---
license: mit
tags:
- generated_from_trainer
model-index:
- name: poem-gen-spanish-t5-small
  results: []
---

# poem-gen-spanish-t5-small

This model is a fine-tuned version of [flax-community/spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on the [Spanish Poetry Dataset](https://www.kaggle.com/andreamorgar/spanish-poetry-dataset/version/1) dataset.

The model was created during the [First Spanish Hackathon](https://somosnlp.org/hackathon) organized by [Somos NLP](https://somosnlp.org/).

The team who participated was composed by:

- 🇮🇳 [Drishti Sharma](https://huggingface.co/DrishtiSharma)
- 🇪🇸 [Andrea Morales Garzón](https://huggingface.co/andreamorgar)
- Jorge Henao
- 🇨🇺 [Alberto Carmona Barthelemy](https://huggingface.co/milyiyo)

It achieves the following results on the evaluation set:
- Loss: 2.8586
- Perplexity: 17.43

## Model description

The model was trained to generate spanish poems attending to some parameters like style, sentiment, words to include and starting phrase.

Example:

```
poema:
  estilo: Pablo Neruda &&
  sentimiento: positivo &&
  palabras: cielo, luna, mar &&
  texto: Todos fueron a verle pasar
```

## 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: 2e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step   | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 3.1354        | 0.73  | 30000  | 3.0147          |
| 2.9761        | 1.46  | 60000  | 2.9498          |
| 2.897         | 2.19  | 90000  | 2.9019          |
| 2.8292        | 2.93  | 120000 | 2.8792          |
| 2.7774        | 3.66  | 150000 | 2.8738          |
| 2.741         | 4.39  | 180000 | 2.8634          |
| 2.7128        | 5.12  | 210000 | 2.8666          |
| 2.7108        | 5.85  | 240000 | 2.8595          |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6