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---
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. More information can be found on the repository.
```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)
textual_query = '''kusto: find the session ids which have duration greater than 10 and having Manoj Raheja as the owner | conferencesessions : conference, sessionid, session_title, session_type, owner, participants, URL, level, session_location, starttime, duration, time_and_duration, kusto_affinity'''
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
```
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