Instructions to use binwang/bert-base-nli-stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binwang/bert-base-nli-stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="binwang/bert-base-nli-stsb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("binwang/bert-base-nli-stsb") model = AutoModelForMaskedLM.from_pretrained("binwang/bert-base-nli-stsb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from binwang/bert-base-nli-stsb: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/binwang/bert-base-nli-stsb/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://binwang/bert-base-nli-stsb/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/binwang/bert-base-nli-stsb/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- c9a044e5997cfa5e94f1b74ce15b37de721f404ba4c5e4279c89107fb9edc1ad
- Size of remote file:
- 438 MB
- SHA256:
- 11948f21e519851454beee7ec8a5c258071c2986eff47630dc575e291d042bed
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