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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
This is an upgraded version of https://huggingface.co/juierror/flan-t5-text2sql-with-schema.
It supports the '<' sign and can handle multiple tables.
How to use
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"]
})))
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