---
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-47
  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-47

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: 3.3826
- Rouge1: 0.2498
- Rouge2: 0.0937
- Rougel: 0.1992
- Rougelsum: 0.2218
- Gen Len: 18.217

## 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: 47
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.7195        | 1.0   | 1250 | 3.3721          | 0.2478 | 0.0885 | 0.1948 | 0.219     | 18.949  |
| 2.6054        | 2.0   | 2500 | 3.3830          | 0.251  | 0.0932 | 0.1977 | 0.2222    | 18.61   |
| 2.572         | 3.0   | 3750 | 3.3801          | 0.251  | 0.092  | 0.1978 | 0.2222    | 18.472  |
| 2.5529        | 4.0   | 5000 | 3.3811          | 0.2495 | 0.0927 | 0.1992 | 0.2215    | 18.222  |
| 2.5453        | 5.0   | 6250 | 3.3833          | 0.2496 | 0.0918 | 0.1983 | 0.2219    | 18.318  |
| 2.5151        | 6.0   | 7500 | 3.3826          | 0.2498 | 0.0937 | 0.1992 | 0.2218    | 18.217  |


### Framework versions

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