---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: ATS-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. -->

# ATS-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: 1.1044
- Rouge1: 0.6648
- Rouge2: 0.4542
- Rougel: 0.5743
- Rougelsum: 0.5743
- Gen Len: 79.5693

## 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   | 274  | 0.8020          | 0.6276 | 0.3994 | 0.5208 | 0.5212    | 77.3942 |
| 0.7545        | 2.0   | 548  | 0.8005          | 0.6469 | 0.4278 | 0.5488 | 0.5488    | 79.8577 |
| 0.7545        | 3.0   | 822  | 0.8290          | 0.6533 | 0.4371 | 0.56   | 0.56      | 78.7865 |
| 0.3296        | 4.0   | 1096 | 0.8996          | 0.6581 | 0.4439 | 0.5636 | 0.5637    | 78.8522 |
| 0.3296        | 5.0   | 1370 | 0.9740          | 0.6602 | 0.4486 | 0.5644 | 0.5645    | 78.8869 |
| 0.1575        | 6.0   | 1644 | 1.0374          | 0.6617 | 0.4493 | 0.5689 | 0.5687    | 78.7974 |
| 0.1575        | 7.0   | 1918 | 1.0972          | 0.6613 | 0.4513 | 0.5706 | 0.5706    | 79.9069 |
| 0.0868        | 8.0   | 2192 | 1.1044          | 0.6648 | 0.4542 | 0.5743 | 0.5743    | 79.5693 |


### Framework versions

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