3 citations · 6 across the 4 of their papers we have counts for
6 papers
nerblackbox: A High-level Library for Named Entity Recognition in Python
Felix Stollenwerk
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 met…
Text Annotation Handbook: A Practical Guide for Machine Learning Projects
Felix Stollenwerk, Joey Öhman, Danila Petrelli +5
This handbook is a hands-on guide on how to approach text annotation tasks. It provides a gentle introduction to the topic, an overview of theoretical concepts as well as practical…
Annotated Job Ads with Named Entity Recognition
Felix Stollenwerk, Niklas Fastlund, Anna Nyqvist +1
We have trained a named entity recognition (NER) model that screens Swedish job ads for different kinds of useful information (e.g. skills required from a job seeker). It was obtai…
GPT-SW3: An Autoregressive Language Model for the Nordic Languages
Ariel Ekgren, Amaru Cuba Gyllensten, Felix Stollenwerk +7
This paper details the process of developing the first native large generative language model for the Nordic languages, GPT-SW3. We cover all parts of the development process, from…
Training and Evaluation of a Multilingual Tokenizer for GPT-SW3
Felix Stollenwerk
This paper provides a detailed discussion of the multilingual tokenizer used for GPT-SW3. It was trained on the Nordic Pile using the SentencePiece library and the BPE algorithm. W…
Adaptive Fine-Tuning of Transformer-Based Language Models for Named Entity Recognition
Felix Stollenwerk
The current standard approach for fine-tuning transformer-based language models includes a fixed number of training epochs and a linear learning rate schedule. In order to obtain a…