Description
Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. umberto-wikipedia-uncased-v1
is a Italian model orginally trained by Musixmatch
.
Predicted Entities
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_umberto_wikipedia_uncased_v1","it") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["Adoro 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 = CamemBertEmbeddings.pretrained("camembert_embeddings_umberto_wikipedia_uncased_v1","it")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("Adoro Spark NLP").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("it.embed.camembert.uncased").predict("""Adoro Spark NLP""")
Model Information
Model Name: | camembert_embeddings_umberto_wikipedia_uncased_v1 |
Compatibility: | Spark NLP 5.0.2+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [sentence, token] |
Output Labels: | [embeddings] |
Language: | it |
Size: | 262.7 MB |
Case sensitive: | false |