Instructions to use fanjiang98/ABEL-Query-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fanjiang98/ABEL-Query-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="fanjiang98/ABEL-Query-Encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("fanjiang98/ABEL-Query-Encoder") model = AutoModel.from_pretrained("fanjiang98/ABEL-Query-Encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from fanjiang98/ABEL-Query-Encoder: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/fanjiang98/ABEL-Query-Encoder/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://fanjiang98/ABEL-Query-Encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/fanjiang98/ABEL-Query-Encoder/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 848a1e22365ae282a13c16c53bb6fb0f29f992865df9a1851ad5327f6808038b
- Size of remote file:
- 438 MB
- SHA256:
- 3ea847d9c0be57fe608473a3a085281ec190673b32bf4ff39d87798119287e11
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