# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("aieng-lab/starcoder2-3b_requirement-type")
model = AutoModelForSequenceClassification.from_pretrained("aieng-lab/starcoder2-3b_requirement-type")Quick Links
StarCoder2 3b for classifying requirements
This model classifies requirement specifications as 'functional' or 'non-functional'.
- Developed by: Fabian C. Peña, Steffen Herbold
- Finetuned from: bigcode/starcoder2-3b
- Replication kit: https://github.com/aieng-lab/senlp-benchmark
- Language: English
- License: MIT
Citation
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
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Model tree for aieng-lab/starcoder2-3b_requirement-type
Base model
bigcode/starcoder2-3b
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aieng-lab/starcoder2-3b_requirement-type")