Model Card for Model ID
Model Details
Model Description
This model is fine-trained from the google/flan-t5-base model to achieve better accuracy on generating SQL Queries. It has been trained to generate sql queries given a question and database schema(s).
It can be used in any of such applications where sql queries are needed (particularly Postgres queries).
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Oghenerunor Adjekpiyede
- Model type: Text2TextGeneration
- Language(s) (NLP): English
- License: MIT
- Finetuned from model [optional]: google/flan-t5-base
Model Sources [optional]
- Repository: https://huggingface.co/kampkelly/sql-generator
Uses
This model is to be used and performs well for generating SQL queries. This model for other tasks may not give satisfactory performance on generating text in other general use cases.
Direct Use
Use with transformers
from peft import PeftModel
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_base = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base", torch_dtype=torch.bfloat16, trust_remote_code=True)
model = PeftModel.from_pretrained(model_base,
peft_model_path,
torch_dtype=torch.bfloat16,
is_trainable=False)
input_ids = tokenizer(prompt, padding="max_length", max_length=300, truncation=True, return_tensors="pt").input_ids
model_output = model.generate(input_ids=input_ids, max_new_tokens = 300, use_cache = True,
num_beams=3,
do_sample=True,
top_k=50,
top_p=0.75,
temperature=0.1,
early_stopping=True
)
model_text_output = tokenizer.decode(model_output[0], skip_special_tokens=True)
print(model_text)
Bias, Risks, and Limitations
This model is particularly good for generating SQL Select
statement queries. Other types of query statements such as Create, Delete, Update, etc are not fully supported.