metadata
language:
- en
license: mit
tags:
- text-to-sql
- t5
- nlp2sql
- agentic-ai
- postgresql
datasets:
- spider
metrics:
- accuracy
model-index:
- name: T5-NL2SQL-Gen
results: []
🤖 T5-NL2SQL-Gen Specialist
This model is a fine-tuned T5-Small architecture specialized for converting Natural Language questions into precise PostgreSQL queries. It serves as the primary "Reasoning Specialist" within the NLP2SQL Autonomous Intelligence Layer.
🚀 Model Details
- Architecture: T5 (Text-to-Text Transfer Transformer)
- Specialization: PostgreSQL Query Generation
- Training Data: Fine-tuned on SQL-specific datasets (Spider/WikiSQL) and custom schema-mapped samples.
- Project Role: Acts as the initial SQL Generator in a Hybrid Agentic loop.
🔄 Hybrid Agentic Flow
This model is designed to work in tandem with Large Language Models (like Gemini 2.0 Flash) in a structured multi-agent workflow:
- Local ML (T5): Generates the initial high-speed SQL draft.
- Gemini Auditor: Validates the draft against the actual schema, adds double-quotes, and fixes hallucinations.
- Self-Healing Loop: If execution fails, the agents use this model's logic to refine the plan.
🔗 Project Context
This model is the engine for the NLP2SQL Platform.
- GitHub Repository: sumit08099/NLP_SQL_GEN
- Frontend: React 19 / Vite
- Backend: FastAPI / LangGraph
🛠 Usage (Hugging Face Transformers)
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = "Karan6124/t5-nl2sql-gen"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
input_text = "translate English to SQL: How many users signed up in the last 30 days? \n Context: Table users (id, username, created_at)"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))