Update README.md

README.md

@@ -1,26 +1,43 @@
 ---
-library_name: transformers
-tags: []
----
-
-# prompt: write a model card in markdow for huggingface
-
-%%writefile README.md
----
 tags:
-- text2cypher
-- cypher
-- neo4j
-- natural-language-processing
-- artificial-intelligence
+  - text2cypher
+  - cypher
+  - neo4j
+  - natural-language-processing
+  - artificial-intelligence
 widget:
-- text: "Which employees joined the company after 2015?"
+  - text: "Which employees joined the company after 2015?"
 ---
 
 # Cypher Query Generator
 
-This model is a fine-tuned T5 model that can generate Cypher queries from natural language descriptions. It was trained on a dataset of WikiSQL examples converted to Cypher queries.
+## Model Overview
+The Cypher Query Generator is a fine-tuned T5 model designed to translate natural language descriptions into Cypher queries. This model was trained on a dataset derived from WikiSQL examples that have been converted into Cypher queries, making it well-suited for generating queries compatible with Neo4j databases.
+
+## Model Details
+- **Architecture**: T5
+- **Training Data**: WikiSQL examples converted to Cypher queries
+- **Primary Use Case**: Generating Cypher queries from natural language descriptions
 
 ## How to Use
+To generate a Cypher query using this model, you can provide a natural language description, and the model will output the corresponding Cypher query. For instance, if you want to find all employees who joined the company after 2015, you can use the following Python code:
+
+```python
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load pre-trained model and tokenizer
+model_name = 'your-model-name-here'
+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')
+
+# Generate Cypher query
+outputs = model.generate(inputs)
+cypher_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-You can use this model to generate Cypher queries from natural language descriptions. For example, to generate a Cypher query that finds all employees who joined the company after 2015, you can use the following code:
+print("Generated Cypher Query:", cypher_query)