---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: PTS-Bart-Large-CNN
  results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: PTS Dataset
          type: PTS-Dataset
        metrics:
          - name: Rouge1
            type: rouge
            value: 0.6376
          - name: Rouge2
            type: rouge
            value: 0.4143
          - name: Rougel
            type: rouge
            value: 0.538
          - name: Rougelsum
            type: rouge
            value: 0.5387
pipeline_tag: summarization
datasets:
- ahmedmbutt/PTS-Dataset
language:
- en
library_name: transformers
---

<!-- 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 PTS dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2638
- Rouge1: 0.6376
- Rouge2: 0.4143
- Rougel: 0.538
- Rougelsum: 0.5387
- Gen Len: 76.8417

<!-- ## 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   | 180  | 0.8748          | 0.6166 | 0.3827 | 0.5058 | 0.5055    | 77.6583 |
| No log        | 2.0   | 360  | 0.8774          | 0.6307 | 0.4064 | 0.5302 | 0.531     | 77.5111 |
| 0.6761        | 3.0   | 540  | 0.9064          | 0.635  | 0.4052 | 0.5309 | 0.5311    | 76.2833 |
| 0.6761        | 4.0   | 720  | 1.0386          | 0.6329 | 0.4038 | 0.5261 | 0.5262    | 78.4889 |
| 0.6761        | 5.0   | 900  | 1.0993          | 0.6285 | 0.4016 | 0.5239 | 0.5246    | 77.0083 |
| 0.2016        | 6.0   | 1080 | 1.2025          | 0.6351 | 0.4126 | 0.5351 | 0.5356    | 76.0722 |
| 0.2016        | 7.0   | 1260 | 1.2399          | 0.6356 | 0.4108 | 0.5362 | 0.5368    | 78.5361 |
| 0.2016        | 8.0   | 1440 | 1.2638          | 0.6376 | 0.4143 | 0.538  | 0.5387    | 76.8417 |


### Framework versions

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