Swedish BERT Sentence Base Cased Embedding

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

Download Copy S3 URI

How to use

sent_embeddings = BertSentenceEmbeddings.pretrained("sent_bert_base_cased", "sv") \
.setInputCols("sentence") \
.setOutputCol("bert_sentence")

nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, sent_embeddings ])

val sent_embeddings = BertSentenceEmbeddings.pretrained("sent_bert_base_cased", "sv")
.setInputCols("sentence")
.setOutputCol("bert_sentence")

val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, sent_embeddings ))

import nlu
nlu.load("sv.embed_sentence.bert.base_cased").predict("""Put your text here.""")

Model Information

Model Name:	sent_bert_base_cased
Compatibility:	Spark NLP 3.2.2+
License:	Open Source
Edition:	Official
Input Labels:	[sentence]
Output Labels:	[bert_sentence]
Language:	sv
Case sensitive:	true

Data Source

The model is imported from: https://huggingface.co/KB/bert-base-swedish-cased

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