---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v47
  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_v47

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: 6.3912
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Bert precision: 0.6047
- Bert recall: 0.5681
- Average word count: 1.0
- Max word count: 1
- Min word count: 1
- Average token count: 12.0
- % shortened texts with length > 12: 0.0

## 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: 0.005
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:|
| 7.822         | 1.0   | 83   | 7.4737          | 0.0776 | 0.0    | 0.0775 | 0.0776    | 0.6348         | 0.6223      | 2.0                | 2              | 2              | 13.0                | 0.0                                |
| 3.2859        | 2.0   | 166  | 6.6585          | 0.1063 | 0.0    | 0.1063 | 0.1063    | 0.6469         | 0.608       | 5.0026             | 6              | 5              | 12.0                | 0.0                                |
| 3.0284        | 3.0   | 249  | 6.4761          | 0.116  | 0.0    | 0.116  | 0.1161    | 0.6479         | 0.6388      | 3.9974             | 4              | 3              | 14.0                | 0.0                                |
| 2.9681        | 4.0   | 332  | 6.4592          | 0.0    | 0.0    | 0.0    | 0.0       | 0.6071         | 0.5723      | 1.0                | 1              | 1              | 12.0                | 0.0                                |
| 2.9377        | 5.0   | 415  | 6.4142          | 0.0    | 0.0    | 0.0    | 0.0       | 0.6047         | 0.5681      | 1.0                | 1              | 1              | 12.0                | 0.0                                |
| 2.9168        | 6.0   | 498  | 6.4049          | 0.0    | 0.0    | 0.0    | 0.0       | 0.6049         | 0.5685      | 1.0                | 1              | 1              | 12.0                | 0.0                                |
| 2.8964        | 7.0   | 581  | 6.3912          | 0.0    | 0.0    | 0.0    | 0.0       | 0.6047         | 0.5681      | 1.0                | 1              | 1              | 12.0                | 0.0                                |


### Framework versions

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