Create README.md

README.md

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+---
+language: en
+pipeline_tag: text-generation
+tags:
+- text-to-sql
+- t5
+- natural-language-processing
+- sql
+license: apache-2.0
+datasets:
+- gretelai/synthetic_text_to_sql
+base_model:
+- Salesforce/codet5-base
+---
+
+# Text-to-SQL T5 Model (`16pramodh/t2s_model`)
+
+## Model Description
+This is a **T5-based text-to-SQL model** trained to convert **natural language questions** into **SQL queries**.  
+It works by taking in:
+
+natural language query [SEP] table schema
+
+and producing a SQL statement based on the provided database schema.
+
+The model is based on `T5ForConditionalGeneration` and supports **text2text-generation** via the Hugging Face Inference API.
+
+---
+
+## Intended Use
+- **Input:** English natural language question **plus** the database schema.
+- **Output:** SQL query that can be executed on the described database.
+
+---
+
+## Example
+
+**Input:**
+Get the names and emails of all customers who signed up after January 1, 2024 [SEP] CREATE TABLE customers (customer_id INT PRIMARY KEY, name VARCHAR(50), email VARCHAR(100), signup_date DATE);
+
+**Output:**
+SELECT name, email FROM customers WHERE signup_date > '2024-01-01';
+
+---
+
+## How to Use
+
+### Hugging Face Inference API
+```bash
+curl -X POST \
+  -H "Authorization: Bearer YOUR_HF_TOKEN" \
+  -H "Content-Type: application/json" \
+  -d '{"inputs": "Get the names and emails of all customers who signed up after January 1, 2024 [SEP] CREATE TABLE customers (customer_id INT PRIMARY KEY, name VARCHAR(50), email VARCHAR(100), signup_date DATE);"}' \
+  https://api-inference.huggingface.co/models/16pramodh/t2s_model
+```
+
+### Python (Transformers)
+```
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+model_name = "16pramodh/t2s_model"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+input_text = "Get the names and emails of all customers who signed up after January 1, 2024 [SEP] CREATE TABLE customers (customer_id INT PRIMARY KEY, name VARCHAR(50), email VARCHAR(100), signup_date DATE);"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+---