

# BERTimbau large for Question Answering

This is the [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) model finetuned for 
Text Simplification with the [FaQUaD-NLI](https://huggingface.co/ruanchaves/faquad-nli) dataset. 
This model is suitable for Portuguese.

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

### **Labels**:
* 0 : The answer is not suitable for the provided question.   
* 1 : The answer is suitable for the provided question.


## 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-faquad-nli"
s1 = "Qual a montanha mais alta do mundo?"
s2 = "Monte Everest é a montanha mais alta do mundo."
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}
}
```
{"language": "pt", "datasets": ["ruanchaves/faquad-nli"], "inference": false}
transformers pytorch bert text-classification pt dataset:ruanchaves/faquad-nli autotrain_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #pt #dataset-ruanchaves/faquad-nli #autotrain_compatible #has_space #region-us 



# BERTimbau large for Question Answering

This is the neuralmind/bert-large-portuguese-cased model finetuned for 
Text Simplification with the FaQUaD-NLI dataset. 
This model is suitable for Portuguese.

- Git Repo: Evaluation of Portuguese Language Models.
- Demo: Hugging Face Space: Question Answering

### Labels:
* 0 : The answer is not suitable for the provided question.   
* 1 : The answer is suitable for the provided question.


## Full classification example



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:


# BERTimbau large for Question Answering

This is the neuralmind/bert-large-portuguese-cased model finetuned for 
Text Simplification with the FaQUaD-NLI dataset. 
This model is suitable for Portuguese.

- Git Repo: Evaluation of Portuguese Language Models.
- Demo: Hugging Face Space: Question Answering ### Labels:
* 0 : The answer is not suitable for the provided question.   
* 1 : The answer is suitable for the provided question. ## Full classification example



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:
TAGS
#transformers #pytorch #bert #text-classification #pt #dataset-ruanchaves/faquad-nli #autotrain_compatible #has_space #region-us 
 # BERTimbau large for Question Answering

This is the neuralmind/bert-large-portuguese-cased model finetuned for 
Text Simplification with the FaQUaD-NLI dataset. 
This model is suitable for Portuguese.

- Git Repo: Evaluation of Portuguese Language Models.
- Demo: Hugging Face Space: Question Answering ### Labels:
* 0 : The answer is not suitable for the provided question.   
* 1 : The answer is suitable for the provided question. ## Full classification example



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:
TAGS
#transformers #pytorch #bert #text-classification #pt #dataset-ruanchaves/faquad-nli #autotrain_compatible #has_space #region-us 
# BERTimbau large for Question Answering

This is the neuralmind/bert-large-portuguese-cased model finetuned for 
Text Simplification with the FaQUaD-NLI dataset. 
This model is suitable for Portuguese.

- Git Repo: Evaluation of Portuguese Language Models.
- Demo: Hugging Face Space: Question Answering### Labels:
* 0 : The answer is not suitable for the provided question.   
* 1 : The answer is suitable for the provided question.## Full classification example



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: