Instructions to use aieng-lab/bert-base-cased_smell-doc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aieng-lab/bert-base-cased_smell-doc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aieng-lab/bert-base-cased_smell-doc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aieng-lab/bert-base-cased_smell-doc") model = AutoModelForSequenceClassification.from_pretrained("aieng-lab/bert-base-cased_smell-doc") - Notebooks
- Google Colab
- Kaggle
BERT base for classifying smell documentation (multi-label)
This model classifies smell documentation as 'fragmented', 'tangled', 'excessive', 'bloated' or 'lazy'.
- Developed by: Fabian C. Peña, Steffen Herbold
- Finetuned from: bert-base-cased
- 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}
}
- Downloads last month
- 2
Model tree for aieng-lab/bert-base-cased_smell-doc
Base model
google-bert/bert-base-cased