Create script

script

@@ -0,0 +1,26 @@
+import torch
+from transformers import DistilBertTokenizer, DistilBertModel
+
+# Load the tokenizer and model
+tokenizer = DistilBertTokenizer.from_pretrained("./directory")
+model = DistilBertModel.from_pretrained("./directory")
+
+# Define the inference function
+def predict(text):
+    # Tokenize the input
+    inputs = tokenizer(text, padding="max_length", truncation=True, return_tensors="pt")
+
+    # Perform the inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+
+    # Convert logits to probabilities
+    probabilities = torch.softmax(logits, dim=1).squeeze().tolist()
+
+    return probabilities
+
+# Example usage
+text = "This is a sample input."
+probabilities = predict(text)
+print(probabilities)