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Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Uses
context = """
"""
input_question = ""
prompt = f"""
Instruction:
Based on the database schema description, generate the correct SQL query to answer the following question.
Context:
{context}
Input:
{input_question}
Output:
"""
tokens = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(
**tokens,
temperature=1,
max_new_tokens=100,
repetition_penalty=1.2,
top_p=0.9,
no_repeat_ngram_size=3
)
result = tokenizer.decode(output[0], skip_special_tokens=True)
Chỉ lấy phần sau "### Output:"
import re
output_start = re.search(r"### Output:\s*(.*)", result, re.DOTALL)
if output_start:
result = output_start.group(1).strip()
print(result)
Direct Use
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
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Training Hyperparameters
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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Model Architecture and Objective
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Compute Infrastructure
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Software
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