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