Description
Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-uncased-swa is a Swahili model originally trained by arnolfokam.
Predicted Entities
DATE, LOC, PER, ORG
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_bert_base_uncased_swa","sw") \
.setInputCols(["sentence", "token"]) \
.setOutputCol("ner")
pipeline = Pipeline(stages=[documentAssembler, sentenceDetector, tokenizer, tokenClassifier])
data = spark.createDataFrame([["Ninapenda cheche NLP"]]).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_bert_base_uncased_swa","sw")
.setInputCols(Array("sentence", "token"))
.setOutputCol("ner")
val pipeline = new Pipeline().setStages(Array(documentAssembler,sentenceDetector, tokenizer, tokenClassifier))
val data = Seq("Ninapenda cheche NLP").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("sw.ner.bert.uncased_base").predict("""Ninapenda cheche NLP""")
Model Information
| Model Name: | bert_ner_bert_base_uncased_swa |
| Compatibility: | Spark NLP 4.0.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [ner] |
| Language: | sw |
| Size: | 404.2 MB |
| Case sensitive: | false |
| Max sentence length: | 128 |
References
- https://huggingface.co/arnolfokam/bert-base-uncased-swa
- https://github.com/masakhane-io/masakhane-ner
- https://www.paperspace.com