Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. NER_legal_de is a German model originally trained by Sahajtomar.
Predicted Entities
UN, ST, VO, VS, ORG, STR, RS, LIT, PER, GRT, LD, INN, VT, AN, EUN, MRK, RR, GS, LDS
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_ner_legal","de") \
.setInputCols(["document", "token"]) \
.setOutputCol("ner")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, tokenClassifier])
data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_ner_legal","de")
.setInputCols(Array("document", "token"))
.setOutputCol("ner")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, tokenClassifier))
val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("de.ner.bert.legal").predict("""PUT YOUR STRING HERE""")
Model Information
| Model Name: | bert_token_classifier_ner_legal |
| Compatibility: | Spark NLP 4.2.4+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | de |
| Size: | 1.3 GB |
| Case sensitive: | true |
| Max sentence length: | 256 |
References
- https://huggingface.co/Sahajtomar/NER_legal_de