Description
Türk Adlandırılmış Varlık Tanıma
bert_token_classifier_turkish_ner is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. This model has been trained to recognize four types of entities: location (LOC), organizations (ORG), and person (PER).
Predicted Entities
- B-LOC
- B-ORG
- B-PER
- I-LOC
- I-ORG
- I-PER
- O
How to use
document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')
tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')
tokenClassifier = BertForTokenClassification \
.pretrained('bert_token_classifier_turkish_ner', 'tr') \
.setInputCols(['token', 'document']) \
.setOutputCol('ner') \
.setCaseSensitive(False) \
.setMaxSentenceLength(512)
# since output column is IOB/IOB2 style, NerConverter can extract entities
ner_converter = NerConverter() \
.setInputCols(['document', 'token', 'ner']) \
.setOutputCol('entities')
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
tokenClassifier,
ner_converter
])
example = spark.createDataFrame([["İstanbul Türkiye'nin kuzeybatısında, Marmara kıyısı ve Boğaziçi boyunca, Haliç'i de çevreleyecek şekilde kurulmuştur."]]).toDF("text")
result = pipeline.fit(example).transform(example)
val document_assembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_turkish_ner", "tr")
.setInputCols("document", "token")
.setOutputCol("ner")
.setCaseSensitive(false)
.setMaxSentenceLength(512)
// since output column is IOB/IOB2 style, NerConverter can extract entities
val ner_converter = NerConverter()
.setInputCols("document", "token", "ner")
.setOutputCol("entities")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, tokenClassifier, ner_converter))
val example = Seq.empty["İstanbul Türkiye'nin kuzeybatısında, Marmara kıyısı ve Boğaziçi boyunca, Haliç'i de çevreleyecek şekilde kurulmuştur."].toDS.toDF("text")
val result = pipeline.fit(example).transform(example)
import nlu
nlu.load("tr.classify.token_bert.turkish_ner").predict("""İstanbul Türkiye'nin kuzeybatısında, Marmara kıyısı ve Boğaziçi boyunca, Haliç'i de çevreleyecek şekilde kurulmuştur.""")
Model Information
| Model Name: | bert_token_classifier_turkish_ner |
| Compatibility: | Spark NLP 3.2.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [token, document] |
| Output Labels: | [ner] |
| Language: | tr |
| Case sensitive: | false |
| Max sentense length: | 512 |
Data Source
https://huggingface.co/savasy/bert-base-turkish-ner-cased
Benchmarking
Eval Results:
* precision = 0.916400580551524
* recall = 0.9342309684101502
* f1 = 0.9252298787412536
* loss = 0.11335893666411284
Test Results:
* precision = 0.9192058759362955
* recall = 0.9303010230367262
* f1 = 0.9247201697271198
* loss = 0.11182546521618497