Description
Pretrained BertSentenceEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.sent_bert_large_nordic_pile_1m_steps is a Swedish model originally trained by AI-Sweden-Models.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
sentenceDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx") \
.setInputCols(["document"]) \
.setOutputCol("sentence")
embeddings = BertSentenceEmbeddings.pretrained("sent_bert_large_nordic_pile_1m_steps","sv") \
.setInputCols(["sentence"]) \
.setOutputCol("embeddings")
pipeline = Pipeline().setStages([documentAssembler, sentenceDL, embeddings])
data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val embeddings = BertSentenceEmbeddings.pretrained("sent_bert_large_nordic_pile_1m_steps","sv")
.setInputCols(Array("sentence"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, sentenceDL, embeddings))
val data = Seq("I love spark-nlp").toDF("text")
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)
Model Information
| Model Name: | sent_bert_large_nordic_pile_1m_steps |
| Compatibility: | Spark NLP 5.5.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence] |
| Output Labels: | [embeddings] |
| Language: | sv |
| Size: | 1.4 GB |
References
https://huggingface.co/AI-Sweden-Models/bert-large-nordic-pile-1M-steps