Description
This model was imported from Hugging Face and it’s been fine-tuned for 6 Scandinavian languages (Danish, Norwegian-Bokmål, Norwegian-Nynorsk, Swedish, Icelandic, Faroese), leveraging Bert embeddings and BertForTokenClassification for NER purposes.
Predicted Entities
PER, ORG, LOC, MISC
Live Demo Open in Colab Download Copy S3 URI
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_scandi_ner", "xx"))\
.setInputCols(["sentence",'token'])\
.setOutputCol("ner")
ner_converter = NerConverter()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
nlpPipeline = Pipeline(stages=[documentAssembler, sentenceDetector, tokenizer, tokenClassifier, ner_converter])
empty_data = spark.createDataFrame([[""]]).toDF("text")
model = nlpPipeline.fit(empty_data)
text = """Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner."""
result = model.transform(spark.createDataFrame([[text]]).toDF("text"))
val documentAssembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val tokenizer = Tokenizer()
.setInputCols(Array("sentence"))
.setOutputCol("token")
val tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_scandi_ner", "xx"))
.setInputCols(Array("sentence","token"))
.setOutputCol("ner")
ner_converter = NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(documentAssembler, sentenceDetector, tokenizer, tokenClassifier, ner_converter))
val example = Seq.empty["Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner."].toDS.toDF("text")
val result = pipeline.fit(example).transform(example)
import nlu
nlu.load("xx.ner.scandinavian").predict("""Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.""")
Results
+-------------------+---------+
|chunk |ner_label|
+-------------------+---------+
|Hans |PER |
|Statens Universitet|ORG |
|København |LOC |
|københavner |MISC |
+-------------------+---------+
Model Information
| Model Name: | bert_token_classifier_scandi_ner |
| Compatibility: | Spark NLP 3.3.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [ner] |
| Language: | xx |
| Size: | 666.9 MB |
| Case sensitive: | true |
| Max sentense length: | 256 |
Data Source
https://huggingface.co/saattrupdan/nbailab-base-ner-scandi
Benchmarking
languages : F1 Score:
---------- --------
Danish 0.8744
Bokmål 0.9106
Nynorsk 0.9042
Swedish 0.8837
Icelandic 0.8861
Faroese 0.9022
PREVIOUSDutch NER Model