Turkish Named Entity Recognition (NER) Model

This repository is cloned from https://huggingface.co/akdeniz27/bert-base-turkish-cased-ner. This is the tensorflow version.

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("winvoker/bert-base-turkish-cased-ner-tf")
tokenizer = AutoTokenizer.from_pretrained("winvoker/bert-base-turkish-cased-ner-tf")
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