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README.md

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+---
+inference: false
+language: pt
+datasets:
+- ruanchaves/faquad-nli
+---
+
+
+# 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 = ,
+url = {https://github.com/ruanchaves/eplm},
+version = {1.0.0},
+year = {2023}
+}
+```