---
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-05-bs-8-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-2409-1947-lr-3e-05-bs-8-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: 4.2084
- Rouge/rouge1: 0.4731
- Rouge/rouge2: 0.2204
- Rouge/rougel: 0.4091
- Rouge/rougelsum: 0.4105
- Bertscore/bertscore-precision: 0.8963
- Bertscore/bertscore-recall: 0.8954
- Bertscore/bertscore-f1: 0.8957
- Meteor: 0.4281
- Gen Len: 38.3182

## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 0.0785        | 1.0   | 109  | 3.9838          | 0.4581       | 0.1987       | 0.3887       | 0.3892          | 0.8957                        | 0.8906                     | 0.893                  | 0.4019 | 35.6455 |
| 0.0862        | 2.0   | 218  | 3.9075          | 0.4574       | 0.2028       | 0.3856       | 0.3863          | 0.8923                        | 0.8918                     | 0.8919                 | 0.4091 | 39.1545 |
| 0.0792        | 3.0   | 327  | 3.9794          | 0.4593       | 0.1972       | 0.3807       | 0.3814          | 0.889                         | 0.8926                     | 0.8906                 | 0.4145 | 41.9182 |
| 0.0673        | 4.0   | 436  | 4.0419          | 0.4634       | 0.2047       | 0.3928       | 0.3937          | 0.8948                        | 0.8918                     | 0.8931                 | 0.4133 | 36.6909 |
| 0.0604        | 5.0   | 545  | 4.1048          | 0.4629       | 0.2112       | 0.396        | 0.3971          | 0.8956                        | 0.8926                     | 0.8939                 | 0.4118 | 36.9727 |
| 0.0548        | 6.0   | 654  | 4.1331          | 0.4556       | 0.2042       | 0.3904       | 0.391           | 0.8938                        | 0.8917                     | 0.8926                 | 0.4079 | 38.1545 |
| 0.0508        | 7.0   | 763  | 4.1740          | 0.4546       | 0.1949       | 0.383        | 0.3842          | 0.8925                        | 0.8903                     | 0.8913                 | 0.4028 | 37.5273 |
| 0.0473        | 8.0   | 872  | 4.1643          | 0.4653       | 0.212        | 0.401        | 0.4026          | 0.8949                        | 0.8939                     | 0.8942                 | 0.4212 | 38.4818 |
| 0.0438        | 9.0   | 981  | 4.1913          | 0.472        | 0.2155       | 0.4063       | 0.4071          | 0.8969                        | 0.8947                     | 0.8956                 | 0.4223 | 37.9091 |
| 0.0401        | 10.0  | 1090 | 4.2084          | 0.4731       | 0.2204       | 0.4091       | 0.4105          | 0.8963                        | 0.8954                     | 0.8957                 | 0.4281 | 38.3182 |


### Framework versions

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