Description
Pretrained RoBerta Embeddings model is a English Large Legal embeddings model adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.
How to use
documentAssembler = nlp.DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = nlp.Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = nlp.RoBertaEmbeddings.pretrained("roberta_legal_embedding_xlm_roberta_large","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = nlp.Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["I Love spark nlp"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")
val embeddings = nlp.RoBertaEmbeddings.pretrained("roberta_legal_embedding_xlm_roberta_large","en")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("I Love spark nlp").toDF("text")
val result = pipeline.fit(data).transform(data)
Model Information
| Model Name: | roberta_legal_embedding_xlm_roberta_large_spark_nlp |
| Compatibility: | Spark NLP 4.4.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [embeddings] |
| Language: | en |
| Size: | 1.6 GB |
| Case sensitive: | true |
References
https://huggingface.co/joelito/legal-xlm-roberta-base