---
inference: false
language: pt
datasets:
- assin2
---


# BERTimbau large for Recognizing Textual Entailment

This is the [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) model finetuned for 
Recognizing Textual Entailment with the [ASSIN 2](https://huggingface.co/datasets/assin2) 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 : There is no entailment between premise and hypothesis.   
* 1 : There is entailment between premise and hypothesis.


## Full classification example

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

model_name = "ruanchaves/bert-large-portuguese-cased-assin2-entailment"
s1 = "Os homens estão cuidadosamente colocando as malas no porta-malas de um carro."
s2 = "Os homens estão colocando bagagens dentro do porta-malas de um carro."
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)}")
```

Output: 

```
1) Label: 1 Score: 1.0
2) Label: 0 Score: 0.0
```

## 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 = {{eplm}},
url = {https://github.com/ruanchaves/eplm},
version = {1.0.0},
year = {2023}
}
```