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Found 5 papers, 2 papers with code
Fine-grained Named Entity Annotation for Finnish
We introduce a corpus with fine-grained named entity annotation for Finnish, following the OntoNotes guidelines to create a resource that is cross-lingually compatible with existing annotations for other languages.
FinGPT: Large Generative Models for a Small Language
We pursue two approaches to pretrain models: 1) we train seven monolingual models from scratch (186M to 13B parameters) dubbed FinGPT, 2) we continue the pretraining of the multilingual BLOOM model on a mix of its original training data and Finnish, resulting in a 176 billion parameter model we call BLUUMI.
Exploring Cross-sentence Contexts for Named Entity Recognition with BERT
We find that adding context in the form of additional sentences to BERT input systematically increases NER performance on all of the tested languages and models.
A Broad-coverage Corpus for Finnish Named Entity Recognition
We present a new manually annotated corpus for broad-coverage named entity recognition for Finnish.
Multilingual is not enough: BERT for Finnish
Deep learning-based language models pretrained on large unannotated text corpora have been demonstrated to allow efficient transfer learning for natural language processing, with recent approaches such as the transformer-based BERT model advancing the state of the art across a variety of tasks.
