---
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-10
  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-10

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: 7.5826
- Rouge/rouge1: 0.2532
- Rouge/rouge2: 0.0528
- Rouge/rougel: 0.2067
- Rouge/rougelsum: 0.2071
- Bertscore/bertscore-precision: 0.8514
- Bertscore/bertscore-recall: 0.8621
- Bertscore/bertscore-f1: 0.8567
- Meteor: 0.2303
- Gen Len: 46.4909

## 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: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 0.6961        | 1.0   | 109  | 5.5084          | 0.2572       | 0.0691       | 0.1962       | 0.1962          | 0.8672                        | 0.8617                     | 0.8644                 | 0.2158 | 34.0    |
| 0.6838        | 2.0   | 218  | 5.7494          | 0.2975       | 0.0945       | 0.2493       | 0.2495          | 0.8739                        | 0.8626                     | 0.8681                 | 0.2433 | 27.0    |
| 0.5113        | 3.0   | 327  | 6.0212          | 0.2722       | 0.0714       | 0.2029       | 0.2031          | 0.8612                        | 0.8618                     | 0.8615                 | 0.2582 | 44.0    |
| 0.4108        | 4.0   | 436  | 6.5957          | 0.2916       | 0.064        | 0.2118       | 0.2121          | 0.8678                        | 0.8659                     | 0.8668                 | 0.2243 | 47.0    |
| 0.3585        | 5.0   | 545  | 6.7542          | 0.2554       | 0.0561       | 0.198        | 0.1977          | 0.8531                        | 0.8633                     | 0.8581                 | 0.2483 | 42.0    |
| 0.3094        | 6.0   | 654  | 6.9956          | 0.3041       | 0.0711       | 0.2307       | 0.2305          | 0.8646                        | 0.8658                     | 0.8652                 | 0.2861 | 42.0    |
| 0.281         | 7.0   | 763  | 7.1181          | 0.2582       | 0.0781       | 0.2156       | 0.2154          | 0.8771                        | 0.8626                     | 0.8697                 | 0.1855 | 29.0    |
| 0.261         | 8.0   | 872  | 7.2717          | 0.3097       | 0.0856       | 0.2463       | 0.2464          | 0.8589                        | 0.8656                     | 0.8622                 | 0.2246 | 36.0    |
| 0.2415        | 9.0   | 981  | 7.4446          | 0.2906       | 0.0847       | 0.2272       | 0.2274          | 0.8671                        | 0.8567                     | 0.8618                 | 0.1991 | 27.0    |
| 0.2228        | 10.0  | 1090 | 7.5826          | 0.2532       | 0.0528       | 0.2067       | 0.2071          | 0.8514                        | 0.8621                     | 0.8567                 | 0.2303 | 46.4909 |


### Framework versions

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