---
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-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-1509-0313-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: 3.7689
- Rouge/rouge1: 0.4647
- Rouge/rouge2: 0.2065
- Rouge/rougel: 0.3953
- Rouge/rougelsum: 0.3967
- Bertscore/bertscore-precision: 0.8961
- Bertscore/bertscore-recall: 0.8941
- Bertscore/bertscore-f1: 0.895
- Meteor: 0.4195
- Gen Len: 37.9545

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 0.332         | 1.0   | 109  | 2.9323          | 0.4383       | 0.1841       | 0.3691       | 0.3701          | 0.8915                        | 0.8887                     | 0.8899                 | 0.3951 | 37.2182 |
| 0.3384        | 2.0   | 218  | 3.0419          | 0.4611       | 0.2038       | 0.3901       | 0.3918          | 0.8941                        | 0.8913                     | 0.8925                 | 0.4163 | 37.1909 |
| 0.2354        | 3.0   | 327  | 3.2793          | 0.445        | 0.1903       | 0.3776       | 0.3785          | 0.8938                        | 0.8895                     | 0.8915                 | 0.394  | 36.4545 |
| 0.1736        | 4.0   | 436  | 3.4093          | 0.4545       | 0.2          | 0.3877       | 0.3885          | 0.8939                        | 0.8921                     | 0.8928                 | 0.4094 | 38.3818 |
| 0.1406        | 5.0   | 545  | 3.5183          | 0.4634       | 0.2065       | 0.394        | 0.3945          | 0.898                         | 0.8925                     | 0.8951                 | 0.4032 | 35.5182 |
| 0.1108        | 6.0   | 654  | 3.6131          | 0.4667       | 0.2075       | 0.3961       | 0.3964          | 0.8966                        | 0.8936                     | 0.8949                 | 0.4155 | 37.4364 |
| 0.0906        | 7.0   | 763  | 3.6935          | 0.4602       | 0.2002       | 0.3892       | 0.3903          | 0.8922                        | 0.8944                     | 0.8931                 | 0.4116 | 39.6    |
| 0.0788        | 8.0   | 872  | 3.7280          | 0.4704       | 0.212        | 0.4          | 0.4018          | 0.8941                        | 0.8955                     | 0.8946                 | 0.431  | 39.8727 |
| 0.0708        | 9.0   | 981  | 3.7601          | 0.468        | 0.2062       | 0.3979       | 0.3994          | 0.8953                        | 0.8948                     | 0.8949                 | 0.4244 | 38.9727 |
| 0.0637        | 10.0  | 1090 | 3.7689          | 0.4647       | 0.2065       | 0.3953       | 0.3967          | 0.8961                        | 0.8941                     | 0.895                  | 0.4195 | 37.9545 |


### Framework versions

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