Felix Stollenwerk


2024

pdf
GPT-SW3: An Autoregressive Language Model for the Scandinavian Languages
Ariel Ekgren | Amaru Cuba Gyllensten | Felix Stollenwerk | Joey Öhman | Tim Isbister | Evangelia Gogoulou | Fredrik Carlsson | Judit Casademont | Magnus Sahlgren
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

This paper details the process of developing the first native large generative language model for the North Germanic languages, GPT-SW3. We cover all parts of the development process, from data collection and processing, training configuration and instruction finetuning, to evaluation, applications, and considerations for release strategies. We discuss pros and cons of developing large language models for smaller languages and in relatively peripheral regions of the globe, and we hope that this paper can serve as a guide and reference for other researchers that undertake the development of large generative models for smaller languages.

2023

pdf
nerblackbox: A High-level Library for Named Entity Recognition in Python
Felix Stollenwerk
Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)

We present **nerblackbox**, a python library to facilitate the use of state-of-the-art transformer-based models for named entity recognition. It provides simple-to-use yet powerful methods to access data and models from a wide range of sources, for fully automated model training and evaluation as well as versatile model inference. While many technical challenges are solved and hidden from the user by default, **nerblackbox** also offers fine-grained control and a rich set of customizable features. It is thus targeted both at application-oriented developers as well as machine learning experts and researchers.