Description
Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.600_stmodel_brand_rem is a English model originally trained by jamiehudson.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
embeddings = MPNetEmbeddings.pretrained("600_stmodel_brand_rem","en") \
.setInputCols(["document"]) \
.setOutputCol("embeddings")
pipeline = Pipeline().setStages([documentAssembler, 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 embeddings = MPNetEmbeddings.pretrained("600_stmodel_brand_rem","en")
.setInputCols(Array("document"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, embeddings))
val data = Seq("I love spark-nlp").toDF("text")
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)
Model Information
| Model Name: | 600_stmodel_brand_rem |
| Compatibility: | Spark NLP 5.5.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document] |
| Output Labels: | [mpnet] |
| Language: | en |
| Size: | 407.2 MB |
References
https://huggingface.co/jamiehudson/600-STmodel-brand-rem