Description
Pretrained RobertaForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. roberta-base-ca-cased-ner is a Catalan model originally trained by projecte-aina.
Predicted Entities
LOC, PER, MISC, 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("roberta_ner_roberta_base_ca_cased_ner","ca") \
.setInputCols(["sentence", "token"]) \
.setOutputCol("ner")
pipeline = Pipeline(stages=[documentAssembler, sentenceDetector, tokenizer, tokenClassifier])
data = spark.createDataFrame([["M'encanta la Spark 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("roberta_ner_roberta_base_ca_cased_ner","ca")
.setInputCols(Array("sentence", "token"))
.setOutputCol("ner")
val pipeline = new Pipeline().setStages(Array(documentAssembler,sentenceDetector, tokenizer, tokenClassifier))
val data = Seq("M'encanta la Spark NLP").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("ca.ner.roberta.cased_base").predict("""M'encanta la Spark NLP""")
Model Information
| Model Name: | roberta_ner_roberta_base_ca_cased_ner |
| Compatibility: | Spark NLP 4.1.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [ner] |
| Language: | ca |
| Size: | 445.8 MB |
| Case sensitive: | true |
| Max sentence length: | 128 |
References
- https://huggingface.co/projecte-aina/roberta-base-ca-cased-ner
- https://arxiv.org/abs/1907.11692
- https://github.com/projecte-aina/club
- https://paperswithcode.com/sota?task=token-classification&dataset=ancora-ca-ner