German BERT Base Cased Model


The source data for the model consists of a recent Wikipedia dump, EU Bookshop corpus, Open Subtitles, CommonCrawl, ParaCrawl and News Crawl. This results in a dataset with a size of 16GB and 2,350,234,427 tokens. The model is trained with an initial sequence length of 512 subwords and was performed for 1.5M steps.


How to use

embeddings = BertEmbeddings.pretrained("bert_base_german_cased", "de") \
.setInputCols("sentence", "token") \
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings])
val embeddings = BertEmbeddings.pretrained("bert_base_german_cased", "de")
.setInputCols("sentence", "token")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings))
import nlu
nlu.load("de.embed.bert").predict("""Put your text here.""")

Model Information

Model Name: bert_base_german_cased
Compatibility: Spark NLP 3.1.0+
License: Open Source
Edition: Official
Input Labels: [token, sentence]
Output Labels: [embeddings]
Language: de
Case sensitive: true

Data Source


For results on downstream tasks like NER or PoS tagging, please refer to
[this repository](