Sentence Similarity
sentence-transformers
Safetensors
English
Lushai
bert
feature-extraction
labse
mizo
cross-lingual
bitext-mining
information-retrieval
clir
clri
text-embeddings-inference
Instructions to use hillbyte/labse-mizo-en-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hillbyte/labse-mizo-en-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hillbyte/labse-mizo-en-embed") 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 tokenizer.json from hillbyte/labse-mizo-en-embed: direct link, hf CLI and curl.
- Browser
- Download file 13.6 MB
-
https://huggingface.co/hillbyte/labse-mizo-en-embed/resolve/main/tokenizer.json
- Command line
-
hf download hf://hillbyte/labse-mizo-en-embed/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/hillbyte/labse-mizo-en-embed/resolve/main/tokenizer.json
13.6 MB
- Xet hash:
- ff7b6e9479ff15e415b9f359e4cbb40f55a631245c896ba3318a8bc9f4cefe17
- Size of remote file:
- 13.6 MB
- SHA256:
- edba4e57ec22a2a74bbdb601d3f908e4699c34f8386d52ed055e6fe6bd2b51ac
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