Instructions to use uer/chinese_roberta_L-4_H-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uer/chinese_roberta_L-4_H-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="uer/chinese_roberta_L-4_H-256")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("uer/chinese_roberta_L-4_H-256") model = AutoModelForMaskedLM.from_pretrained("uer/chinese_roberta_L-4_H-256", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from uer/chinese_roberta_L-4_H-256: direct link, hf CLI and curl.
- Browser
- Download file 35.2 MB
-
https://huggingface.co/uer/chinese_roberta_L-4_H-256/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://uer/chinese_roberta_L-4_H-256/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/uer/chinese_roberta_L-4_H-256/resolve/main/pytorch_model.bin
35.2 MB
- Xet hash:
- 6b852ee4d4541f9d0866331922b4af2d90264c4896c566f95f0ace2df1eb5f3a
- Size of remote file:
- 35.2 MB
- SHA256:
- c60f6fde023a817b263dab52a4d0767ae139be98971da4a1a070c695ad24612e
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