Text Classification
Transformers
Safetensors
Korean
xlm-roberta
feature-extraction
korean
hanja
hangul
mixed-script
word-sense-disambiguation
cross-encoder
preview
text-embeddings-inference
Instructions to use LinkinShan/hanja-wsd-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LinkinShan/hanja-wsd-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LinkinShan/hanja-wsd-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LinkinShan/hanja-wsd-base") model = AutoModel.from_pretrained("LinkinShan/hanja-wsd-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download src/simplified_drop.json from LinkinShan/hanja-wsd-base: direct link, hf CLI and curl.
- Browser
- Download file 509 Bytes
-
https://huggingface.co/LinkinShan/hanja-wsd-base/resolve/main/src/simplified_drop.json
- Command line
-
hf download hf://LinkinShan/hanja-wsd-base/src/simplified_drop.json
-
curl -L -o simplified_drop.json https://huggingface.co/LinkinShan/hanja-wsd-base/resolve/main/src/simplified_drop.json
509 Bytes
| ["䴘", "么", "产", "众", "冲", "凫", "别", "卤", "历", "厕", "发", "启", "团", "坛", "坝", "墙", "壳", "复", "妫", "并", "恶", "悫", "摆", "汇", "沩", "瘘", "硷", "竖", "签", "纤", "线", "绣", "绦", "绱", "绷", "缊", "缰", "脏", "苏", "荡", "药", "获", "莼", "蕴", "谫", "赃", "赍", "赝", "辒", "酝", "钟", "钩", "钵", "锈", "镋", "镌", "闲", "须", "颓", "颜", "饥", "骂", "鲞", "鳁", "鳄", "鸡", "鿭", "𩙧", "𫇭", "", "", "𰷭"] |