# 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
```

{"license": "mit", "tags": ["code"]}
transformers pytorch safetensors t5 text2text-generation code license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #code #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

# 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 URL

You can use this model via the github repository or via following code. More information can be found on the repository.



# 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 URL

You can use this model via the github repository or via following code. More information can be found on the repository.
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #code #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # 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 URL

You can use this model via the github repository or via following code. More information can be found on the repository.