---
library_name: transformers
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-finetuned-billsum
  results: []
---

<!-- 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-finetuned-billsum

This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1947
- Rouge1: 35.1575
- Rouge2: 27.7021
- Rougel: 32.9801
- Rougelsum: 33.6194
- Gen Len: 31.9873

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:------:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.5279        | 0.4221 | 1000  | 1.3638          | 34.2853 | 26.1627 | 31.897  | 32.6399   | 31.999  |
| 1.3237        | 0.8442 | 2000  | 1.2357          | 34.7055 | 26.7936 | 32.3811 | 33.0823   | 31.9973 |
| 1.1594        | 1.2664 | 3000  | 1.2246          | 34.6975 | 27.0964 | 32.5326 | 33.1883   | 31.982  |
| 1.1029        | 1.6885 | 4000  | 1.2092          | 34.4969 | 26.9107 | 32.3644 | 33.0481   | 31.9987 |
| 1.0461        | 2.1106 | 5000  | 1.1769          | 35.2419 | 27.6038 | 33.0339 | 33.6849   | 31.9903 |
| 0.9535        | 2.5327 | 6000  | 1.1958          | 34.7138 | 27.2185 | 32.5573 | 33.2043   | 31.9947 |
| 0.9373        | 2.9548 | 7000  | 1.1600          | 35.1741 | 27.6199 | 32.9618 | 33.6181   | 31.9783 |
| 0.8506        | 3.3770 | 8000  | 1.1940          | 34.8976 | 27.4455 | 32.7581 | 33.4013   | 31.99   |
| 0.8341        | 3.7991 | 9000  | 1.1716          | 35.1191 | 27.6856 | 32.9822 | 33.6221   | 31.9853 |
| 0.8083        | 4.2212 | 10000 | 1.1916          | 35.1839 | 27.7013 | 32.995  | 33.6131   | 31.988  |
| 0.7749        | 4.6433 | 11000 | 1.1947          | 35.1575 | 27.7021 | 32.9801 | 33.6194   | 31.9873 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.2.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1