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--- |
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language: hu |
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license: mit |
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widget: |
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- text: "Karikó Katalin megkapja Szeged díszpolgárságát." |
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--- |
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# Hungarian Named Entity Recognition (NER) Model |
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This model is the fine-tuned model of "SZTAKI-HLT/hubert-base-cc" |
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using the famous WikiANN dataset presented |
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in the "Cross-lingual Name Tagging and Linking for 282 Languages" [paper](https://aclanthology.org/P17-1178.pdf). |
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# Fine-tuning parameters: |
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``` |
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task = "ner" |
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model_checkpoint = "SZTAKI-HLT/hubert-base-cc" |
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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-hungarian-cased-ner") |
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tokenizer = AutoTokenizer.from_pretrained("akdeniz27/bert-base-hungarian-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: 0.9774538310923768 |
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* f1: 0.9462099085573904 |
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* precision: 0.9425718667406271 |
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* recall: 0.9498761426661113 |
