Update README.md

README.md

@@ -26,18 +26,17 @@ To generate a Cypher query using this model, you can provide a natural language
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 
 # Load pre-trained model and tokenizer
-model_name = 'your-model-name-here'
+model_name = 'VPrashant/cypher-gen'
 tokenizer = T5Tokenizer.from_pretrained(model_name)
 model = T5ForConditionalGeneration.from_pretrained(model_name)
 
-# Define input
-input_text = "Which employees joined the company after 2015?"
-
-# Tokenize input
-inputs = tokenizer.encode(input_text, return_tensors='pt')
+# Example input for testing
+test_input = "Which employees joined the company after 2015?"
+test_encoding = tokenizer(test_input, return_tensors="pt", max_length=128, truncation=True, padding="max_length")
 
 # Generate Cypher query
-outputs = model.generate(inputs)
-cypher_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
+output = model.generate(input_ids=test_encoding['input_ids'], max_length=128)
+generated_query = tokenizer.decode(output[0], skip_special_tokens=True)
+
+print("Generated Cypher Query:", generated_query)
 
-print("Generated Cypher Query:", cypher_query)