---
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: 0.6551
    - name: Rouge2
      type: rouge
      value: 0.4332
    - name: Rougel
      type: rouge
      value: 0.5543
    - name: Rougelsum
      type: rouge
      value: 0.5541
datasets:
- ahmedmbutt/PTS-Dataset
language:
- en
library_name: transformers
pipeline_tag: summarization
widget:
- text: >-
    I have to say that I do miss talking to a good psychiatrist- however. I
    could sit and argue for ages with a psychiatrist who is intelligent and kind
    (quite hard to find- but they do exist). Especially now that I have a PhD in
    philosophy and have read everything that can be found on madness- including
    the notes they wrote about me when I was in the hospital. Nowadays-
    psychiatrists have a tendency to sign me off pretty quickly when I come onto
    their radar. They don’t wish to deal with me- I tire them out.
---

<!-- 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: 1.1760
- Rouge1: 0.6551
- Rouge2: 0.4332
- Rougel: 0.5543
- Rougelsum: 0.5541
- Gen Len: 80.0886

<!-- ## 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  | 0.8239          | 0.6263 | 0.3973 | 0.5238 | 0.5237    | 84.2023 |
| No log        | 2.0   | 440  | 0.8201          | 0.6461 | 0.4184 | 0.5417 | 0.5416    | 81.1659 |
| 0.7121        | 3.0   | 660  | 0.8661          | 0.6479 | 0.4226 | 0.5448 | 0.5454    | 80.5409 |
| 0.7121        | 4.0   | 880  | 0.9784          | 0.6474 | 0.4242 | 0.5424 | 0.5425    | 82.2932 |
| 0.2619        | 5.0   | 1100 | 1.0645          | 0.655  | 0.4327 | 0.5517 | 0.5517    | 80.8386 |
| 0.2619        | 6.0   | 1320 | 1.1098          | 0.6548 | 0.4339 | 0.5542 | 0.5543    | 81.3545 |
| 0.1124        | 7.0   | 1540 | 1.1528          | 0.6528 | 0.4298 | 0.5511 | 0.551     | 80.5705 |
| 0.1124        | 8.0   | 1760 | 1.1760          | 0.6551 | 0.4332 | 0.5543 | 0.5541    | 80.0886 |


### Framework versions

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