---
inference: false
language: pt
datasets:
- ruanchaves/porsimplessent
---


# mDeBERTa v3 base for Text Simplification

This is the [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) model finetuned for 
Text Simplification with the [PorSimplesSent](https://huggingface.co/ruanchaves/porsimplessent) dataset. 
This model is suitable for Portuguese.

- Git Repo: [Evaluation of Portuguese Language Models](https://github.com/ruanchaves/eplm).
- Demo: [Hugging Face Space: Portuguese Text Simplification](https://ruanchaves-portuguese-text-simplification.hf.space)

### **Labels**:
* 0 : Sentence A is more simple than Sentence B.   
* 1 : The two sentences are equally simple.   
* 2 : Sentence B is more simple than Sentence A.   


## Full classification example

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
import numpy as np
import torch
from scipy.special import softmax

model_name = "ruanchaves/mdeberta-v3-base-porsimplessent"
s1 = "O preço para instalar um DVD player no carro fica entre R$ 2 mil e R$ 5 mil."
s2 = "Instalar um DVD player no carro tem preço médio entre R$ 2 mil e R$ 5 mil."
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
model_input = tokenizer(*([s1], [s2]), padding=True, return_tensors="pt")
with torch.no_grad():
    output = model(**model_input)
    scores = output[0][0].detach().numpy()
    scores = softmax(scores)
    ranking = np.argsort(scores)
    ranking = ranking[::-1]
    for i in range(scores.shape[0]):
        l = config.id2label[ranking[i]]
        s = scores[ranking[i]]
        print(f"{i+1}) Label: {l} Score: {np.round(float(s), 4)}")
```

## Citation 

Our research is ongoing, and we are currently working on describing our experiments in a paper, which will be published soon. 
In the meanwhile, if you would like to cite our work or models before the publication of the paper, please cite our [GitHub repository](https://github.com/ruanchaves/eplm):

```
@software{Chaves_Rodrigues_eplm_2023,
author = {Chaves Rodrigues, Ruan and Tanti, Marc and Agerri, Rodrigo},
doi = {10.5281/zenodo.7781848},
month = {3},
title = {{Evaluation of Portuguese Language Models}},
url = {https://github.com/ruanchaves/eplm},
version = {1.0.0},
year = {2023}
}
```