| BART Large CNN model trained for converting NLP queries to SQL queries. |
| The model was trained on the Spider dataset Link: https://yale-lily.github.io/spider |
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| The model was trained using Google colab. |
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| Hyperparameters: |
| "num epochs" = 3 |
| "learning rate" = 1e-5 |
| "batch size" = 8 |
| "weight decay" = 0.01 |
| "max input length" = 256 |
| "max target length" = 256 |
| "model name" : "facebook/bart-large-cnn" |
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| use code : |
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| from typing import List |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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| tokenizer = AutoTokenizer.from_pretrained("upadhyay/sql") |
| model = AutoModelForSeq2SeqLM.from_pretrained("upadhyay/sql") |
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| def prepare_input(question: str, table: List[str]): |
| table_prefix = "table:" |
| question_prefix = "question:" |
| join_table = ",".join(table) |
| inputs = f"{question_prefix} {question} {table_prefix} {join_table}" |
| input_ids = tokenizer(inputs, max_length=700, return_tensors="pt").input_ids |
| return input_ids |
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| def inference(question: str, table: List[str]) -> str: |
| input_data = prepare_input(question=question, table=table) |
| input_data = input_data.to(model.device) |
| outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=700) |
| result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True) |
| return result |
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| print(inference(question="what is salary?", table=["id", "name", "age"])) |
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