---
license: mit
base_model: facebook/bart-large-cnn
tags:
- summarization
- generated_from_trainer
datasets:
- tldr_news
metrics:
- rouge
model-index:
- name: my_summ
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: tldr_news
      type: tldr_news
      config: all
      split: test
      args: all
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.21647643221587914
---

<!-- 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. -->

# my_summ

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co./facebook/bart-large-cnn) on the tldr_news dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1133
- Rouge1: 0.2165
- Rouge2: 0.0872
- Rougel: 0.1846
- Rougelsum: 0.1881

## 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: 5.6e-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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.2607        | 1.0   | 125  | 2.2706          | 0.2318 | 0.0950 | 0.1983 | 0.2024    |
| 1.1698        | 2.0   | 250  | 2.3624          | 0.2150 | 0.0848 | 0.1828 | 0.1856    |
| 0.5798        | 3.0   | 375  | 2.8369          | 0.2144 | 0.0838 | 0.1802 | 0.1848    |
| 0.2813        | 4.0   | 500  | 3.3045          | 0.2112 | 0.0803 | 0.1788 | 0.1821    |
| 0.1544        | 5.0   | 625  | 3.6092          | 0.2096 | 0.0793 | 0.1780 | 0.1838    |
| 0.0862        | 6.0   | 750  | 3.7615          | 0.2168 | 0.0848 | 0.1851 | 0.1881    |
| 0.0518        | 7.0   | 875  | 3.9039          | 0.2180 | 0.0861 | 0.1842 | 0.1873    |
| 0.0253        | 8.0   | 1000 | 4.1133          | 0.2165 | 0.0872 | 0.1846 | 0.1881    |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0