Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. german-news-sentiment-bert is a German model originally trained by mdraw.
Predicted Entities
positive, negative, neutral
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_german_news_sentiment","de") \
.setInputCols(["document", "token"]) \
.setOutputCol("class")
pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded])
data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")
val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_german_news_sentiment","de")
.setInputCols(Array("document", "token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded))
val data = Seq("Ich liebe Spark NLP").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("de.classify.bert.news_sentiment.").predict("""Ich liebe Spark NLP""")
Model Information
| Model Name: | bert_classifier_german_news_sentiment |
| Compatibility: | Spark NLP 4.2.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | de |
| Size: | 408.6 MB |
| Case sensitive: | true |
| Max sentence length: | 256 |
References
- https://huggingface.co/mdraw/german-news-sentiment-bert
- https://github.com/text-analytics-20/news-sentiment-development
- https://github.com/text-analytics-20/news-sentiment-development/blob/main/sentiment_analysis/bert.py