Description
Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. ArBERTMo is a French model orginally trained by Ebtihal.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_ArBERTMo","fr") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["J'adore 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_ArBERTMo","fr")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("J'adore Spark NLP").toDF("text")
val result = pipeline.fit(data).transform(data)
Model Information
| Model Name: | camembert_embeddings_ArBERTMo |
| Compatibility: | Spark NLP 3.4.4+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [embeddings] |
| Language: | fr |
| Size: | 266.8 MB |
| Case sensitive: | true |
References
- https://huggingface.co/Ebtihal/ArBERTMo