---
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-46
  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-large-summarization-medical-46

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: 1.8378
- Rouge1: 0.4404
- Rouge2: 0.2412
- Rougel: 0.3768
- Rougelsum: 0.3769
- Gen Len: 18.977

## 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: 46
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.2273        | 1.0   | 1250 | 1.9018          | 0.4342 | 0.2347 | 0.3676 | 0.3677    | 19.319  |
| 2.1445        | 2.0   | 2500 | 1.8668          | 0.4394 | 0.2388 | 0.3744 | 0.3743    | 18.977  |
| 2.0968        | 3.0   | 3750 | 1.8556          | 0.4406 | 0.2411 | 0.3767 | 0.3769    | 18.689  |
| 2.0883        | 4.0   | 5000 | 1.8502          | 0.4398 | 0.2391 | 0.3758 | 0.376     | 18.757  |
| 2.0638        | 5.0   | 6250 | 1.8393          | 0.4416 | 0.2406 | 0.3779 | 0.3777    | 18.88   |
| 2.0453        | 6.0   | 7500 | 1.8378          | 0.4404 | 0.2412 | 0.3768 | 0.3769    | 18.977  |


### Framework versions

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