Update README.md

README.md

@@ -70,15 +70,33 @@ It can be used to determine whether two sentences are paraphrases (semantically
 Example usage:
 
 ```python
-from transformers import pipeline
+model  = AutoModelForSequenceClassification.from_pretrained('peeyush01/albert-paraphrase-detector')
+tokenizer = AutoTokenizer.from_pretrained('peeyush01/albert-paraphrase-detector-tokenizer')
+
+def predict_paraphrase(sentence1, sentence2):
+    inputs = tokenizer(sentence1, sentence2, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = torch.softmax(logits, dim=1)
+        paraphrase_prob = probs[0][1].item()   # assuming label 1 = paraphrase
+    return {"Paraphrase": paraphrase_prob, "Not Paraphrase": 1 - paraphrase_prob}
 
-sim_model = pipeline("text-classification", model="your-username/albert-paraphrase-similarity")
+```
+```python
+import torch
+
+pairs = [
+    ("The movie was fantastic!", "The film was amazing!"),
+    ("He is playing cricket.", "She is reading a book."),
+]
 
-sentence1 = "The car crashed into the wall."
-sentence2 = "The vehicle hit the barrier."
+for s1, s2 in pairs:
+    result = predict_paraphrase(s1, s2)
+    print(f"Sentence 1: {s1}")
+    print(f"Sentence 2: {s2}")
+    print(f"Result: {result}\n")
 
-result = sim_model({"text": sentence1, "text_pair": sentence2})
-print(result)
 ```
 
 ## Training Details