Description
Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.remote_sensing_distilbert_cased is a English model originally trained by Chramer.
How to use
document_assembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("documents")
embeddings =DistilBertEmbeddings.pretrained("remote_sensing_distilbert_cased","en") \
.setInputCols(["documents","token"]) \
.setOutputCol("embeddings")
pipeline = Pipeline().setStages([document_assembler, embeddings])
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("embeddings")
val embeddings = DistilBertEmbeddings
.pretrained("remote_sensing_distilbert_cased", "en")
.setInputCols(Array("documents","token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings))
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)
Model Information
| Model Name: | remote_sensing_distilbert_cased |
| Compatibility: | Spark NLP 5.1.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [documents, token] |
| Output Labels: | [embeddings] |
| Language: | en |
| Size: | 243.7 MB |
References
https://huggingface.co/Chramer/remote-sensing-distilbert-cased