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README.md
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This model can be used with the Hugging Face Transformers library. Here is a quick example:
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tokenizer = AutoTokenizer.from_pretrained("ansumanpandey/codgen-finetuned-SQLQueryGeneration")
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model = AutoModelForCausalLM.from_pretrained("ansumanpandey/codgen-finetuned-SQLQueryGeneration")
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def get_sql(query):
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input_text = "Write a SQL query to %s </s>" % query
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features = tokenizer([input_text], return_tensors='pt')
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output = model.generate(input_ids=features['input_ids'],
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attention_mask=features['attention_mask'],
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max_new_tokens=70)
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sql_query= tokenizer.decode(output[0])
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return sql_query
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This model can be used with the Hugging Face Transformers library. Here is a quick example:
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The link:
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https://huggingface.co/ansumanpandey/codgen-finetuned-SQLQueryGeneration/blob/main/The%20SQL%20Generation%20Code
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