---
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-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-2409-1947-lr-3e-06-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: 7.3666
- Rouge/rouge1: 0.2722
- Rouge/rouge2: 0.0714
- Rouge/rougel: 0.2029
- Rouge/rougelsum: 0.2031
- Bertscore/bertscore-precision: 0.8612
- Bertscore/bertscore-recall: 0.8618
- Bertscore/bertscore-f1: 0.8615
- Meteor: 0.2582
- Gen Len: 44.0

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 0.2442        | 1.0   | 217  | 7.1796          | 0.2722       | 0.0714       | 0.2029       | 0.2031          | 0.8612                        | 0.8618                     | 0.8615                 | 0.2582 | 44.0    |
| 0.2514        | 2.0   | 434  | 7.2470          | 0.2722       | 0.0714       | 0.2029       | 0.2031          | 0.8612                        | 0.8618                     | 0.8615                 | 0.2582 | 44.0    |
| 0.2226        | 3.0   | 651  | 7.2953          | 0.2722       | 0.0714       | 0.2029       | 0.2031          | 0.8612                        | 0.8618                     | 0.8615                 | 0.2582 | 44.0    |
| 0.2207        | 4.0   | 868  | 7.3342          | 0.2722       | 0.0714       | 0.2029       | 0.2031          | 0.8612                        | 0.8618                     | 0.8615                 | 0.2582 | 44.0    |
| 0.2177        | 5.0   | 1085 | 7.3588          | 0.2722       | 0.0714       | 0.2029       | 0.2031          | 0.8612                        | 0.8618                     | 0.8615                 | 0.2582 | 44.0    |
| 0.2176        | 6.0   | 1302 | 7.3666          | 0.2722       | 0.0714       | 0.2029       | 0.2031          | 0.8612                        | 0.8618                     | 0.8615                 | 0.2582 | 44.0    |


### Framework versions

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