---
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-50
  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-50

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: 3.3948
- Rouge1: 0.2474
- Rouge2: 0.0918
- Rougel: 0.1969
- Rougelsum: 0.2198
- Gen Len: 18.166

## 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: 50
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.7028        | 1.0   | 1250 | 3.3697          | 0.25   | 0.0901 | 0.1969 | 0.2216    | 18.807  |
| 2.5838        | 2.0   | 2500 | 3.3865          | 0.2496 | 0.0904 | 0.196  | 0.2207    | 18.586  |
| 2.5612        | 3.0   | 3750 | 3.3752          | 0.2511 | 0.0928 | 0.1984 | 0.2223    | 18.311  |
| 2.5589        | 4.0   | 5000 | 3.3884          | 0.2508 | 0.0931 | 0.1991 | 0.2222    | 18.33   |
| 2.5524        | 5.0   | 6250 | 3.3936          | 0.2477 | 0.0931 | 0.1967 | 0.2204    | 18.159  |
| 2.5314        | 6.0   | 7500 | 3.3948          | 0.2474 | 0.0918 | 0.1969 | 0.2198    | 18.166  |


### Framework versions

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