---
license: mit
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: bart_summarization_pretrained
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: billsum
      type: billsum
      config: default
      split: ca_test
      args: default
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.5264
---

<!-- 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. -->

# bart_summarization_pretrained

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co./facebook/bart-large-cnn) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7402
- Rouge1: 0.5264
- Rouge2: 0.2745
- Rougel: 0.3432
- Rougelsum: 0.4049
- Gen Len: 131.0645

## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len  |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
| 1.7347        | 1.0   | 989  | 1.6263          | 0.5044 | 0.254  | 0.3219 | 0.3734    | 121.8306 |
| 1.2029        | 2.0   | 1978 | 1.6037          | 0.5278 | 0.2723 | 0.3351 | 0.3977    | 136.4718 |
| 0.8435        | 3.0   | 2967 | 1.6054          | 0.513  | 0.2661 | 0.3357 | 0.3957    | 129.1048 |
| 0.6326        | 4.0   | 3956 | 1.7402          | 0.5264 | 0.2745 | 0.3432 | 0.4049    | 131.0645 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3