---
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
- generated_from_trainer
model-index:
- name: bart-abs-1509-0313-lr-0.0003-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-1509-0313-lr-0.0003-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: 3.8541
- Rouge/rouge1: 0.4315
- Rouge/rouge2: 0.1861
- Rouge/rougel: 0.3638
- Rouge/rougelsum: 0.3654
- Bertscore/bertscore-precision: 0.8936
- Bertscore/bertscore-recall: 0.8875
- Bertscore/bertscore-f1: 0.8904
- Meteor: 0.3814
- Gen Len: 35.4273

## 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: 0.0003
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 2.1071        | 1.0   | 217  | 2.5496          | 0.4238       | 0.1753       | 0.3521       | 0.3541          | 0.8943                        | 0.8836                     | 0.8888                 | 0.3359 | 32.1    |
| 1.5392        | 2.0   | 434  | 2.5867          | 0.4296       | 0.1807       | 0.3556       | 0.3551          | 0.8921                        | 0.886                      | 0.8889                 | 0.3598 | 35.0909 |
| 1.0328        | 3.0   | 651  | 2.6952          | 0.4096       | 0.1667       | 0.3444       | 0.3453          | 0.8919                        | 0.8826                     | 0.8871                 | 0.3519 | 33.8818 |
| 0.62          | 4.0   | 868  | 2.9126          | 0.4104       | 0.16         | 0.3478       | 0.3487          | 0.8904                        | 0.8815                     | 0.8858                 | 0.3524 | 33.4273 |
| 0.3251        | 5.0   | 1085 | 3.3250          | 0.43         | 0.1771       | 0.3591       | 0.3598          | 0.8935                        | 0.8861                     | 0.8896                 | 0.3744 | 34.9636 |
| 0.1503        | 6.0   | 1302 | 3.8541          | 0.4315       | 0.1861       | 0.3638       | 0.3654          | 0.8936                        | 0.8875                     | 0.8904                 | 0.3814 | 35.4273 |


### Framework versions

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