---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v50
  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_v50

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: 1.8296
- Rouge1: 0.5063
- Rouge2: 0.2803
- Rougel: 0.4415
- Rougelsum: 0.4405
- Bert precision: 0.8741
- Bert recall: 0.8787
- Average word count: 8.7857
- Max word count: 16
- Min word count: 3
- Average token count: 16.3942
- % shortened texts with length > 12: 11.9048

## 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: 0.0001
- 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: 5

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:|
| 1.3343        | 1.0   | 83   | 1.4625          | 0.5101 | 0.2866 | 0.4536 | 0.4527    | 0.8751         | 0.877       | 8.3042             | 19             | 4              | 15.1508             | 5.0265                             |
| 0.7011        | 2.0   | 166  | 1.4296          | 0.5101 | 0.284  | 0.4548 | 0.4551    | 0.8736         | 0.8797      | 8.7593             | 18             | 5              | 16.0529             | 7.672                              |
| 0.483         | 3.0   | 249  | 1.3880          | 0.5025 | 0.2819 | 0.4433 | 0.442     | 0.8722         | 0.8782      | 8.7698             | 18             | 5              | 14.8492             | 6.3492                             |
| 0.3876        | 4.0   | 332  | 1.7614          | 0.4934 | 0.2653 | 0.4334 | 0.4327    | 0.8715         | 0.8725      | 8.2249             | 18             | 5              | 16.3042             | 5.5556                             |
| 0.291         | 5.0   | 415  | 1.8296          | 0.5063 | 0.2803 | 0.4415 | 0.4405    | 0.8741         | 0.8787      | 8.7857             | 16             | 3              | 16.3942             | 11.9048                            |


### Framework versions

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