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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 "xlm-roberta-base" |
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(a multilingual version of RoBERTa) |
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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 = "xlm-roberta-base" |
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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 = 4 |
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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/xlm-roberta-base-turkish-ner") |
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tokenizer = AutoTokenizer.from_pretrained("akdeniz27/xlm-roberta-base-turkish-ner") |
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ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="none") |
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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: 0.9919343118732742 |
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* f1: 0.945422814532762 |
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* precision: 0.9366551398931153 |
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* recall: 0.9543561819346573 |
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