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--- |
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language: tr |
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widget: |
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- text: "Almanya, koronavirüs aşısını geliştiren Dr. Özlem Türeci ve eşi Prof. Dr. Uğur Şahin'e liyakat nişanı verdi" |
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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/convbert-base-turkish-cased (ConvBERTurk) |
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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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The ConvBERT architecture is presented in the ["ConvBERT: Improving BERT with Span-based Dynamic Convolution"](https://arxiv.org/abs/2008.02496) paper. |
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# Fine-tuning parameters: |
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``` |
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task = "ner" |
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model_checkpoint = "dbmdz/convbert-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/convbert-base-turkish-cased-ner") |
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tokenizer = AutoTokenizer.from_pretrained("akdeniz27/convbert-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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# Pls refer "https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html" for entity grouping with aggregation_strategy parameter. |
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``` |
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# Reference test results: |
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* accuracy: 0.9937648915431506 |
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* f1: 0.9610945644080416 |
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* precision: 0.9619899385131359 |
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* recall: 0.9602008554956295 |
