Update README.md

README.md

@@ -1,3 +1,45 @@
 ---
+inference: false
+language: pt
+datasets:
+- assin2
 license: mit
 ---
+
+# DeBERTinha XSmall for Recognizing Textual Entailment
+
+### **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 = "sagui-nlp/debertinha-ptbr-xsmall-assin2-rte"
+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)}")
+```
+
+## Citation 
+
+Comming soon