Description
Pretrained BGEEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.bge_small_en_v1.5 is a English model originally trained by BAAI.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
embeds = BGEEmbeddings.pretrained("bge_small_en_v1.5","en") \
.setInputCols(["document"]) \
.setOutputCol("embeddings")
pipeline = Pipeline().setStages([documentAssembler, embeds])
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 embeds = BGEEmbeddings.pretrained("bge_small_en_v1.5","en")
.setInputCols(Array("document"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, embeds))
val data = Seq("I love spark-nlp").toDF("text")
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)
Model Information
| Model Name: | bge_small_en_v1.5 |
| Compatibility: | Spark NLP 5.5.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document] |
| Output Labels: | [bge] |
| Language: | en |
| Size: | 78.9 MB |
References
https://huggingface.co/BAAI/bge-small-en-v1.5