---
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
- generated_from_trainer
model-index:
- name: bart-abs-1509-0313-lr-3e-05-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-05-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: 3.3486
- Rouge/rouge1: 0.4705
- Rouge/rouge2: 0.2108
- Rouge/rougel: 0.3877
- Rouge/rougelsum: 0.3894
- Bertscore/bertscore-precision: 0.8936
- Bertscore/bertscore-recall: 0.8936
- Bertscore/bertscore-f1: 0.8934
- Meteor: 0.4277
- Gen Len: 38.4091

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 1.1204        | 1.0   | 434  | 2.2199          | 0.4542       | 0.2173       | 0.3843       | 0.3855          | 0.8945                        | 0.8893                     | 0.8917                 | 0.4072 | 37.2273 |
| 0.8222        | 2.0   | 868  | 2.3549          | 0.4613       | 0.2095       | 0.3935       | 0.3957          | 0.8994                        | 0.8929                     | 0.896                  | 0.4089 | 36.8818 |
| 0.565         | 3.0   | 1302 | 2.6652          | 0.4686       | 0.2079       | 0.3905       | 0.3911          | 0.8943                        | 0.8941                     | 0.894                  | 0.4207 | 39.6636 |
| 0.379         | 4.0   | 1736 | 2.9239          | 0.4614       | 0.2076       | 0.3937       | 0.3951          | 0.8962                        | 0.8898                     | 0.8928                 | 0.401  | 34.8545 |
| 0.2543        | 5.0   | 2170 | 3.1849          | 0.4629       | 0.2086       | 0.3988       | 0.3998          | 0.8958                        | 0.8914                     | 0.8935                 | 0.4076 | 36.1091 |
| 0.1761        | 6.0   | 2604 | 3.3486          | 0.4705       | 0.2108       | 0.3877       | 0.3894          | 0.8936                        | 0.8936                     | 0.8934                 | 0.4277 | 38.4091 |


### Framework versions

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