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--- |
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library_name: transformers |
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license: mit |
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language: |
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- fa |
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pipeline_tag: token-classification |
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--- |
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Named entity recognition On Persian dataset |
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traindataset=20484 persian sentense |
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valdataset=2561 |
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AutoTokenizer=HooshvareLab/bert-fa-base-uncased |
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ner_tags= |
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['O', 'B-pro', |
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'I-pro', |
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'B-pers', |
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'I-pers', |
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'B-org', |
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'I-org', |
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'B-loc', |
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'I-loc', |
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'B-fac', |
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'I-fac', |
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'B-event', |
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'I-event'] |
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training_args= |
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learning_rate=2e-5, |
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per_device_train_batch_size=16, |
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per_device_eval_batch_size=16, |
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num_train_epochs=4, |
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weight_decay=0.01 |
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Training Loss=0.001000 |
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sample1: |
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'entity': 'B-loc', |
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'score': 0.9998902, |
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'index': 2, |
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'word': 'تهران', |
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sample2: |
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'entity': 'B-pers', |
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'score': 0.99988234, |
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'index': 2, |
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'word': 'عباس', |
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for use this model: |
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from transformers import pipeline |
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pipe = pipeline("token-classification", model="NLPclass/Named_entity_recognition_persian") |
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sentence = "" |
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predicted_ner = pipe(sentence) |
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for entity in predicted_ner: |
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print(f"Entity: {entity['word']}, Label: {entity['entity']}") |
