language: tr
widget:
- text: Mustafa Kemal Atatürk 19 Mayıs 1919'da Samsun'a çıktı.
Turkish Named Entity Recognition (NER) Model
This model is the fine-tuned model of "dbmdz/bert-base-turkish-cased" using a reviewed version of well known Turkish NER dataset (https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt).
Fine-tuning parameters:
task = "ner"
model_checkpoint = "dbmdz/bert-base-turkish-cased"
batch_size = 8
label_list = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']
max_length = 512
learning_rate = 2e-5
num_train_epochs = 3
weight_decay = 0.01
How to use:
model = AutoModelForTokenClassification.from_pretrained("akdeniz27/bert-base-turkish-cased-ner")
tokenizer = AutoTokenizer.from_pretrained("akdeniz27/bert-base-turkish-cased-ner")
ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="first")
ner("<your text here>")
Pls refer "https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html" for entity grouping with aggregation_strategy parameter.
Reference test results:
- accuracy: 0.9933935699477056
- f1: 0.9592969472710453
- precision: 0.9543530277931161
- recall: 0.9642923563325274
Evaluation results with the test sets proposed in "Küçük, D., Küçük, D., Arıcı, N. 2016. Türkçe Varlık İsmi Tanıma için bir Veri Kümesi ("A Named Entity Recognition Dataset for Turkish"). IEEE Sinyal İşleme, İletişim ve Uygulamaları Kurultayı. Zonguldak, Türkiye." paper.
Test Set Acc. Prec. Rec. F1-Score 20010000 0.9946 0.9871 0.9463 0.9662 20020000 0.9928 0.9134 0.9206 0.9170 20030000 0.9942 0.9814 0.9186 0.9489 20040000 0.9943 0.9660 0.9522 0.9590 20050000 0.9971 0.9539 0.9932 0.9732 20060000 0.9993 0.9942 0.9942 0.9942 20070000 0.9970 0.9806 0.9439 0.9619 20080000 0.9988 0.9821 0.9649 0.9735 20090000 0.9977 0.9891 0.9479 0.9681 20100000 0.9961 0.9684 0.9293 0.9485 Overall 0.9961 0.9720 0.9516 0.9617