnn-49
  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_on_cnn-49

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: 3.3782
- Rouge1: 0.2538
- Rouge2: 0.0951
- Rougel: 0.1997
- Rougelsum: 0.2242
- Gen Len: 18.562

## 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: 49
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.699         | 1.0   | 1250 | 3.3753          | 0.2516 | 0.0907 | 0.1966 | 0.2214    | 19.026  |
| 2.6011        | 2.0   | 2500 | 3.3638          | 0.2505 | 0.0913 | 0.1968 | 0.2211    | 18.839  |
| 2.578         | 3.0   | 3750 | 3.3738          | 0.2516 | 0.0918 | 0.1971 | 0.2208    | 18.888  |
| 2.532         | 4.0   | 5000 | 3.3729          | 0.2523 | 0.0946 | 0.1993 | 0.223     | 18.506  |
| 2.5583        | 5.0   | 6250 | 3.3794          | 0.2528 | 0.0939 | 0.1984 | 0.2233    | 18.612  |
| 2.539         | 6.0   | 7500 | 3.3782          | 0.2538 | 0.0951 | 0.1997 | 0.2242    | 18.562  |


### Framework versions

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