README.md

metadata

library_name: transformers
tags: []

BERT Transformer Model Trained on Custom Database

This is a BERT model fine-tuned on the Custom dataset for SQL query generation.

Model Details

• Model Type: BERT
• Training Data: Custom dataset
• Use Case: SQL query generation from natural language questions

Usage

You can use this model with the Hugging Face transformers library:

from transformers import BertTokenizer, BertForSequenceClassification

tokenizer = BertTokenizer.from_pretrained('VPrashant/sql_bert')
model = BertForSequenceClassification.from_pretrained('VPrashant/sql_bert')

def predict_sql_query(question, tokenizer, model):
    inputs = tokenizer(question, return_tensors='pt', max_length=128, truncation=True, padding='max_length')
    
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    predicted_label = torch.argmax(logits, dim=1).item()
    reverse_label_map = {i: query for query, i in label_map.items()}
    predicted_query = reverse_label_map[predicted_label]
    
    return predicted_query

question = "Which projects have more than 5 employees working on them?"
# Predict the SQL query
predicted_query = predict_sql_query(question, tokenizer, model)
print(f"Predicted SQL Query: {predicted_query}")