---
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-2-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-2-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: 6.8567
- Rouge/rouge1: 0.3035
- Rouge/rouge2: 0.072
- Rouge/rougel: 0.2428
- Rouge/rougelsum: 0.2429
- Bertscore/bertscore-precision: 0.8724
- Bertscore/bertscore-recall: 0.8571
- Bertscore/bertscore-f1: 0.8646
- Meteor: 0.2108
- Gen Len: 29.0

## 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: 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 2.4741        | 1.0   | 434  | 4.0269          | 0.2771       | 0.0691       | 0.2057       | 0.2053          | 0.8702                        | 0.8596                     | 0.8648                 | 0.233  | 39.0    |
| 3.0848        | 2.0   | 868  | 3.9978          | 0.2554       | 0.0651       | 0.2183       | 0.2183          | 0.8646                        | 0.8589                     | 0.8617                 | 0.2022 | 29.1364 |
| 1.9491        | 3.0   | 1302 | 4.4524          | 0.2722       | 0.0714       | 0.2029       | 0.2031          | 0.8612                        | 0.8618                     | 0.8615                 | 0.2582 | 47.0    |
| 1.0603        | 4.0   | 1736 | 5.4022          | 0.2465       | 0.0593       | 0.2071       | 0.2071          | 0.8464                        | 0.858                      | 0.8521                 | 0.2294 | 42.0    |
| 0.5921        | 5.0   | 2170 | 6.1146          | 0.3035       | 0.072        | 0.2428       | 0.2429          | 0.8724                        | 0.8571                     | 0.8646                 | 0.2108 | 29.0    |
| 0.3762        | 6.0   | 2604 | 6.8567          | 0.3035       | 0.072        | 0.2428       | 0.2429          | 0.8724                        | 0.8571                     | 0.8646                 | 0.2108 | 29.0    |


### Framework versions

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