Description
CamemBertForSequenceClassification can load CamemBERT model with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks.
camembert_large_sequence_classifier_allocine is a fine-tuned CamemBERT model on French Allociné dataset that is ready to be used for Sequence Classification sentiment analysis task abd it achieves state-of-the-art performance.
We used TFCamembertForSequenceClassification in Transformers to train this model and used CamemBertForSequenceClassification annotator in Spark NLP 🚀 for prediction at scale!
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = Tokenizer()\
.setInputCols(['document'])\
.setOutputCol('token')
sequenceClassifier = CamemBertForSequenceClassification.pretrained("camembert_large_sequence_classifier_allocine", "fr")\
.setInputCols(["document", "token"])\
.setOutputCol("class")\
.setCaseSensitive(True)\
.setMaxSentenceLength(512)
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
sequenceClassifier
])
example = spark.createDataFrame([['I really liked that movie!']]).toDF("text")
result = pipeline.fit(example).transform(example)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val sequenceClassifier = CamemBertForSequenceClassification.pretrained("camembert_large_sequence_classifier_allocine", "fr")
.setInputCols("document", "token")
.setOutputCol("class")
.setCaseSensitive(true)
.setMaxSentenceLength(512)
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, sequenceClassifier))
val example = Seq("I really liked that movie!").toDS.toDF("text")
val result = pipeline.fit(example).transform(example)
import nlu
nlu.load("fr.classify.camembert.large").predict("""I really liked that movie!""")
Model Information
| Model Name: | camembert_large_sequence_classifier_allocine |
| Compatibility: | Spark NLP 4.2.5+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [token, document] |
| Output Labels: | [class] |
| Language: | fr |
| Size: | 1.3 GB |
| Case sensitive: | true |
| Max sentence length: | 512 |