---
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-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-0.0003-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: 7.2543
- Rouge/rouge1: 0.3097
- Rouge/rouge2: 0.0856
- Rouge/rougel: 0.2463
- Rouge/rougelsum: 0.2464
- Bertscore/bertscore-precision: 0.8589
- Bertscore/bertscore-recall: 0.8656
- Bertscore/bertscore-f1: 0.8622
- Meteor: 0.2246
- Gen Len: 36.0

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 1.3455        | 1.0   | 109  | 4.8900          | 0.3002       | 0.06         | 0.2217       | 0.2214          | 0.8741                        | 0.8561                     | 0.8649                 | 0.2198 | 32.0    |
| 0.7475        | 2.0   | 218  | 5.6171          | 0.2599       | 0.0592       | 0.202        | 0.2017          | 0.8553                        | 0.8626                     | 0.8589                 | 0.2583 | 43.0    |
| 0.5078        | 3.0   | 327  | 6.1951          | 0.2722       | 0.0714       | 0.2029       | 0.2031          | 0.8612                        | 0.8618                     | 0.8615                 | 0.2582 | 44.0    |
| 0.3719        | 4.0   | 436  | 6.6790          | 0.3035       | 0.072        | 0.2428       | 0.2429          | 0.8724                        | 0.8571                     | 0.8646                 | 0.2108 | 29.0    |
| 0.3026        | 5.0   | 545  | 7.0205          | 0.3101       | 0.0691       | 0.2302       | 0.2302          | 0.8569                        | 0.8665                     | 0.8616                 | 0.2291 | 43.0    |
| 0.2546        | 6.0   | 654  | 7.2543          | 0.3097       | 0.0856       | 0.2463       | 0.2464          | 0.8589                        | 0.8656                     | 0.8622                 | 0.2246 | 36.0    |


### Framework versions

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