Instructions to use hs-hf/m2v-LaBSE-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use hs-hf/m2v-LaBSE-distilled with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("hs-hf/m2v-LaBSE-distilled") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from hs-hf/m2v-LaBSE-distilled: direct link, hf CLI and curl.
- Browser
- Download file 13.6 MB
-
https://huggingface.co/hs-hf/m2v-LaBSE-distilled/resolve/main/tokenizer.json
- Command line
-
hf download hf://hs-hf/m2v-LaBSE-distilled/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/hs-hf/m2v-LaBSE-distilled/resolve/main/tokenizer.json
13.6 MB
- Xet hash:
- 1ae37f8241801be24085d070e93768eafa7b8659c6b2cd1e8d3d8c6bb9c625ca
- Size of remote file:
- 13.6 MB
- SHA256:
- 147aa80a5285705d23d2574b6578f5e03edb3825607e6a5ec65504b733bbec81
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