Instructions to use hellonlp/promcse-bert-base-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hellonlp/promcse-bert-base-zh with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, BertForCL tokenizer = AutoTokenizer.from_pretrained("hellonlp/promcse-bert-base-zh") model = BertForCL.from_pretrained("hellonlp/promcse-bert-base-zh", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hellonlp/promcse-bert-base-zh: direct link, hf CLI and curl.
- Browser
- Download file 410 MB
-
https://huggingface.co/hellonlp/promcse-bert-base-zh/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hellonlp/promcse-bert-base-zh/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hellonlp/promcse-bert-base-zh/resolve/main/pytorch_model.bin
410 MB
- Xet hash:
- 5a65e453540d61d99c6165b4b87feee9f936462987a081f41efed498d6caed28
- Size of remote file:
- 410 MB
- SHA256:
- b3c5a0dccb54fb4f08a8c86e0897d26813b22eff8360608ba9610899439493e0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.