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README.md
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# text2sql-8b-instruct-v1
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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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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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## 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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## 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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- 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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### 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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## 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)
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