Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language: en
|
| 3 |
+
datasets:
|
| 4 |
+
- wikisql
|
| 5 |
+
widget:
|
| 6 |
+
- text: 'question: get people name with age equal 25 table: id, name, age'
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# How to use
|
| 10 |
+
```python
|
| 11 |
+
from typing import List
|
| 12 |
+
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
|
| 13 |
+
|
| 14 |
+
tokenizer = AutoTokenizer.from_pretrained("juierror/text-to-sql-with-table-schema")
|
| 15 |
+
model = AutoModelForSeq2SeqLM.from_pretrained("juierror/text-to-sql-with-table-schema")
|
| 16 |
+
|
| 17 |
+
def prepare_input(question: str, table: List[str]):
|
| 18 |
+
table_prefix = "table:"
|
| 19 |
+
question_prefix = "question:"
|
| 20 |
+
join_table = ",".join(table)
|
| 21 |
+
inputs = f"{question_prefix} {question} {table_prefix} {join_table}"
|
| 22 |
+
input_ids = tokenizer(inputs, max_length=512, return_tensors="pt").input_ids
|
| 23 |
+
return input_ids
|
| 24 |
+
|
| 25 |
+
def inference(question: str, table: List[str]) -> str:
|
| 26 |
+
input_data = prepare_input(question=question, table=table)
|
| 27 |
+
input_data = input_data.to(model.device)
|
| 28 |
+
outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=700)
|
| 29 |
+
result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True)
|
| 30 |
+
return result
|
| 31 |
+
|
| 32 |
+
print(inference(question="get people name with age equal 25", table=["id", "name", "age"]))
|
| 33 |
+
```
|