Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use micoff/rubert-tiny2-tuned-for-prosept with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use micoff/rubert-tiny2-tuned-for-prosept with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("micoff/rubert-tiny2-tuned-for-prosept") 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 micoff/rubert-tiny2-tuned-for-prosept: direct link, hf CLI and curl.
- Browser
- Download file 2.41 MB
-
https://huggingface.co/micoff/rubert-tiny2-tuned-for-prosept/resolve/main/tokenizer.json
- Command line
-
hf download hf://micoff/rubert-tiny2-tuned-for-prosept/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/micoff/rubert-tiny2-tuned-for-prosept/resolve/main/tokenizer.json
2.41 MB
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