---
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-4-maxep-10
  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-4-maxep-10

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: 3.5339
- Rouge/rouge1: 0.4605
- Rouge/rouge2: 0.2065
- Rouge/rougel: 0.3887
- Rouge/rougelsum: 0.3902
- Bertscore/bertscore-precision: 0.8931
- Bertscore/bertscore-recall: 0.8914
- Bertscore/bertscore-f1: 0.8921
- Meteor: 0.4157
- Gen Len: 37.8364

## 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: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 1.2307        | 1.0   | 217  | 2.1312          | 0.4591       | 0.2098       | 0.3873       | 0.3883          | 0.8977                        | 0.892                      | 0.8947                 | 0.4036 | 37.4909 |
| 0.888         | 2.0   | 434  | 2.2053          | 0.4516       | 0.2075       | 0.3834       | 0.3857          | 0.8957                        | 0.8906                     | 0.893                  | 0.3977 | 35.2273 |
| 0.784         | 3.0   | 651  | 2.3695          | 0.4573       | 0.2136       | 0.3893       | 0.3911          | 0.8972                        | 0.8904                     | 0.8937                 | 0.4003 | 34.8    |
| 0.5673        | 4.0   | 868  | 2.6218          | 0.4714       | 0.2118       | 0.3982       | 0.3996          | 0.8947                        | 0.8924                     | 0.8934                 | 0.4235 | 39.2727 |
| 0.4163        | 5.0   | 1085 | 2.9151          | 0.4683       | 0.2131       | 0.4005       | 0.4023          | 0.8958                        | 0.8916                     | 0.8935                 | 0.4129 | 36.7545 |
| 0.3021        | 6.0   | 1302 | 3.0962          | 0.4648       | 0.2045       | 0.3918       | 0.3935          | 0.8967                        | 0.893                      | 0.8947                 | 0.4119 | 37.0636 |
| 0.2266        | 7.0   | 1519 | 3.2782          | 0.4639       | 0.2074       | 0.3907       | 0.3925          | 0.8942                        | 0.8941                     | 0.894                  | 0.4203 | 38.9273 |
| 0.1684        | 8.0   | 1736 | 3.4198          | 0.4565       | 0.1964       | 0.3822       | 0.3841          | 0.8934                        | 0.8905                     | 0.8918                 | 0.4035 | 37.1818 |
| 0.1347        | 9.0   | 1953 | 3.4878          | 0.4723       | 0.2189       | 0.3987       | 0.4005          | 0.8954                        | 0.8957                     | 0.8954                 | 0.4308 | 39.5273 |
| 0.1124        | 10.0  | 2170 | 3.5339          | 0.4605       | 0.2065       | 0.3887       | 0.3902          | 0.8931                        | 0.8914                     | 0.8921                 | 0.4157 | 37.8364 |


### Framework versions

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