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README.md
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NER-NewsBI-150142-e3b4 can recognize named entities in input sentences and predicts one label from a set of 150 labels for each named entity, thereby performing labeling for the input sentences.
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In particular, it is specialized for articles because it was trained using a news dataset.
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base model: https://huggingface.co/xlm-roberta-large-finetuned-conll03-english
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tokenizer: "xlm-roberta-large-finetuned-conll03-english"
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dataset: https://huggingface.co/datasets/yeajinmin/NER-News-BIDataset
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Because the Base Model is a multilingual model, even though it was trained only for Korean, it can recognize entity names with 150 labels for other languages.
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Available languages can be checked in the language of the base model above.
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NER-NewsBI-150142-e3b4 can recognize named entities in input sentences and predicts one label from a set of 150 labels for each named entity, thereby performing labeling for the input sentences.
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| 12 |
In particular, it is specialized for articles because it was trained using a news dataset.
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- base model: https://huggingface.co/xlm-roberta-large-finetuned-conll03-english
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- tokenizer: "xlm-roberta-large-finetuned-conll03-english"
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- dataset: https://huggingface.co/datasets/yeajinmin/NER-News-BIDataset
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Because the Base Model is a multilingual model, even though it was trained only for Korean, it can recognize entity names with 150 labels for other languages.
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Available languages can be checked in the language of the base model above.
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