Update README.md

README.md

@@ -8,4 +8,30 @@ base_model:
 - distilbert/distilbert-base-uncased
 pipeline_tag: zero-shot-classification
 library_name: transformers
----
+---
+
+Example code:
+```python3
+# Sample text to predict
+text = "I love this movie, it was fantastic!"
+
+# Tokenize the input text
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+
+# Get model predictions
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# Get the logits (model's raw output)
+logits = outputs.logits
+
+# Convert logits to probabilities (if needed) and get the predicted class (0 or 1)
+predictions = torch.argmax(logits, dim=-1).item()
+
+# Map the prediction to sentiment labels
+labels = {0: "NEGATIVE", 1: "POSITIVE"}  # Assuming binary classification
+predicted_label = labels[predictions]
+
+print(f"Predicted Sentiment: {predicted_label}")
+```
+---