Description
Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. umberto-commoncrawl-cased-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_commoncrawl_cased_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_commoncrawl_cased_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.cased").predict("""Adoro Spark NLP""")
Model Information
| Model Name: | camembert_embeddings_umberto_commoncrawl_cased_v1 |
| Compatibility: | Spark NLP 5.0.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [embeddings] |
| Language: | it |
| Size: | 263.1 MB |
| Case sensitive: | true |