Description
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on aproximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and internet forums) aiming to provide a representative BERT model for Swedish text.
Predicted Entities
How to use
embeddings = BertEmbeddings.pretrained("bert_base_cased", "sv") \
.setInputCols("sentence", "token") \
.setOutputCol("embeddings")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings])
Model Information
Model Name: | bert_base_cased |
Compatibility: | Spark NLP 3.2.2+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [sentence, token] |
Output Labels: | [bert] |
Language: | sv |
Case sensitive: | true |
Data Source
The model is imported from: https://huggingface.co/KB/bert-base-swedish-cased