Description
Pretrained XLMRoberta Embeddings model is a multilingual embedding model adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.
Predicted Entities
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = XlmRoBertaEmbeddings.pretrained("xlmroberta_embeddings_paraphrase_mpnet_base_v2","xx") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings") \
.setCaseSensitive(True)
pipeline = 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("document")
.setOutputCol("token")
val embeddings = XlmRoBertaEmbeddings.pretrained("xlmroberta_embeddings_paraphrase_mpnet_base_v2", "xx")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler,
tokenizer,
embeddings))
val data = Seq("I love Spark NLP").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Model Information
| Model Name: | xlmroberta_embeddings_paraphrase_mpnet_base_v2 |
| Compatibility: | Spark NLP 4.4.4+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [embeddings] |
| Language: | xx |
| Size: | 1.0 GB |
| Case sensitive: | true |
References
https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2