---
license: mit
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: bart-large-cnn-billsum
  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: 0.5014
---

<!-- 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-large-cnn-billsum

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: 1.7658
- Rouge1: 0.5014
- Rouge2: 0.2463
- Rougel: 0.3189
- Rougelsum: 0.3752
- Gen Len: 125.5645

## 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: 4
- eval_batch_size: 4
- 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   | 248  | 1.8112          | 0.4809 | 0.2299 | 0.3067 | 0.3716    | 113.1371 |
| No log        | 2.0   | 496  | 1.7501          | 0.5089 | 0.2484 | 0.325  | 0.3844    | 123.9435 |
| 1.7258        | 3.0   | 744  | 1.7386          | 0.5008 | 0.2412 | 0.3163 | 0.3732    | 127.2056 |
| 1.7258        | 4.0   | 992  | 1.7658          | 0.5014 | 0.2463 | 0.3189 | 0.3752    | 125.5645 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2