---
license: mit
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: bart-large-cnn-small-billsum-3epochs
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: billsum
      type: billsum
      config: default
      split: train
      args: default
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.5409
---

<!-- 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-small-billsum-3epochs

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: 1.7523
- Rouge1: 0.5409
- Rouge2: 0.3112
- Rougel: 0.3929
- Rougelsum: 0.4633

## 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: 2.5764683748161164e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.7132        | 0.32  | 8    | 2.2000          | 0.4619 | 0.2328 | 0.3201 | 0.3939    |
| 2.236         | 0.64  | 16   | 1.9705          | 0.499  | 0.2768 | 0.3651 | 0.4216    |
| 2.1109        | 0.96  | 24   | 1.8845          | 0.5214 | 0.2974 | 0.3844 | 0.4417    |
| 1.7663        | 1.28  | 32   | 1.8211          | 0.5226 | 0.2935 | 0.3718 | 0.4479    |
| 1.7838        | 1.6   | 40   | 1.7981          | 0.5338 | 0.3001 | 0.383  | 0.4466    |
| 1.5229        | 1.92  | 48   | 1.7625          | 0.5299 | 0.3012 | 0.3839 | 0.4494    |
| 1.5221        | 2.24  | 56   | 1.7532          | 0.5384 | 0.3117 | 0.3939 | 0.4637    |
| 1.2879        | 2.56  | 64   | 1.7560          | 0.5338 | 0.3075 | 0.3865 | 0.4584    |
| 1.4046        | 2.88  | 72   | 1.7523          | 0.5409 | 0.3112 | 0.3929 | 0.4633    |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2