Description
Pretrained XlmRoBertaSentenceEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.bge_m3 is a Multilingual model originally trained by BAII.
Predicted Entities
How to use
document_assembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("documents")
sentencerDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\
.setInputCols(["document"])\
.setOutputCol("sentence")
embeddings =XlmRoBertaSentenceEmbeddings.pretrained("bge_m3","xx") \
.setInputCols(["sentence"]) \
.setOutputCol("embeddings")
pipeline = Pipeline().setStages([document_assembler, sentencerDL, embeddings])
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("documents")
val sentencerDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
.setInputCols(["document"])
.setOutputCol("sentence")
val embeddings = XlmRoBertaSentenceEmbeddings
.pretrained("bge_m3", "xx")
.setInputCols(Array("sentence"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentencerDL, embeddings))
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)
Model Information
| Model Name: | bge_m3 |
| Compatibility: | Spark NLP 5.2.3+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence] |
| Output Labels: | [sentence_embeddings] |
| Language: | xx |
| Size: | 410.8 MB |
| Max sentence length: | 32 |
References
https://huggingface.co/BAAI/bge-m3