---
library_name: transformers
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
- generated_from_trainer
model-index:
- name: bart-abs-2409-1947-lr-3e-06-bs-2-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-2409-1947-lr-3e-06-bs-2-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: 7.7957
- 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: 3e-06
- train_batch_size: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 0.1989        | 1.0   | 434  | 7.6906          | 0.3097       | 0.0856       | 0.2463       | 0.2464          | 0.8589                        | 0.8656                     | 0.8622                 | 0.2246 | 36.0    |
| 0.1993        | 2.0   | 868  | 7.7371          | 0.3097       | 0.0856       | 0.2463       | 0.2464          | 0.8589                        | 0.8656                     | 0.8622                 | 0.2246 | 36.0    |
| 0.1978        | 3.0   | 1302 | 7.7614          | 0.3097       | 0.0856       | 0.2463       | 0.2464          | 0.8589                        | 0.8656                     | 0.8622                 | 0.2246 | 36.0    |
| 0.1965        | 4.0   | 1736 | 7.7990          | 0.2722       | 0.0714       | 0.2029       | 0.2031          | 0.8612                        | 0.8618                     | 0.8615                 | 0.2582 | 44.0    |
| 0.1968        | 5.0   | 2170 | 7.8204          | 0.3097       | 0.0856       | 0.2463       | 0.2464          | 0.8589                        | 0.8656                     | 0.8622                 | 0.2246 | 36.0    |
| 0.215         | 6.0   | 2604 | 7.8073          | 0.3097       | 0.0856       | 0.2463       | 0.2464          | 0.8589                        | 0.8656                     | 0.8622                 | 0.2246 | 36.0    |
| 0.2144        | 7.0   | 3038 | 7.8003          | 0.3097       | 0.0856       | 0.2463       | 0.2464          | 0.8589                        | 0.8656                     | 0.8622                 | 0.2246 | 36.0    |
| 0.2133        | 8.0   | 3472 | 7.7974          | 0.3097       | 0.0856       | 0.2463       | 0.2464          | 0.8589                        | 0.8656                     | 0.8622                 | 0.2246 | 36.0    |
| 0.2136        | 9.0   | 3906 | 7.7947          | 0.3097       | 0.0856       | 0.2463       | 0.2464          | 0.8589                        | 0.8656                     | 0.8622                 | 0.2246 | 36.0    |
| 0.2119        | 10.0  | 4340 | 7.7957          | 0.3097       | 0.0856       | 0.2463       | 0.2464          | 0.8589                        | 0.8656                     | 0.8622                 | 0.2246 | 36.0    |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1