|
--- |
|
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 |
|
|
|
More information needed |
|
|
|
## 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 |
|
|
|
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 |
|
|
