---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart_samsum_finetuned_for_asr
  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_samsum_finetuned_for_asr

This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4623
- Rouge1: 54.4349
- Rouge2: 29.4619
- Rougel: 44.7701
- Rougelsum: 50.2825
- Gen Len: 30.2751

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.3855        | 0.9997 | 1841 | 1.5274          | 52.3133 | 27.7547 | 43.0233 | 48.3891   | 30.3004 |
| 1.0882        | 2.0    | 3683 | 1.4969          | 53.2457 | 28.4129 | 44.1196 | 49.1579   | 30.2637 |
| 0.8376        | 2.9997 | 5524 | 1.5882          | 52.6929 | 27.5974 | 43.3339 | 47.9779   | 30.8034 |
| 0.6756        | 4.0    | 7366 | 1.6617          | 52.5167 | 27.1278 | 43.037  | 48.1382   | 30.5299 |
| 0.5417        | 4.9986 | 9205 | 1.8083          | 52.0696 | 26.8054 | 42.6108 | 47.5455   | 30.2894 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1