Search Results for author:
Found 6 papers, 2 papers with code
nerblackbox: A High-level Library for Named Entity Recognition in Python
We present nerblackbox, a python library to facilitate the use of state-of-the-art transformer-based models for named entity recognition.
Text Annotation Handbook: A Practical Guide for Machine Learning Projects
This handbook is a hands-on guide on how to approach text annotation tasks.
Annotated Job Ads with Named Entity Recognition
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).
GPT-SW3: An Autoregressive Language Model for the Nordic Languages
This paper details the process of developing the first native large generative language model for the Nordic languages, GPT-SW3.
Training and Evaluation of a Multilingual Tokenizer for GPT-SW3
This paper provides a detailed discussion of the multilingual tokenizer used for GPT-SW3.
Adaptive Fine-Tuning of Transformer-Based Language Models for Named Entity Recognition
In this paper, we introduce adaptive fine-tuning, which is an alternative approach that uses early stopping and a custom learning rate schedule to dynamically adjust the number of training epochs to the dataset size.
