---
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
- generated_from_trainer
model-index:
- name: bart-abs-1509-0313-lr-3e-05-bs-8-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-1509-0313-lr-3e-05-bs-8-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: 3.3957
- Rouge/rouge1: 0.4646
- Rouge/rouge2: 0.2089
- Rouge/rougel: 0.3939
- Rouge/rougelsum: 0.3945
- Bertscore/bertscore-precision: 0.8956
- Bertscore/bertscore-recall: 0.8935
- Bertscore/bertscore-f1: 0.8944
- Meteor: 0.4132
- Gen Len: 37.5

## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 0.6107        | 1.0   | 109  | 2.4784          | 0.449        | 0.1974       | 0.3774       | 0.3776          | 0.8943                        | 0.8904                     | 0.8922                 | 0.3981 | 36.9182 |
| 0.3993        | 2.0   | 218  | 2.7984          | 0.4656       | 0.2145       | 0.3954       | 0.3965          | 0.8975                        | 0.8914                     | 0.8943                 | 0.408  | 35.1364 |
| 0.2779        | 3.0   | 327  | 3.0563          | 0.4669       | 0.2112       | 0.3981       | 0.3995          | 0.8961                        | 0.8905                     | 0.8931                 | 0.4088 | 36.0545 |
| 0.2038        | 4.0   | 436  | 3.2410          | 0.4639       | 0.2052       | 0.3895       | 0.3904          | 0.896                         | 0.8949                     | 0.8953                 | 0.4109 | 37.9    |
| 0.1606        | 5.0   | 545  | 3.3263          | 0.4582       | 0.2063       | 0.391        | 0.392           | 0.8961                        | 0.893                      | 0.8944                 | 0.4033 | 36.5545 |
| 0.1282        | 6.0   | 654  | 3.3957          | 0.4646       | 0.2089       | 0.3939       | 0.3945          | 0.8956                        | 0.8935                     | 0.8944                 | 0.4132 | 37.5    |


### Framework versions

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