---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
metrics:
- rouge
- wer
model-index:
- name: bart_extractive_512_500
  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_extractive_512_500

This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9749
- Rouge1: 0.7
- Rouge2: 0.4441
- Rougel: 0.6408
- Rougelsum: 0.6409
- Wer: 0.4458

## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:------:|
| No log        | 0.13  | 250  | 1.2262          | 0.6523 | 0.3774 | 0.5876 | 0.5877    | 0.5064 |
| 2.0992        | 0.27  | 500  | 1.1233          | 0.6736 | 0.4029 | 0.6091 | 0.6091    | 0.4868 |
| 2.0992        | 0.4   | 750  | 1.1033          | 0.6826 | 0.4152 | 0.6187 | 0.6188    | 0.4768 |
| 1.1914        | 0.53  | 1000 | 1.0645          | 0.6812 | 0.4159 | 0.6178 | 0.618     | 0.4713 |
| 1.1914        | 0.66  | 1250 | 1.0493          | 0.6845 | 0.4206 | 0.6217 | 0.6219    | 0.4673 |
| 1.1319        | 0.8   | 1500 | 1.0348          | 0.6906 | 0.427  | 0.6292 | 0.6292    | 0.4649 |
| 1.1319        | 0.93  | 1750 | 1.0227          | 0.6893 | 0.4289 | 0.6286 | 0.6287    | 0.4596 |
| 1.0853        | 1.06  | 2000 | 1.0093          | 0.6898 | 0.4297 | 0.6298 | 0.6298    | 0.4575 |
| 1.0853        | 1.2   | 2250 | 1.0045          | 0.6981 | 0.4381 | 0.6376 | 0.6377    | 0.4547 |
| 0.9975        | 1.33  | 2500 | 0.9967          | 0.6964 | 0.4394 | 0.6368 | 0.6369    | 0.4511 |
| 0.9975        | 1.46  | 2750 | 0.9863          | 0.6995 | 0.4419 | 0.6401 | 0.6403    | 0.4495 |
| 0.997         | 1.6   | 3000 | 0.9844          | 0.7016 | 0.4441 | 0.642  | 0.6421    | 0.4483 |
| 0.997         | 1.73  | 3250 | 0.9819          | 0.6982 | 0.4431 | 0.6399 | 0.64      | 0.4476 |
| 0.9651        | 1.86  | 3500 | 0.9746          | 0.6994 | 0.4441 | 0.6404 | 0.6406    | 0.4456 |
| 0.9651        | 1.99  | 3750 | 0.9749          | 0.7    | 0.4441 | 0.6408 | 0.6409    | 0.4458 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2