---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
metrics:
- rouge
- wer
model-index:
- name: bart_bertsum_1024_250_1000
  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_bertsum_1024_250_1000

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: 1.0191
- Rouge1: 0.6894
- Rouge2: 0.4262
- Rougel: 0.6274
- Rougelsum: 0.6272
- Wer: 0.4606
- Bleurt: -0.5228

## 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    | Bleurt  |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:------:|:-------:|
| No log        | 0.13  | 250  | 1.2589          | 0.6432 | 0.3644 | 0.5764 | 0.5763    | 0.5168 | -0.3132 |
| 2.1861        | 0.27  | 500  | 1.1641          | 0.6562 | 0.3824 | 0.591  | 0.591     | 0.4985 | -0.867  |
| 2.1861        | 0.4   | 750  | 1.1326          | 0.6626 | 0.3917 | 0.5988 | 0.5987    | 0.4904 | -0.5078 |
| 1.2496        | 0.53  | 1000 | 1.1111          | 0.6657 | 0.3958 | 0.6015 | 0.6014    | 0.4859 | -0.484  |
| 1.2496        | 0.66  | 1250 | 1.0959          | 0.6708 | 0.4014 | 0.6052 | 0.6051    | 0.4814 | -0.4774 |
| 1.193         | 0.8   | 1500 | 1.0774          | 0.6724 | 0.4041 | 0.609  | 0.609     | 0.4787 | -0.494  |
| 1.193         | 0.93  | 1750 | 1.0662          | 0.681  | 0.4127 | 0.6177 | 0.6176    | 0.4742 | -0.4464 |
| 1.14          | 1.06  | 2000 | 1.0593          | 0.6795 | 0.4157 | 0.6178 | 0.6177    | 0.4709 | -0.5849 |
| 1.14          | 1.2   | 2250 | 1.0504          | 0.6784 | 0.4158 | 0.6161 | 0.616     | 0.4685 | -0.3624 |
| 1.0439        | 1.33  | 2500 | 1.0427          | 0.6815 | 0.418  | 0.6196 | 0.6195    | 0.4667 | -0.5998 |
| 1.0439        | 1.46  | 2750 | 1.0357          | 0.6833 | 0.4198 | 0.6209 | 0.6207    | 0.465  | -0.6198 |
| 1.045         | 1.6   | 3000 | 1.0286          | 0.6872 | 0.4238 | 0.6251 | 0.6251    | 0.4635 | -0.4564 |
| 1.045         | 1.73  | 3250 | 1.0248          | 0.6829 | 0.4214 | 0.6222 | 0.6221    | 0.4622 | -0.5228 |
| 1.0242        | 1.86  | 3500 | 1.0198          | 0.69   | 0.4273 | 0.6284 | 0.6283    | 0.4601 | -0.4592 |
| 1.0242        | 1.99  | 3750 | 1.0191          | 0.6894 | 0.4262 | 0.6274 | 0.6272    | 0.4606 | -0.5228 |


### Framework versions

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