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Anugya
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Model Details
Model Provider
Anugya
Model Tree
HuggingFaceTB/SmolLM2-135M-Instruct
Input Modalities
Output Modalities
Supported Functionality
RomanTeucher/text2cypher-curatedpython
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))Evaluated on 50 test samples using:
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))