|
--- |
|
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."](https://ieeexplore.ieee.org/document/7495744) 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 |
