---
language: es
tags:
- QA
- Q&A
datasets:
- BSC-TeMU/SQAC
widget:
- text: "question: ¿Cuál es el nombre que se le da a la unidad morfológica y funcional de los seres vivos? context: La célula (del latín cellula, diminutivo de cella, ‘celda’) es la unidad morfológica y funcional de todo ser vivo. De hecho, la célula es el elemento de menor tamaño que puede considerarse vivo.\u200b De este modo, puede clasificarse a los organismos vivos según el número de células que posean: si solo tienen una, se les denomina unicelulares (como pueden ser los protozoos o las bacterias, organismos microscópicos); si poseen más, se les llama pluricelulares. En estos últimos el número de células es variable: de unos pocos cientos, como en algunos nematodos, a cientos de billones (1014), como en el caso del ser humano. Las células suelen poseer un tamaño de 10 µm y una masa de 1 ng, si bien existen células mucho mayores."
---

# Spanish T5 (small) fine-tuned on **SQAC** for  Spanish **QA** 📖❓
[spanish-T5-small](https://huggingface.co/flax-community/spanish-t5-small) fine-tuned on [SQAC](https://huggingface.co/datasets/BSC-TeMU/SQAC) for **Q&A** downstream task.

## Details of Spanish T5 (small)

T5 (small) like arch trained from scatch on [large_spanish_corpus](https://huggingface.co/datasets/large_spanish_corpus) for **HuggingFace/Flax/Jax Week**.




## Details of the dataset 📚 

This dataset contains 6,247 contexts and 18,817 questions with their answers, 1 to 5 for each fragment.
The sources of the contexts are:
* Encyclopedic articles from [Wikipedia in Spanish](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode). 
* News from [Wikinews in Spanish](https://es.wikinews.org/), used under [CC-by licence](https://creativecommons.org/licenses/by/2.5/). 
* Text from the Spanish corpus [AnCora](http://clic.ub.edu/corpus/en), which is a mix from diferent newswire and literature sources, used under [CC-by licence](https://creativecommons.org/licenses/by/4.0/legalcode). 
This dataset can be used to build extractive-QA.



## Results on test dataset 📝

| Metric | # Value   |
| ------ | --------- |
| **BLEU** | **41.94** |



## Model in Action 🚀

```python
from transformers import T5ForConditionalGeneration, AutoTokenizer
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
ckpt = 'mrm8488/spanish-t5-small-sqac-for-qa'
tokenizer = AutoTokenizer.from_pretrained(ckpt)
model = T5ForConditionalGeneration.from_pretrained(ckpt).to(device)



def get_answer(question, context):
  input_text = 'question: %s  context: %s' % (question, context)
  features = tokenizer([input_text ], padding='max_length', truncation=True, max_length=512, return_tensors='pt')
  output = model.generate(input_ids=features['input_ids'].to(device), 
               attention_mask=features['attention_mask'].to(device))

  return tokenizer.decode(output[0], skip_special_tokens=True)
  
```

> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) with the support of [Narrativa](https://www.narrativa.com/)

> Made with <span style="color: #e25555;">&hearts;</span> in Spain