Description
This model identifies the sentiments (positive or negative) in French texts.
Predicted Entities
Live Demo Open in Colab Download Copy S3 URI
How to use
document = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
embeddings = BertSentenceEmbeddings\
.pretrained('labse', 'xx') \
.setInputCols(["document"])\
.setOutputCol("sentence_embeddings")
sentimentClassifier = ClassifierDLModel.pretrained("classifierdl_bert_sentiment", "fr") \
.setInputCols(["document", "sentence_embeddings"]) \
.setOutputCol("class")
fr_sentiment_pipeline = Pipeline(stages=[document, embeddings, sentimentClassifier])
light_pipeline = LightPipeline(fr_sentiment_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
result1 = light_pipeline.annotate("Mignolet vraiment dommage de ne jamais le voir comme titulaire")
result2 = light_pipeline.annotate("Je me sens bien, je suis heureux d'être de retour.")
print(result1["class"], result2["class"], sep = "\n")
val document = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val embeddings = BertSentenceEmbeddings
.pretrained("labse", "xx")
.setInputCols(Array("document"))
.setOutputCol("sentence_embeddings")
val sentimentClassifier = ClassifierDLModel.pretrained("classifierdl_bert_sentiment", "fr")
.setInputCols(Array("document", "sentence_embeddings"))
.setOutputCol("class")
val fr_sentiment_pipeline = new Pipeline().setStages(Array(document, embeddings, sentimentClassifier))
val light_pipeline = LightPipeline(fr_sentiment_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
val result1 = light_pipeline.annotate("Mignolet vraiment dommage de ne jamais le voir comme titulaire")
val result2 = light_pipeline.annotate("Je me sens bien, je suis heureux d'être de retour.")
import nlu
nlu.load("fr.classify.sentiment.bert").predict("""Mignolet vraiment dommage de ne jamais le voir comme titulaire""")
Results
['NEGATIVE']
['POSITIVE']
Model Information
| Model Name: | classifierdl_bert_sentiment |
| Compatibility: | Spark NLP 3.2.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [class] |
| Language: | fr |
Data Source
https://github.com/charlesmalafosse/open-dataset-for-sentiment-analysis/
Benchmarking
precision recall f1-score support
NEGATIVE 0.82 0.72 0.77 378
POSITIVE 0.92 0.95 0.94 1240
accuracy 0.90 1618
macro avg 0.87 0.84 0.85 1618
weighted avg 0.90 0.90 0.90 1618