---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
metrics:
- rouge
- wer
model-index:
- name: bart_extractive_1024_750
  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_1024_750

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: 0.8876
- Rouge1: 0.7224
- Rouge2: 0.4761
- Rougel: 0.6677
- Rougelsum: 0.6675
- Wer: 0.4176

## 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  | 1.1438          | 0.6714 | 0.403  | 0.61   | 0.6098    | 0.4822 |
| 2.0429        | 0.27  | 500  | 1.0396          | 0.6869 | 0.4286 | 0.6276 | 0.6274    | 0.4574 |
| 2.0429        | 0.4   | 750  | 1.0071          | 0.6941 | 0.4396 | 0.636  | 0.6359    | 0.4501 |
| 1.1127        | 0.53  | 1000 | 0.9806          | 0.7006 | 0.445  | 0.6414 | 0.6413    | 0.444  |
| 1.1127        | 0.66  | 1250 | 0.9681          | 0.7001 | 0.4471 | 0.6423 | 0.6423    | 0.4404 |
| 1.0522        | 0.8   | 1500 | 0.9541          | 0.7026 | 0.4502 | 0.646  | 0.646     | 0.4375 |
| 1.0522        | 0.93  | 1750 | 0.9325          | 0.7125 | 0.461  | 0.6565 | 0.6564    | 0.431  |
| 1.0094        | 1.06  | 2000 | 0.9239          | 0.7069 | 0.4593 | 0.652  | 0.6519    | 0.429  |
| 1.0094        | 1.2   | 2250 | 0.9168          | 0.71   | 0.4631 | 0.6545 | 0.6544    | 0.4265 |
| 0.9166        | 1.33  | 2500 | 0.9095          | 0.7181 | 0.4701 | 0.6631 | 0.663     | 0.4238 |
| 0.9166        | 1.46  | 2750 | 0.9051          | 0.7147 | 0.4679 | 0.6595 | 0.6594    | 0.422  |
| 0.9135        | 1.6   | 3000 | 0.8989          | 0.7227 | 0.4747 | 0.6673 | 0.6672    | 0.4203 |
| 0.9135        | 1.73  | 3250 | 0.9006          | 0.7144 | 0.4696 | 0.6603 | 0.6603    | 0.4194 |
| 0.8846        | 1.86  | 3500 | 0.8868          | 0.7199 | 0.4746 | 0.6656 | 0.6655    | 0.4176 |
| 0.8846        | 1.99  | 3750 | 0.8876          | 0.7224 | 0.4761 | 0.6677 | 0.6675    | 0.4176 |


### Framework versions

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