Token Classification
Transformers
PyTorch
JAX
Chinese
roberta
chinese
classical chinese
literary chinese
ancient chinese
bert
Instructions to use ethanyt/guwen-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ethanyt/guwen-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ethanyt/guwen-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ethanyt/guwen-ner") model = AutoModelForTokenClassification.from_pretrained("ethanyt/guwen-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: "apache-2.0"
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pipeline_tag: "token-classification"
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# Guwen NER
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license: "apache-2.0"
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pipeline_tag: "token-classification"
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widget:
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- text: "及秦始皇,灭先代典籍,焚书坑儒,天下学士逃难解散,我先人用藏其家书于屋壁。汉室龙兴,开设学校,旁求儒雅,以阐大猷。济南伏生,年过九十,失其本经,口以传授,裁二十馀篇,以其上古之书,谓之尚书。百篇之义,世莫得闻。"
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# Guwen NER
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