Description
“
Pretrained CamemBertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.
How to use
document_assembler = DocumentAssembler() .setInputCol("text") .setOutputCol("document")
tokenizer = Tokenizer() .setInputCols("document") .setOutputCol("token")
sequenceClassifier = CamemBertForSequenceClassification.pretrained("camembert_crossencoder_classification_large_mmarco","fr") .setInputCols(["document","token"]) .setOutputCol("class")
pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier])
data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val sequenceClassifier = CamemBertForSequenceClassification.pretrained("camembert_crossencoder_classification_large_mmarco","fr")
.setInputCols(Array("document","token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Model Information
| Model Name: | camembert_crossencoder_classification_large_mmarco |
| Compatibility: | Spark NLP 5.5.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document_question, document_context] |
| Output Labels: | [answer] |
| Language: | en |
| Size: | 411.0 MB |
| Case sensitive: | false |
| Max sentence length: | 512 |