---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
metrics:
- rouge
- wer
model-index:
- name: bart_baseline_1024
  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_baseline_1024

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: 0.9558
- Rouge1: 0.7069
- Rouge2: 0.4544
- Rougel: 0.6489
- Rougelsum: 0.6489
- Wer: 0.4398

## 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  | 1.2224          | 0.665  | 0.3911 | 0.6    | 0.6001    | 0.4993 |
| 2.0905        | 0.27  | 500  | 1.1190          | 0.6743 | 0.4083 | 0.6103 | 0.6104    | 0.4809 |
| 2.0905        | 0.4   | 750  | 1.0832          | 0.6818 | 0.418  | 0.6178 | 0.6178    | 0.4726 |
| 1.188         | 0.53  | 1000 | 1.0541          | 0.6871 | 0.4246 | 0.6242 | 0.6242    | 0.4675 |
| 1.188         | 0.66  | 1250 | 1.0352          | 0.6881 | 0.4283 | 0.6269 | 0.6268    | 0.4628 |
| 1.1172        | 0.8   | 1500 | 1.0291          | 0.6912 | 0.4319 | 0.6303 | 0.6303    | 0.4586 |
| 1.1172        | 0.93  | 1750 | 1.0079          | 0.7001 | 0.4406 | 0.6396 | 0.6397    | 0.4543 |
| 1.0803        | 1.06  | 2000 | 0.9957          | 0.6939 | 0.4396 | 0.6359 | 0.6359    | 0.4511 |
| 1.0803        | 1.2   | 2250 | 0.9891          | 0.6972 | 0.443  | 0.6383 | 0.6383    | 0.4479 |
| 0.9849        | 1.33  | 2500 | 0.9800          | 0.7009 | 0.4467 | 0.6425 | 0.6425    | 0.4464 |
| 0.9849        | 1.46  | 2750 | 0.9771          | 0.7017 | 0.4479 | 0.6426 | 0.6426    | 0.4437 |
| 0.9867        | 1.6   | 3000 | 0.9638          | 0.7085 | 0.4541 | 0.6495 | 0.6495    | 0.4422 |
| 0.9867        | 1.73  | 3250 | 0.9675          | 0.7013 | 0.4495 | 0.6438 | 0.6438    | 0.4413 |
| 0.9556        | 1.86  | 3500 | 0.9565          | 0.707  | 0.4544 | 0.6493 | 0.6492    | 0.4401 |
| 0.9556        | 1.99  | 3750 | 0.9558          | 0.7069 | 0.4544 | 0.6489 | 0.6489    | 0.4398 |


### Framework versions

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