---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v46
  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. -->

# text_shortening_model_v46

This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co./facebook/bart-large-xsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8536
- Rouge1: 0.485
- Rouge2: 0.271
- Rougel: 0.4374
- Rougelsum: 0.4371
- Bert precision: 0.8676
- Bert recall: 0.8761
- Average word count: 9.1032
- Max word count: 17
- Min word count: 4
- Average token count: 15.8254
- % shortened texts with length > 12: 9.5238

## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:|
| 1.8213        | 1.0   | 42   | 2.1030          | 0.4561 | 0.2433 | 0.4131 | 0.4131    | 0.8606         | 0.8724      | 9.0529             | 15             | 5              | 14.7249             | 7.4074                             |
| 0.8874        | 2.0   | 84   | 1.8034          | 0.4778 | 0.2609 | 0.4316 | 0.4316    | 0.8569         | 0.8787      | 10.6323            | 21             | 5              | 16.2963             | 19.3122                            |
| 0.603         | 3.0   | 126  | 1.6613          | 0.4749 | 0.2594 | 0.425  | 0.4253    | 0.8576         | 0.8796      | 10.5106            | 21             | 5              | 16.2751             | 23.0159                            |
| 0.5413        | 4.0   | 168  | 1.5975          | 0.4729 | 0.249  | 0.4258 | 0.4254    | 0.8635         | 0.8696      | 8.6481             | 16             | 4              | 14.3677             | 4.2328                             |
| 0.3393        | 5.0   | 210  | 1.6755          | 0.4959 | 0.28   | 0.4476 | 0.4473    | 0.8687         | 0.8772      | 8.8942             | 20             | 5              | 15.8915             | 8.4656                             |
| 0.2573        | 6.0   | 252  | 1.6908          | 0.4775 | 0.2589 | 0.4309 | 0.4307    | 0.866          | 0.873       | 8.9868             | 22             | 4              | 15.4339             | 10.3175                            |
| 0.173         | 7.0   | 294  | 1.8536          | 0.485  | 0.271  | 0.4374 | 0.4371    | 0.8676         | 0.8761      | 9.1032             | 17             | 4              | 15.8254             | 9.5238                             |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3