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  # text2sql-8b-instruct-v1
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- ## Summary
 
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  it is a natural language-to-SQL conversion model optimized specifically for Chinese and English users. It is based on the llama-3-chinese-8b-instruct-v3 model. We used the latest optimization algorithms to improve the performance of the model, especially in handling complex queries and multi-table joins.
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- ## Usage:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  Please upgrade the `transformers` package to ensure it supports Llama3 models. The current version we are using is `4.41.2`.
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  ```python
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  # Use a pipeline as a high-level helper
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  ```
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- ## Ethical Considerations
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  While fine-tuned for text to sql, this model inherits the ethical considerations of the base Llama 3 model. Use responsibly and implement additional safeguards as needed for your application.
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- ## Availability
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  The model is available through:
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  - [Hugging Face](https://huggingface.co/xbrain/text2sql-8b-instruct-v1)
 
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  # text2sql-8b-instruct-v1
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+
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+ ## 1. Summary
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  it is a natural language-to-SQL conversion model optimized specifically for Chinese and English users. It is based on the llama-3-chinese-8b-instruct-v3 model. We used the latest optimization algorithms to improve the performance of the model, especially in handling complex queries and multi-table joins.
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+ ### 1.1 characteristics
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+
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+ - Bilingual support: Ability to handle natural language queries in both Chinese and English languages.
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+ - High accuracy: After a large number of tests on actual database queries, it has been proved that the SQL statements generated have high accuracy.
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+
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+ ### 1.2 training data
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+ Training data for the model comes from multiple sources, including:
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+ - Open source databases (such as WikiSQL, Spider)
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+ - Internally generated dataset covering a variety of query types and complexities
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+ - User feedback data for continuous improvement of model performance
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+ Training data is strictly screened and cleaned to ensure data quality and diversity.
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+ ### 1.3 test results
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+ Test results on multiple benchmark datasets show the model exceeds other existing models in terms of accuracy and generation efficiency. For example:
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+ - On the WikiSQL dataset, the model achieved an execution accuracy rate of 87.5%.
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+ - On the Spider dataset, the model achieved an execution accuracy rate of 95.3%.
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+ These results show the model has significant advantages in handling complex queries and multi-table joins.
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+
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+ ## 2. Usage:
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  Please upgrade the `transformers` package to ensure it supports Llama3 models. The current version we are using is `4.41.2`.
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  ```python
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  # Use a pipeline as a high-level helper
 
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  ```
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+ ## 3. Ethical Considerations
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  While fine-tuned for text to sql, this model inherits the ethical considerations of the base Llama 3 model. Use responsibly and implement additional safeguards as needed for your application.
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+ ## 4. Availability
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  The model is available through:
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  - [Hugging Face](https://huggingface.co/xbrain/text2sql-8b-instruct-v1)