Usage

python
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from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "oscardean/smollm2-135m-text2cypher"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

messages = [
    {
        "role": "system",
        "content": (
            "You translate natural-language questions into Cypher queries. "
            "Use only the supplied graph schema and return only the Cypher query."
        ),
    },
    {
        "role": "user",
        "content": (
            "Graph schema:\n"
            "Person {name: STRING}\n"
            "Movie {title: STRING, year: INTEGER}\n"
            "(Person)-[:DIRECTED]->(Movie)\n\n"
            "Question:\n"
            "Which movies did Christopher Nolan direct before 2010?"
        ),
    },
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
)

outputs = model.generate(
    inputs,
    max_new_tokens=192,
    do_sample=False,
)

prediction = tokenizer.decode(
    outputs[0, inputs.shape[1]:],
    skip_special_tokens=True,
)

print(prediction)

Training

Evaluation

Evaluated on the 50-sample test split.

Limitations

• May hallucinate labels, relationships, or properties.
• May omit filters, constants, or return fields.
• May repeat conditions.
• May use incorrect relationship directions or operators.
• May generate SQL-like syntax instead of valid Cypher.
• Can produce structurally plausible but semantically incorrect queries.
• Should be validated before execution.
• Not intended for direct production use.