---
license: mit
base_model: duancleypaul/bart-cnn-samsum-finetuned
tags:
- generated_from_trainer
model-index:
- name: bart-cnn-samsum-peft
  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-cnn-samsum-peft

This model is a fine-tuned version of [duancleypaul/bart-cnn-samsum-finetuned](https://huggingface.co/duancleypaul/bart-cnn-samsum-finetuned) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1351

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1088        | 1.0   | 148  | 0.1342          |
| 0.0754        | 2.0   | 296  | 0.1341          |
| 0.0947        | 3.0   | 444  | 0.1340          |
| 0.0982        | 4.0   | 592  | 0.1344          |
| 0.0704        | 5.0   | 740  | 0.1346          |
| 0.1018        | 6.0   | 888  | 0.1345          |
| 0.0904        | 7.0   | 1036 | 0.1341          |
| 0.091         | 8.0   | 1184 | 0.1346          |
| 0.0957        | 9.0   | 1332 | 0.1346          |
| 0.0785        | 10.0  | 1480 | 0.1345          |
| 0.104         | 11.0  | 1628 | 0.1348          |
| 0.1111        | 12.0  | 1776 | 0.1349          |
| 0.0839        | 13.0  | 1924 | 0.1350          |
| 0.0828        | 14.0  | 2072 | 0.1351          |
| 0.0925        | 15.0  | 2220 | 0.1351          |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2