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Model: Anugya/text2cypher-smollm2
Source: Original Platform

README.md

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+---
+library_name: transformers
+base_model: HuggingFaceTB/SmolLM2-135M-Instruct
+tags:
+- text2cypher
+- cypher
+- graph
+- fine-tuned
+language:
+- en
+license: apache-2.0
+datasets:
+- RomanTeucher/text2cypher-curated
+---
+
+# Text2Cypher — SmolLM2-135M Fine-tuned
+
+A fine-tuned version of `SmolLM2-135M-Instruct` that generates Cypher queries from natural language questions and a graph schema.
+
+## Model Details
+
+- **Base model:** HuggingFaceTB/SmolLM2-135M-Instruct
+- **Model type:** Causal Language Model
+- **Language:** English
+- **License:** Apache 2.0
+- **Finetuned by:** Anugya Sahu
+
+## Training Data
+
+- **Dataset:** `RomanTeucher/text2cypher-curated`
+- 1000 training samples, 75 validation, 50 test
+- Each sample contains a graph schema, a natural language question, and a target Cypher query
+
+## How to Use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("Anugya/text2cypher-smollm2")
+tokenizer = AutoTokenizer.from_pretrained("Anugya/text2cypher-smollm2")
+tokenizer.pad_token = tokenizer.eos_token
+
+schema = "Movie {title, year}, Person {name}, (Person)-[:DIRECTED]->(Movie)"
+question = "Which movies did Christopher Nolan direct before 2010?"
+
+prompt = f"""### Schema:
+{schema}
+
+### Question:
+{question}
+
+### Cypher:"""
+
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=128, do_sample=False)
+generated = outputs[0][inputs["input_ids"].shape[1]:]
+print(tokenizer.decode(generated, skip_special_tokens=True))
+```
+
+## Training Details
+
+- **Full fine-tune** — all weights updated, no LoRA
+- **Epochs:** 3
+- **Learning rate:** 2e-4
+- **Batch size:** 4
+- **Max token length:** 256
+- **Hardware:** CPU (Apple M-series)
+- **Precision:** float32
+
+## Evaluation
+
+Evaluated on 50 test samples using:
+
+- **Exact Match** — strict comparison after lowercasing and stripping
+- **Token F1** — token overlap between prediction and ground truth
+
+## Limitations
+
+- 135M parameter model — generates Cypher that looks right but often isn't
+- No query execution validation against a real Neo4j database
+- May struggle with complex schemas or multi-hop queries
+- Trained on CPU with limited epochs — larger training would improve results