Instructions to use binwang/bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binwang/bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="binwang/bert-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("binwang/bert-base-uncased") model = AutoModelForMaskedLM.from_pretrained("binwang/bert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from binwang/bert-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/binwang/bert-base-uncased/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://binwang/bert-base-uncased@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/binwang/bert-base-uncased/resolve/refs%2Fpr%2F2/pytorch_model.bin
440 MB
- Xet hash:
- 46b186a974d5802f5ce41e233eda8a521bd6837e40852356d5e7a468081152b0
- Size of remote file:
- 440 MB
- SHA256:
- bc0c52a61d46c10f6d7794dfbe61e93a2c2d763529abd8c72897acf00ab8348b
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