Description
This model was imported from Hugging Face and it’s been fine-tuned for the German language, leveraging XLM-RoBERTa embeddings and XlmRobertaForTokenClassification for NER purposes.
Predicted Entities
PER, ORG, LOC, MISC
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 = XlmRoBertaForTokenClassification.pretrained("xlm_roberta_large_token_classifier_conll03", "de"))\
.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 = """Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte."""
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 = XlmRoBertaForTokenClassification.pretrained("xlm_roberta_large_token_classifier_conll03", "de"))
.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["Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte."].toDS.toDF("text")
val result = pipeline.fit(example).transform(example)
import nlu
nlu.load("de.ner.xlm").predict("""Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.""")
Results
+----------------------+---------+
|chunk |ner_label|
+----------------------+---------+
|Ibser |PER |
|ASK Ebreichsdorf |ORG |
|Admira Wacker Mödling |ORG |
+----------------------+---------+
Model Information
| Model Name: | xlm_roberta_large_token_classifier_conll03 |
| Compatibility: | Spark NLP 3.3.4+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [ner] |
| Language: | de |
| Size: | 1.8 GB |
| Case sensitive: | true |
| Max sentense length: | 256 |
Data Source
https://huggingface.co/xlm-roberta-large-finetuned-conll03-german