Instructions to use aieng-lab/ModernBERT-base_requirement-completion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aieng-lab/ModernBERT-base_requirement-completion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="aieng-lab/ModernBERT-base_requirement-completion")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("aieng-lab/ModernBERT-base_requirement-completion") model = AutoModelForMaskedLM.from_pretrained("aieng-lab/ModernBERT-base_requirement-completion") - Notebooks
- Google Colab
- Kaggle
ModernBERT base for filling user actions in requirement specifications
This model fills masks ([MASK]) in requirements specifications. During the fine-tuning process, POS verbs were used as a proxy of user actions.
- Developed by: Fabian C. Peña, Steffen Herbold
- Finetuned from: answerdotai/ModernBERT-base
- 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
- 6
Model tree for aieng-lab/ModernBERT-base_requirement-completion
Base model
answerdotai/ModernBERT-base