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")# pip install -U transformers accelerate # 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
Download flax_model.msgpack from ethanyt/guwen-ner: direct link, hf CLI and curl.
- Browser
- Download file 413 MB
-
https://huggingface.co/ethanyt/guwen-ner/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://ethanyt/guwen-ner/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/ethanyt/guwen-ner/resolve/main/flax_model.msgpack
413 MB
- Xet hash:
- bb3c2b2316e354c9d9bb324eb9ff4e76aca5e2daee06dd3e3727a694177f32e1
- Size of remote file:
- 413 MB
- SHA256:
- da82acdf9e00663a56552dc3e7045d8a8d0fbfb612739309003f14f44b80c415
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