---
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-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-2-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: 4.0180
- Rouge/rouge1: 0.4724
- Rouge/rouge2: 0.2094
- Rouge/rougel: 0.3964
- Rouge/rougelsum: 0.3976
- Bertscore/bertscore-precision: 0.8964
- Bertscore/bertscore-recall: 0.8932
- Bertscore/bertscore-f1: 0.8947
- Meteor: 0.4217
- Gen Len: 36.8818

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 0.6813        | 1.0   | 434  | 2.4588          | 0.4636       | 0.2101       | 0.3907       | 0.3921          | 0.8975                        | 0.8904                     | 0.8937                 | 0.4099 | 35.0727 |
| 0.6702        | 2.0   | 868  | 2.5377          | 0.4448       | 0.1862       | 0.3725       | 0.3735          | 0.8942                        | 0.8887                     | 0.8913                 | 0.3825 | 35.7273 |
| 0.4591        | 3.0   | 1302 | 2.8762          | 0.4533       | 0.1916       | 0.3767       | 0.3778          | 0.8961                        | 0.8897                     | 0.8928                 | 0.3911 | 35.2091 |
| 0.3165        | 4.0   | 1736 | 3.2129          | 0.4519       | 0.1976       | 0.3803       | 0.3806          | 0.8936                        | 0.891                      | 0.8922                 | 0.4023 | 37.6364 |
| 0.2222        | 5.0   | 2170 | 3.4971          | 0.47         | 0.2049       | 0.392        | 0.3924          | 0.8959                        | 0.8926                     | 0.8941                 | 0.4107 | 36.5545 |
| 0.1596        | 6.0   | 2604 | 3.6405          | 0.4607       | 0.2101       | 0.3853       | 0.3879          | 0.8943                        | 0.8908                     | 0.8924                 | 0.4021 | 37.2273 |
| 0.1166        | 7.0   | 3038 | 3.7827          | 0.4759       | 0.2191       | 0.4086       | 0.4106          | 0.8988                        | 0.8928                     | 0.8956                 | 0.4173 | 35.5727 |
| 0.0891        | 8.0   | 3472 | 3.9388          | 0.4677       | 0.2047       | 0.3905       | 0.3925          | 0.8933                        | 0.8927                     | 0.8929                 | 0.417  | 38.7    |
| 0.0695        | 9.0   | 3906 | 3.9583          | 0.4775       | 0.2116       | 0.4032       | 0.4051          | 0.8981                        | 0.8931                     | 0.8955                 | 0.4228 | 36.3182 |
| 0.0592        | 10.0  | 4340 | 4.0180          | 0.4724       | 0.2094       | 0.3964       | 0.3976          | 0.8964                        | 0.8932                     | 0.8947                 | 0.4217 | 36.8818 |


### Framework versions

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