Description
Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. deformer is a English model originally trained by Lauler.
Predicted Entities
DE, ord, DEM
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_ner_deformer","en") \
.setInputCols(["sentence", "token"]) \
.setOutputCol("ner")
pipeline = Pipeline(stages=[documentAssembler, sentenceDetector, 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 sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols(Array("sentence"))
.setOutputCol("token")
val tokenClassifier = BertForTokenClassification.pretrained("bert_ner_deformer","en")
.setInputCols(Array("sentence", "token"))
.setOutputCol("ner")
val pipeline = new Pipeline().setStages(Array(documentAssembler,sentenceDetector, tokenizer, tokenClassifier))
val data = Seq("PUT YOUR STRING HERE").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.ner.bert.by_lauler").predict("""PUT YOUR STRING HERE""")
Model Information
| Model Name: | bert_ner_deformer |
| Compatibility: | Spark NLP 4.1.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 465.8 MB |
| Case sensitive: | true |
| Max sentence length: | 128 |
References
- https://huggingface.co/Lauler/deformer
- https://opus.nlpl.eu/download.php?f=wikimedia/v20210402/mono/sv.txt.gz
- https://opus.nlpl.eu/download.php?f=JRC-Acquis/mono/JRC-Acquis.raw.sv.gz
- https://opus.nlpl.eu/
- https://opus.nlpl.eu/download.php?f=Europarl/v8/mono/sv.txt.gz
- https://www4.isof.se/cgi-bin/srfl/visasvar.py?sok=dem%20som&svar=79718&log_id=705355