Instructions to use odunola/Hack_embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use odunola/Hack_embedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("odunola/Hack_embedder") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from odunola/Hack_embedder: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/odunola/Hack_embedder/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://odunola/Hack_embedder@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/odunola/Hack_embedder/resolve/refs%2Fpr%2F1/pytorch_model.bin
134 MB
- Xet hash:
- a759f1b05a665b9a49a267d6e1a3efa76e299586be3c9deff946f17828475184
- Size of remote file:
- 134 MB
- SHA256:
- 5dc4f20b2adff8018249e414071a15f3c141a4218b7c0e9abaa4ca4914f7701b
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