---
license: mit
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
  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: 0.5281
---

<!-- 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_awesome_billsum_model

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: 1.4680
- Rouge1: 0.5281
- Rouge2: 0.2775
- Rougel: 0.3421
- Rougelsum: 0.4021
- Gen Len: 130.3669

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len  |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
| No log        | 1.0   | 495  | 1.5156          | 0.5029 | 0.2538 | 0.3184 | 0.3865    | 131.4073 |
| 1.8254        | 2.0   | 990  | 1.4455          | 0.5102 | 0.2663 | 0.3395 | 0.3949    | 117.4516 |
| 1.2925        | 3.0   | 1485 | 1.4291          | 0.5212 | 0.2713 | 0.3392 | 0.3944    | 130.379  |
| 0.9936        | 4.0   | 1980 | 1.4680          | 0.5281 | 0.2775 | 0.3421 | 0.4021    | 130.3669 |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2