---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
metrics:
- rouge
- wer
model-index:
- name: bart_extractive_512_375
  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_512_375

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: 0.9965
- Rouge1: 0.6939
- Rouge2: 0.4349
- Rougel: 0.6334
- Rougelsum: 0.6333
- Wer: 0.4534

## 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  | 1.2428          | 0.6513 | 0.3723 | 0.5834 | 0.5833    | 0.5119 |
| 2.1522        | 0.27  | 500  | 1.1468          | 0.6717 | 0.3957 | 0.605  | 0.6049    | 0.494  |
| 2.1522        | 0.4   | 750  | 1.1064          | 0.6729 | 0.404  | 0.609  | 0.609     | 0.483  |
| 1.2231        | 0.53  | 1000 | 1.0908          | 0.6762 | 0.4078 | 0.6116 | 0.6115    | 0.479  |
| 1.2231        | 0.66  | 1250 | 1.0726          | 0.6774 | 0.4108 | 0.6137 | 0.6136    | 0.4755 |
| 1.1583        | 0.8   | 1500 | 1.0581          | 0.6868 | 0.4196 | 0.6246 | 0.6245    | 0.4714 |
| 1.1583        | 0.93  | 1750 | 1.0534          | 0.6833 | 0.4209 | 0.6215 | 0.6214    | 0.4686 |
| 1.1133        | 1.06  | 2000 | 1.0330          | 0.6909 | 0.4263 | 0.6297 | 0.6297    | 0.4647 |
| 1.1133        | 1.2   | 2250 | 1.0288          | 0.6929 | 0.4293 | 0.631  | 0.6309    | 0.4626 |
| 1.0198        | 1.33  | 2500 | 1.0204          | 0.6925 | 0.4303 | 0.6305 | 0.6305    | 0.4601 |
| 1.0198        | 1.46  | 2750 | 1.0097          | 0.6965 | 0.4336 | 0.6349 | 0.6348    | 0.4582 |
| 1.0204        | 1.6   | 3000 | 1.0087          | 0.6976 | 0.4359 | 0.6361 | 0.636     | 0.4565 |
| 1.0204        | 1.73  | 3250 | 1.0042          | 0.6949 | 0.4345 | 0.6342 | 0.6342    | 0.4557 |
| 0.9889        | 1.86  | 3500 | 0.9965          | 0.696  | 0.4366 | 0.6352 | 0.6351    | 0.4534 |
| 0.9889        | 1.99  | 3750 | 0.9965          | 0.6939 | 0.4349 | 0.6334 | 0.6333    | 0.4534 |


### Framework versions

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