Update README.md

README.md

@@ -1,3 +1,54 @@
 ---
 license: mit
+tags:
+- code
 ---
+# What does this model do?
+This model converts the natural language input to Kusto (KQL) query. It is a fine-tuned CodeT5+ 220M. This model is a part of nl2query repository which is present at https://github.com/Chirayu-Tripathi/nl2query
+
+You can use this model via the github repository or via following code.
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+model = AutoModelForSeq2SeqLM.from_pretrained("Chirayu/nl2kql")
+tokenizer = AutoTokenizer.from_pretrained("Chirayu/nl2kql")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+def generate_query(
+        textual_query: str,
+        num_beams: int = 10,
+        max_length: int = 128,
+        repetition_penalty: int = 2.5,
+        length_penalty: int = 1,
+        early_stopping: bool = True,
+        top_p: int = 0.95,
+        top_k: int = 50,
+        num_return_sequences: int = 1,
+    ) -> str:
+        input_ids = tokenizer.encode(
+            textual_query, return_tensors="pt", add_special_tokens=True
+        )
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        input_ids = input_ids.to(device)
+        generated_ids = model.generate(
+            input_ids=input_ids,
+            num_beams=num_beams,
+            max_length=max_length,
+            repetition_penalty=repetition_penalty,
+            length_penalty=length_penalty,
+            early_stopping=early_stopping,
+            top_p=top_p,
+            top_k=top_k,
+            num_return_sequences=num_return_sequences,
+        )
+        query = [
+            tokenizer.decode(
+                generated_id,
+                skip_special_tokens=True,
+                clean_up_tokenization_spaces=True,
+            )
+            for generated_id in generated_ids
+        ][0]
+        return query
+```