Albanian BertForTokenClassification Base Cased model (from akdeniz27)

Description

Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. mbert-base-albanian-cased-ner is a Albanian model originally trained by akdeniz27.

Predicted Entities

PER, ORG, LOC

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_ner_mbert_base_albanian_cased_ner","sq") \
    .setInputCols(["sentence", "token"]) \
    .setOutputCol("ner")

pipeline = Pipeline(stages=[documentAssembler, sentenceDetector, tokenizer, tokenClassifier])

data = spark.createDataFrame([["E dua shkëndijën 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_mbert_base_albanian_cased_ner","sq")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("ner")

val pipeline = new Pipeline().setStages(Array(documentAssembler,sentenceDetector, tokenizer, tokenClassifier))

val data = Seq("E dua shkëndijën nlp").toDF("text")

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("sq.ner.bert.cased_base").predict("""E dua shkëndijën nlp""")

Model Information

Model Name: bert_ner_mbert_base_albanian_cased_ner
Compatibility: Spark NLP 5.2.0+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [ner]
Language: sq
Size: 665.1 MB
Case sensitive: true
Max sentence length: 128

References

References

  • https://huggingface.co/akdeniz27/mbert-base-albanian-cased-ner
  • https://aclanthology.org/P17-1178.pdf