Description
Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.custom_legalbert is a English model originally trained by casehold.
How to use
document_assembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("documents")
embeddings =BertEmbeddings.pretrained("custom_legalbert","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 = BertEmbeddings
.pretrained("custom_legalbert", "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: | custom_legalbert |
| Compatibility: | Spark NLP 5.1.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [documents, token] |
| Output Labels: | [embeddings] |
| Language: | en |
| Size: | 411.6 MB |
References
https://huggingface.co/casehold/custom-legalbert