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@@ -8,7 +8,7 @@ language:
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  # What does this model do?
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  This model converts the natural language input to MongoDB (MQL) 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.
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  ```python
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  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
@@ -17,6 +17,8 @@ model = AutoModelForSeq2SeqLM.from_pretrained("Chirayu/nl2mongo")
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  tokenizer = AutoTokenizer.from_pretrained("Chirayu/nl2mongo")
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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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  def generate_query(
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  textual_query: str,
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  num_beams: int = 10,
 
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  # What does this model do?
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  This model converts the natural language input to MongoDB (MQL) 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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  tokenizer = AutoTokenizer.from_pretrained("Chirayu/nl2mongo")
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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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+
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+ textual_query = '''mongo: which cabinet has average age less than 21? | titanic : _id, passengerid, survived, pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked'''
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  def generate_query(
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  textual_query: str,
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  num_beams: int = 10,