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--- |
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license: mit |
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tags: |
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- code |
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--- |
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# What does this model do? |
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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 |
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You can use this model via the github repository or via following code. More information can be found on the repository. |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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import torch |
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model = AutoModelForSeq2SeqLM.from_pretrained("Chirayu/nl2kql") |
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tokenizer = AutoTokenizer.from_pretrained("Chirayu/nl2kql") |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model = model.to(device) |
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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''' |
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def generate_query( |
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textual_query: str, |
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num_beams: int = 10, |
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max_length: int = 128, |
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repetition_penalty: int = 2.5, |
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length_penalty: int = 1, |
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early_stopping: bool = True, |
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top_p: int = 0.95, |
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top_k: int = 50, |
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num_return_sequences: int = 1, |
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) -> str: |
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input_ids = tokenizer.encode( |
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textual_query, return_tensors="pt", add_special_tokens=True |
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) |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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input_ids = input_ids.to(device) |
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generated_ids = model.generate( |
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input_ids=input_ids, |
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num_beams=num_beams, |
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max_length=max_length, |
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repetition_penalty=repetition_penalty, |
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length_penalty=length_penalty, |
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early_stopping=early_stopping, |
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top_p=top_p, |
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top_k=top_k, |
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num_return_sequences=num_return_sequences, |
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) |
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query = [ |
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tokenizer.decode( |
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generated_id, |
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skip_special_tokens=True, |
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clean_up_tokenization_spaces=True, |
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) |
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for generated_id in generated_ids |
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][0] |
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return query |
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``` |
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