---
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: 0.9368
- Rouge1: 0.7111
- Rouge2: 0.4588
- Rougel: 0.6541
- Rougelsum: 0.6542
- Wer: 0.433

## 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: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:------:|
| No log        | 0.13  | 250  | 1.1442          | 0.6749 | 0.4062 | 0.6133 | 0.6132    | 0.4806 |
| 2.053         | 0.27  | 500  | 1.0353          | 0.6859 | 0.4274 | 0.6269 | 0.6269    | 0.4586 |
| 2.053         | 0.4   | 750  | 1.0013          | 0.6935 | 0.4384 | 0.6351 | 0.6352    | 0.4499 |
| 1.1091        | 0.53  | 1000 | 0.9866          | 0.7003 | 0.4467 | 0.6416 | 0.6417    | 0.4425 |
| 1.1091        | 0.66  | 1250 | 0.9591          | 0.7052 | 0.4512 | 0.6469 | 0.647     | 0.4386 |
| 1.0491        | 0.8   | 1500 | 0.9502          | 0.7035 | 0.4517 | 0.6469 | 0.647     | 0.4366 |
| 1.0491        | 0.93  | 1750 | 0.9368          | 0.7111 | 0.4588 | 0.6541 | 0.6542    | 0.433  |


### Framework versions

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