Instructions to use zachzhang5/cross-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zachzhang5/cross-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zachzhang5/cross-encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zachzhang5/cross-encoder") model = AutoModelForSequenceClassification.from_pretrained("zachzhang5/cross-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from zachzhang5/cross-encoder: direct link, hf CLI and curl.
- Browser
- Download file 90.9 MB
-
https://huggingface.co/zachzhang5/cross-encoder/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://zachzhang5/cross-encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/zachzhang5/cross-encoder/resolve/main/pytorch_model.bin
90.9 MB
- Xet hash:
- 6259af915ec6d8dfd37534bad9f51c94330412638235899ee35cf9b166fdc8aa
- Size of remote file:
- 90.9 MB
- SHA256:
- 82d1b6db54200201d8b9ca82b4583139a6cd604f9ce79e2943d87af5e2a6a2ce
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