Description
This model was imported from Hugging Face and it’s been fine-tuned for Italian language, leveraging Bert embeddings and BertForSequenceClassification for text classification purposes.
Predicted Entities
negative, positive, neutral
How to use
document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')
tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')
sequenceClassifier = BertForSequenceClassification \
.pretrained('bert_sequence_classifier_sentiment', 'it') \
.setInputCols(['token', 'document']) \
.setOutputCol('class')
pipeline = Pipeline(stages=[document_assembler, tokenizer, sequenceClassifier])
example = spark.createDataFrame([['Ho mal di testa e mi sento male.']]).toDF("text")
result = pipeline.fit(example).transform(example)
val document_assembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val tokenClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_sentiment", "it")
.setInputCols("document", "token")
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, sequenceClassifier))
val example = Seq.empty["Ho mal di testa e mi sento male."].toDS.toDF("text")
val result = pipeline.fit(example).transform(example)
import nlu
nlu.load("it.classify.sentiment").predict("""Ho mal di testa e mi sento male.""")
Results
['negative']
Model Information
| Model Name: | bert_sequence_classifier_sentiment |
| Compatibility: | Spark NLP 3.3.4+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | it |
| Size: | 415.5 MB |
| Case sensitive: | true |
| Max sentense length: | 512 |
Data Source
https://huggingface.co/neuraly/bert-base-italian-cased-sentiment
Benchmarking
label score
accuracy 0.82