---
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: 0.8901
- Rouge1: 0.7176
- Rouge2: 0.4726
- Rougel: 0.6632
- Rougelsum: 0.6633
- Wer: 0.4177

## 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  | 1.1639          | 0.6758 | 0.4064 | 0.6138 | 0.6136    | 0.4827 |
| 2.044         | 0.27  | 500  | 1.0693          | 0.6853 | 0.4267 | 0.6258 | 0.6256    | 0.4594 |
| 2.044         | 0.4   | 750  | 1.0210          | 0.6982 | 0.4409 | 0.6399 | 0.6399    | 0.452  |
| 1.1195        | 0.53  | 1000 | 0.9865          | 0.6989 | 0.4442 | 0.64   | 0.64      | 0.4449 |
| 1.1195        | 0.66  | 1250 | 0.9697          | 0.7007 | 0.4476 | 0.643  | 0.6429    | 0.4407 |
| 1.0531        | 0.8   | 1500 | 0.9680          | 0.7009 | 0.4495 | 0.6451 | 0.645     | 0.4384 |
| 1.0531        | 0.93  | 1750 | 0.9346          | 0.7099 | 0.4587 | 0.6538 | 0.6539    | 0.4323 |
| 1.0109        | 1.06  | 2000 | 0.9249          | 0.7066 | 0.4589 | 0.6519 | 0.6518    | 0.4295 |
| 1.0109        | 1.2   | 2250 | 0.9221          | 0.7092 | 0.4627 | 0.6541 | 0.654     | 0.427  |
| 0.9199        | 1.33  | 2500 | 0.9117          | 0.7134 | 0.4668 | 0.6583 | 0.6582    | 0.424  |
| 0.9199        | 1.46  | 2750 | 0.9064          | 0.7147 | 0.4676 | 0.6593 | 0.6592    | 0.4225 |
| 0.9164        | 1.6   | 3000 | 0.8996          | 0.7164 | 0.4701 | 0.6612 | 0.6611    | 0.4212 |
| 0.9164        | 1.73  | 3250 | 0.9006          | 0.714  | 0.4695 | 0.6602 | 0.6601    | 0.4201 |
| 0.8861        | 1.86  | 3500 | 0.8893          | 0.7176 | 0.4735 | 0.6635 | 0.6635    | 0.4176 |
| 0.8861        | 1.99  | 3750 | 0.8901          | 0.7176 | 0.4726 | 0.6632 | 0.6633    | 0.4177 |


### Framework versions

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