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Found 6 papers, 1 papers with code
Fine-Grained Controllable Text Generation Using Non-Residual Prompting
We propose a resource-efficient method for converting a pre-trained CLM into this architecture, and demonstrate its potential on various experiments, including the novel task of contextualized word inclusion.
Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish
We present GTP-SW3, a 3. 5 billion parameter autoregressive language model, trained on a newly created 100 GB Swedish corpus.
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.
The Nordic Pile: A 1.2TB Nordic Dataset for Language Modeling
Pre-training Large Language Models (LLMs) require massive amounts of text data, and the performance of the LLMs typically correlates with the scale and quality of the datasets.
