---
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-45
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bart-base-summarization-medical_on_cnn-45

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: 3.3819
- Rouge1: 0.2543
- Rouge2: 0.0961
- Rougel: 0.2009
- Rougelsum: 0.2262
- Gen Len: 18.613

## 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: 45
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.6997        | 1.0   | 1250 | 3.3755          | 0.2507 | 0.0879 | 0.1956 | 0.2199    | 19.0    |
| 2.5999        | 2.0   | 2500 | 3.3728          | 0.2478 | 0.0901 | 0.1965 | 0.22      | 18.974  |
| 2.5783        | 3.0   | 3750 | 3.3670          | 0.2532 | 0.0929 | 0.199  | 0.2243    | 18.781  |
| 2.5498        | 4.0   | 5000 | 3.3812          | 0.2541 | 0.0947 | 0.2005 | 0.2253    | 18.681  |
| 2.5358        | 5.0   | 6250 | 3.3795          | 0.254  | 0.095  | 0.2006 | 0.2252    | 18.677  |
| 2.5296        | 6.0   | 7500 | 3.3819          | 0.2543 | 0.0961 | 0.2009 | 0.2262    | 18.613  |


### Framework versions

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