Instructions to use Stancld/roformer_chinese_char_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Stancld/roformer_chinese_char_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Stancld/roformer_chinese_char_base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Stancld/roformer_chinese_char_base") model = AutoModelForMaskedLM.from_pretrained("Stancld/roformer_chinese_char_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Stancld/roformer_chinese_char_base: direct link, hf CLI and curl.
- Browser
- Download file 380 MB
-
https://huggingface.co/Stancld/roformer_chinese_char_base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Stancld/roformer_chinese_char_base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Stancld/roformer_chinese_char_base/resolve/main/flax_model.msgpack
380 MB
- Xet hash:
- 59841734362d6a5e1824c07363f74592a48e52c4bd7ec1b4338b03f0ea57e782
- Size of remote file:
- 380 MB
- SHA256:
- ed00db5525662c1467ffd52c0c14a4511b2594ba49953d0a1d2385b3afd48aa9
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