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--- |
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language: |
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- "ko" |
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tags: |
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- "korean" |
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- "token-classification" |
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- "pos" |
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- "dependency-parsing" |
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datasets: |
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- "universal_dependencies" |
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license: "cc-by-sa-4.0" |
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pipeline_tag: "token-classification" |
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widget: |
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- text: "홍시 맛이 나서 홍시라 생각한다." |
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- text: "紅柹 맛이 나서 紅柹라 生覺한다." |
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--- |
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# roberta-large-korean-upos |
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## Model Description |
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This is a RoBERTa model pre-trained on Korean texts for POS-tagging and dependency-parsing, derived from [roberta-large-korean-hanja](https://huggingface.co/KoichiYasuoka/roberta-large-korean-hanja). Every word (어절) is tagged by [UPOS](https://universaldependencies.org/u/pos/)(Universal Part-Of-Speech). |
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## How to Use |
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```py |
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from transformers import AutoTokenizer,AutoModelForTokenClassification,TokenClassificationPipeline |
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tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-large-korean-upos") |
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model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-large-korean-upos") |
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pipeline=TokenClassificationPipeline(tokenizer=tokenizer,model=model,aggregation_strategy="simple") |
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nlp=lambda x:[(x[t["start"]:t["end"]],t["entity_group"]) for t in pipeline(x)] |
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print(nlp("홍시 맛이 나서 홍시라 생각한다.")) |
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``` |
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or |
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```py |
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import esupar |
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nlp=esupar.load("KoichiYasuoka/roberta-large-korean-upos") |
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print(nlp("홍시 맛이 나서 홍시라 생각한다.")) |
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``` |
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## See Also |
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[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models |
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