Description
This model was imported from Hugging Face (link) and it’s been finetuned on news texts about migration for German language, leveraging Bert embeddings and BertForSequenceClassification for text classification purposes.
Predicted Entities
positive, negative, neutral
How to use
document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')
tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')
sequenceClassifier = BertForSequenceClassification \
.pretrained('bert_sequence_classifier_news_sentiment', 'de') \
.setInputCols(['token', 'document']) \
.setOutputCol('class')
pipeline = Pipeline(stages=[document_assembler, tokenizer, sequenceClassifier])
example = spark.createDataFrame([['Die Zahl der Flüchtlinge in Deutschland steigt von Tag zu Tag.']]).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_news_sentiment", "de")
.setInputCols("document", "token")
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, sequenceClassifier))
val example = Seq.empty["Die Zahl der Flüchtlinge in Deutschland steigt von Tag zu Tag."].toDS.toDF("text")
val result = pipeline.fit(example).transform(example)
import nlu
nlu.load("de.classify.news_sentiment.bert").predict("""Die Zahl der Flüchtlinge in Deutschland steigt von Tag zu Tag.""")
Results
['neutral']
Model Information
| Model Name: | bert_sequence_classifier_news_sentiment |
| Compatibility: | Spark NLP 3.3.4+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | de |
| Size: | 408.7 MB |
| Case sensitive: | true |
| Max sentence length: | 512 |