Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
text-embeddings-inference
Instructions to use mykor/mmBERT-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mykor/mmBERT-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mykor/mmBERT-small") 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.model from mykor/mmBERT-small: direct link, hf CLI and curl.
- Browser
- Download file 4.24 MB
-
https://huggingface.co/mykor/mmBERT-small/resolve/main/tokenizer.model
- Command line
-
hf download hf://mykor/mmBERT-small/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/mykor/mmBERT-small/resolve/main/tokenizer.model
4.24 MB
- Xet hash:
- 4747d621061ff864406560d79c8c3e355c05f2e88ba30eba88c8d5881d3d220d
- Size of remote file:
- 4.24 MB
- SHA256:
- 61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
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