---
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
- generated_from_trainer
model-index:
- name: bart-abs-1409-1800-lr-3e-05-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-1409-1800-lr-3e-05-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: 3.3829
- Rouge/rouge1: 0.3549
- Rouge/rouge2: 0.1363
- Rouge/rougel: 0.3021
- Rouge/rougelsum: 0.3032
- Bertscore/bertscore-precision: 0.9037
- Bertscore/bertscore-recall: 0.8687
- Bertscore/bertscore-f1: 0.8857
- Meteor: 0.2545
- Gen Len: 25.5

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 1.7439        | 1.0   | 13   | 2.8127          | 0.2992       | 0.0924       | 0.2491       | 0.2492          | 0.8984                        | 0.8552                     | 0.876                  | 0.2093 | 22.5    |
| 1.263         | 2.0   | 26   | 2.9358          | 0.3773       | 0.1311       | 0.3058       | 0.306           | 0.9115                        | 0.8688                     | 0.8895                 | 0.2469 | 24.9    |
| 0.8455        | 3.0   | 39   | 3.0548          | 0.4307       | 0.1554       | 0.3399       | 0.3393          | 0.9029                        | 0.8741                     | 0.8881                 | 0.3255 | 28.1    |
| 0.6581        | 4.0   | 52   | 3.2153          | 0.3986       | 0.1569       | 0.3487       | 0.3471          | 0.9111                        | 0.8757                     | 0.8929                 | 0.3092 | 25.8    |
| 0.4915        | 5.0   | 65   | 3.2936          | 0.4004       | 0.1256       | 0.3204       | 0.3222          | 0.9035                        | 0.8738                     | 0.8883                 | 0.2892 | 26.0    |
| 0.393         | 6.0   | 78   | 3.3829          | 0.3549       | 0.1363       | 0.3021       | 0.3032          | 0.9037                        | 0.8687                     | 0.8857                 | 0.2545 | 25.5    |


### Framework versions

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