---
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
- generated_from_trainer
model-index:
- name: bart-abs-1509-0313-lr-3e-06-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-3e-06-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: 6.5722
- Rouge/rouge1: 0.3111
- Rouge/rouge2: 0.0793
- Rouge/rougel: 0.2212
- Rouge/rougelsum: 0.2213
- Bertscore/bertscore-precision: 0.8659
- Bertscore/bertscore-recall: 0.864
- Bertscore/bertscore-f1: 0.8649
- Meteor: 0.228
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 0.3628        | 1.0   | 434  | 6.2314          | 0.2519       | 0.0551       | 0.191        | 0.191           | 0.8502                        | 0.8569                     | 0.8535                 | 0.2501 | 50.8    |
| 0.3799        | 2.0   | 868  | 6.4498          | 0.3111       | 0.0793       | 0.2212       | 0.2213          | 0.8659                        | 0.864                      | 0.8649                 | 0.228  | 36.0    |
| 0.4173        | 3.0   | 1302 | 6.4553          | 0.3111       | 0.0793       | 0.2212       | 0.2213          | 0.8659                        | 0.864                      | 0.8649                 | 0.228  | 36.0    |
| 0.3921        | 4.0   | 1736 | 6.5283          | 0.3111       | 0.0793       | 0.2212       | 0.2213          | 0.8659                        | 0.864                      | 0.8649                 | 0.228  | 36.0    |
| 0.3833        | 5.0   | 2170 | 6.5582          | 0.3111       | 0.0793       | 0.2212       | 0.2213          | 0.8659                        | 0.864                      | 0.8649                 | 0.228  | 36.0    |
| 0.378         | 6.0   | 2604 | 6.5722          | 0.3111       | 0.0793       | 0.2212       | 0.2213          | 0.8659                        | 0.864                      | 0.8649                 | 0.228  | 36.0    |


### Framework versions

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