Create README.md

README.md

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+---
+language: en
+widget:
+- text: >-
+    convert question and table into SQL query. tables: people_name(id,name),
+    people_age(people_id,age). question: how many people with name jui and age
+    less than 25
+license: cc-by-sa-4.0
+---
+
+This is an upgraded version of [https://huggingface.co/juierror/flan-t5-text2sql-with-schema](https://huggingface.co/juierror/flan-t5-text2sql-with-schema). 
+
+It supports the '<' sign and can handle multiple tables.
+
+# How to use
+```python
+from typing import List
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("juierror/flan-t5-text2sql-with-schema-v2")
+model = AutoModelForSeq2SeqLM.from_pretrained("juierror/flan-t5-text2sql-with-schema-v2")
+
+def get_prompt(tables, question):
+    prompt = f"""convert question and table into SQL query. tables: {tables}. question: {question}"""
+    return prompt
+
+def prepare_input(question: str, tables: Dict[str, List[str]]):
+    tables = [f"""{table_name}({",".join(tables[table_name])})""" for table_name in tables]
+    tables = ", ".join(tables)
+    prompt = get_prompt(tables, question)
+    input_ids = tokenizer(prompt, max_length=512, return_tensors="pt").input_ids
+    return input_ids
+
+def inference(question: str, tables: Dict[str, List[str]]) -> str:
+    input_data = prepare_input(question=question, tables=tables)
+    input_data = input_data.to(model.device)
+    outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=512)
+    result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True)
+    return result
+
+print(inference("how many people with name jui and age less than 25", {
+    "people_name": ["id", "name"],
+    "people_age": ["people_id", "age"]
+}))
+
+print(inference("what is id with name jui and age less than 25", {
+    "people_name": ["id", "name", "age"]
+})))
+```