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--- |
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library_name: peft |
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base_model: declare-lab/flan-alpaca-base |
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license: mit |
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language: |
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- en |
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pipeline_tag: text2text-generation |
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tags: |
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- sql |
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- query |
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- database |
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datasets: |
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- wikisql |
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--- |
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## Model Details |
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### Model Description |
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This model is based on the declare-lab/flan-alpaca-base model finetuned with wikisql dataset. |
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- **Developed by:** Jonathan Jordan |
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- **Model type:** FLAN Alpaca |
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- **Language(s) (NLP):** English |
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- **License:** [More Information Needed] |
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- **Finetuned from model:** declare-lab/flan-alpaca-base |
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## Uses |
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The model generates a string of SQL query based on a question and table columns. **The generated query always uses "table" as the table name**. |
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Feel free to change the table name in the generated query to match your actual SQL table. |
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The generated SQL query can be run perfectly on the python SQL connection (e.g. psycopg2, mysql_connector, etc). |
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#### Limitations |
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1. The question MUST be in english |
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2. Keep in mind about the difference in data type naming between MySQL and the other SQL databases |
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3. Simple SQL Aggregation functions (SUM, AVG, COUNT, MIN, MAX) are supported |
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4. Advanced SQL Aggregation which involves GROUP BY, ORDER BY, HAVING, etc are highly not recommended |
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5. Table JOIN is not supported |
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### Input Example |
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```python |
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"""Question: what is What was the result of the election in the Florida 18 district?\nTable: table_1341598_10 (result VARCHAR, district VARCHAR)\nSQL: """ |
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``` |
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### Output Example |
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```python |
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"""SELECT * FROM table WHERE district = "Florida 18"""" |
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``` |
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### How to use |
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Load model |
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```python |
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from peft import get_peft_config, get_peft_model, TaskType |
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from peft import PeftConfig, PeftModel |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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model_id = "jonathanjordan21/flan-alpaca-base-finetuned-lora-wikisql" |
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config = PeftConfig.from_pretrained(model_id) |
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model_ = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, return_dict=True) |
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tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
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model = PeftModel.from_pretrained(model_, model_id) |
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``` |
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Model inference |
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```python |
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question = "server of user id 11 with status active and server id 10" |
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table = "table_name_77 ( user id INTEGER, status VARCHAR, server id INTEGER )" |
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test = f"""Question: {question}\nTable: {table}\nSQL: """ |
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p = tokenizer(test, return_tensors='pt') |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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out = model.to(device).generate(**p.to(device),max_new_tokens=50) |
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print("SQL Query :", tokenizer.batch_decode(out,skip_special_tokens=True)[0]) |
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``` |
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## Performance |
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### Speed Performance |
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The model inference takes about 2-3 seconds to run in Google Colab Free Tier CPU |
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### Framework versions |
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- PEFT 0.6.2 |
