Instructions to use jpohhhh/biencoder_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jpohhhh/biencoder_embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jpohhhh/biencoder_embedding")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jpohhhh/biencoder_embedding") model = AutoModel.from_pretrained("jpohhhh/biencoder_embedding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jpohhhh/biencoder_embedding: direct link, hf CLI and curl.
- Browser
- Download file 128 Bytes
-
https://huggingface.co/jpohhhh/biencoder_embedding/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jpohhhh/biencoder_embedding/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jpohhhh/biencoder_embedding/resolve/main/pytorch_model.bin
128 Bytes
- Xet hash:
- 87e6a1b9becc8f5b749a8fa9e7e81b6380f662595524e526d4b45b03b2521447
- Size of remote file:
- 128 Bytes
- SHA256:
- 4593facd634592b4a1ba74db6e75b0d3a6418343a0fec6e019a07d04785b08b5
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