---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: PTS-Bart-Large-CNN
  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. -->

# PTS-Bart-Large-CNN

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co./facebook/bart-large-cnn) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1442
- Rouge1: 0.6591
- Rouge2: 0.449
- Rougel: 0.5635
- Rougelsum: 0.5633
- Gen Len: 78.7977

## 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: 2e-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: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 220  | 0.8235          | 0.6279 | 0.4019 | 0.5268 | 0.5267    | 82.8295 |
| No log        | 2.0   | 440  | 0.8053          | 0.6461 | 0.4278 | 0.5486 | 0.5484    | 78.6318 |
| 0.7147        | 3.0   | 660  | 0.8889          | 0.6471 | 0.4324 | 0.5491 | 0.5488    | 79.4432 |
| 0.7147        | 4.0   | 880  | 0.9679          | 0.6533 | 0.4391 | 0.5538 | 0.5534    | 80.2023 |
| 0.2566        | 5.0   | 1100 | 0.9734          | 0.6563 | 0.4422 | 0.5574 | 0.5571    | 78.9727 |
| 0.2566        | 6.0   | 1320 | 1.0504          | 0.6538 | 0.4436 | 0.559  | 0.5585    | 78.5682 |
| 0.1136        | 7.0   | 1540 | 1.1172          | 0.6591 | 0.4474 | 0.5646 | 0.5647    | 78.6068 |
| 0.1136        | 8.0   | 1760 | 1.1442          | 0.6591 | 0.449  | 0.5635 | 0.5633    | 78.7977 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1