---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v60
  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_v60

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: 0.7251
- Rouge1: 0.7246
- Rouge2: 0.5572
- Rougel: 0.6745
- Rougelsum: 0.6724
- Bert precision: 0.9227
- Bert recall: 0.9242
- Bert f1-score: 0.923
- Average word count: 8.4018
- Max word count: 16
- Min word count: 4
- Average token count: 16.1562
- % shortened texts with length > 12: 7.5893

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Bert f1-score | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:-------------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:|
| 1.4241        | 1.0   | 49   | 0.7533          | 0.7094 | 0.5458 | 0.6655 | 0.6641    | 0.9182         | 0.9214      | 0.9193        | 8.3884             | 17             | 5              | 15.3661             | 6.25                               |
| 0.5792        | 2.0   | 98   | 0.7279          | 0.7058 | 0.5397 | 0.6587 | 0.6582    | 0.9201         | 0.9193      | 0.9192        | 8.3393             | 17             | 4              | 15.9062             | 5.3571                             |
| 0.4392        | 3.0   | 147  | 0.7251          | 0.7246 | 0.5572 | 0.6745 | 0.6724    | 0.9227         | 0.9242      | 0.923         | 8.4018             | 16             | 4              | 16.1562             | 7.5893                             |


### Framework versions

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