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README.md

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+---
+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:
+1. **Local ML (T5)**: Generates the initial high-speed SQL draft.
+2. **Gemini Auditor**: Validates the draft against the actual schema, adds double-quotes, and fixes hallucinations.
+3. **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](https://github.com/sumit08099/NLP_SQL_GEN)
+- **Frontend**: React 19 / Vite
+- **Backend**: FastAPI / LangGraph
+
+## 🛠 Usage (Hugging Face Transformers)
+```python
+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))
+```
+
+## 👥 Lead Engineers
+Developed and optimized by **Sumit** & **Karan**.