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 tokenizer.json from LinkinShan/hanja-wsd-base: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/LinkinShan/hanja-wsd-base/resolve/main/tokenizer.json
- Command line
-
hf download hf://LinkinShan/hanja-wsd-base/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/LinkinShan/hanja-wsd-base/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- a6819ea8d759539857505e59e1310cf1d59510788eae78f16e8d6a786b77ca64
- Size of remote file:
- 17.1 MB
- SHA256:
- 238331c275374fbb4f75072cb1458c001cc2c211c2250aa445c2439fc3a8c6ba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.