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Model: oscardean/smollm2-135m-text2cypher
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- HuggingFaceTB/SmolLM2-135M-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text2cypher
+- cypher
+- graph-databases
+- supervised-fine-tuning
+- cpu-training
+---
+
+# SmolLM2-135M Text2Cypher
+
+Fine-tuned `HuggingFaceTB/SmolLM2-135M-Instruct` for generating Cypher queries from a graph schema and a natural-language question.
+
+## Usage
+
+```python
+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
+
+| Hyperparameter          |                  Value |
+| ----------------------- | ---------------------: |
+| Training samples        |                  1,000 |
+| Validation samples      |                     75 |
+| Epochs                  |                      3 |
+| Learning rate           |                 `5e-5` |
+| Batch size              |                      2 |
+| Gradient accumulation   |                      4 |
+| Effective batch size    |                      8 |
+| Weight decay            |                 `0.01` |
+| Warmup ratio            |                 `0.05` |
+| Maximum sequence length |                    800 |
+| Decoding                |                 Greedy |
+| Checkpoint selection    | Lowest validation loss |
+
+## Evaluation
+
+Evaluated on the 50-sample test split.
+
+| Metric                 |   Base | Fine-tuned |
+| ---------------------- | -----: | ---------: |
+| Basic query structure  |  2.00% |    100.00% |
+| Token F1               | 12.35% |     55.20% |
+| Node-label agreement   |  0.00% |     58.00% |
+| Component match rate   | 29.20% |     49.60% |
+| Normalized exact match |  0.00% |      0.00% |
+
+## 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.