---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v1
  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_v1

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on a dataset of 699 original-shortened texts pairs of advertising texts.
It achieves the following results on the evaluation set:
- Loss: 1.9266
- Rouge1: 0.4797
- Rouge2: 0.2787
- Rougel: 0.4325
- Rougelsum: 0.4321
- Bert precision: 0.8713
- Bert recall: 0.8594
- Average word count: 10.0714
- Max word count: 18
- Min word count: 1
- Average token count: 15.45

## Model description
Data is cleaned and preprocessed: "summarize" prefix added for each original text input.

Loss is a combination of:
- CrossEntropy
- Custom loss which can be seen as a length penalty: +1 if predicted text length > 12, else 0

Loss = theta * Custom loss + (1 - theta) * CrossEntropy

(theta = 0.3)

## Intended uses & limitations

More information needed

## Training and evaluation data

699 original-shortened texts pairs of advertising texts of various lengths.
- Original texts lengths: > 12
- Shortened texts lengths: < 13

Splitting amongst sub-datasets:
- 70% of the dataset is used for training
- 20% of the dataset is used for validation
- 10% of the dataset is kept for testing

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|
| 1.7188        | 1.0   | 8    | 1.9266          | 0.4797 | 0.2787 | 0.4325 | 0.4321    | 0.8713         | 0.8594      | 10.0714            | 18             | 1              | 15.45               |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3