---
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
- generated_from_trainer
model-index:
- name: bart-abs-2409-0144-lr-3e-05-bs-4-maxep-6
  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-abs-2409-0144-lr-3e-05-bs-4-maxep-6

This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-6](https://huggingface.co/sshleifer/distilbart-xsum-12-6) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3498
- Rouge/rouge1: 0.4687
- Rouge/rouge2: 0.2167
- Rouge/rougel: 0.3984
- Rouge/rougelsum: 0.3997
- Bertscore/bertscore-precision: 0.8968
- Bertscore/bertscore-recall: 0.8932
- Bertscore/bertscore-f1: 0.8948
- Meteor: 0.4183
- Gen Len: 37.6273

## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge/rouge1 | Rouge/rouge2 | Rouge/rougel | Rouge/rougelsum | Bertscore/bertscore-precision | Bertscore/bertscore-recall | Bertscore/bertscore-f1 | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 2.3602        | 1.0   | 217  | 2.0806          | 0.4443       | 0.2067       | 0.3739       | 0.3745          | 0.8966                        | 0.8891                     | 0.8927                 | 0.3937 | 38.2364 |
| 1.6559        | 2.0   | 434  | 1.9951          | 0.4581       | 0.22         | 0.3968       | 0.3973          | 0.8994                        | 0.8919                     | 0.8955                 | 0.4032 | 35.1273 |
| 1.2943        | 3.0   | 651  | 2.0581          | 0.4592       | 0.2156       | 0.3932       | 0.3942          | 0.8995                        | 0.8924                     | 0.8958                 | 0.4071 | 35.2364 |
| 1.0197        | 4.0   | 868  | 2.1312          | 0.478        | 0.2304       | 0.409        | 0.4111          | 0.9003                        | 0.8935                     | 0.8967                 | 0.4209 | 36.1091 |
| 0.8117        | 5.0   | 1085 | 2.2532          | 0.4878       | 0.2376       | 0.4181       | 0.4194          | 0.9009                        | 0.8945                     | 0.8976                 | 0.4293 | 35.2636 |
| 0.6733        | 6.0   | 1302 | 2.3498          | 0.4687       | 0.2167       | 0.3984       | 0.3997          | 0.8968                        | 0.8932                     | 0.8948                 | 0.4183 | 37.6273 |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1