---
base_model: facebook/bart-large
library_name: peft
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: bart-large-summarization-medical_on_cnn-42
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bart-large-summarization-medical_on_cnn-42

This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co./facebook/bart-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0346
- Rouge1: 0.2419
- Rouge2: 0.0864
- Rougel: 0.1915
- Rougelsum: 0.2157
- Gen Len: 18.758

## 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: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.2338        | 1.0   | 1250 | 3.0353          | 0.238  | 0.084  | 0.1872 | 0.211     | 19.412  |
| 2.1329        | 2.0   | 2500 | 3.0331          | 0.2383 | 0.0835 | 0.1873 | 0.2116    | 19.091  |
| 2.0982        | 3.0   | 3750 | 3.0363          | 0.2412 | 0.0861 | 0.1911 | 0.2148    | 18.84   |
| 2.0827        | 4.0   | 5000 | 3.0470          | 0.2412 | 0.0865 | 0.191  | 0.2146    | 18.745  |
| 2.0732        | 5.0   | 6250 | 3.0370          | 0.2421 | 0.0865 | 0.1915 | 0.2157    | 18.798  |
| 2.0714        | 6.0   | 7500 | 3.0346          | 0.2419 | 0.0864 | 0.1915 | 0.2157    | 18.758  |


### Framework versions

- PEFT 0.11.1
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1