Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. german-sentiment-bert
is a German model originally trained by oliverguhr
.
Predicted Entities
neutral
, positive
, negative
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_german_sentiment","de") \
.setInputCols(["document", "token"]) \
.setOutputCol("class")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier])
data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCols(Array("text"))
.setOutputCols(Array("document"))
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_german_sentiment","de")
.setInputCols(Array("document", "token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier))
val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("de.classify.bert.sentiment.by_oliverguhr").predict("""PUT YOUR STRING HERE""")
Model Information
Model Name: | bert_classifier_german_sentiment |
Compatibility: | Spark NLP 4.1.0+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [document, token] |
Output Labels: | [class] |
Language: | de |
Size: | 408.7 MB |
Case sensitive: | true |
Max sentence length: | 256 |
References
- https://huggingface.co/oliverguhr/german-sentiment-bert
- http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.202.pdf
- https://pypi.org/project/germansentiment/
- https://github.com/oliverguhr/german-sentiment
- http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.202.pdf
- https://www.romanklinger.de/scare/
- https://www.aclweb.org/anthology/L16-1181/
- https://www.spinningbytes.com/resources/germansentiment/
- https://wortschatz.uni-leipzig.de/de/download/german