Instructions to use binwang/bert-large-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binwang/bert-large-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="binwang/bert-large-nli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("binwang/bert-large-nli") model = AutoModelForMaskedLM.from_pretrained("binwang/bert-large-nli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from binwang/bert-large-nli: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/binwang/bert-large-nli/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://binwang/bert-large-nli/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/binwang/bert-large-nli/resolve/main/flax_model.msgpack
1.34 GB
- Xet hash:
- e3e770f906d93d51acc3e817384bf7a53627e80d0e30fb817c9fd28390c50036
- Size of remote file:
- 1.34 GB
- SHA256:
- 64ceb889783de340c7d02ded0f66837f3cfd05ced43b283a76fa9bf1245f1034
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