Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
text-embeddings-inference
Instructions to use Techatcore/zeiss-re-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Techatcore/zeiss-re-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Techatcore/zeiss-re-v2") 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 Techatcore/zeiss-re-v2: direct link, hf CLI and curl.
- Browser
- Download file 717 kB
-
https://huggingface.co/Techatcore/zeiss-re-v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://Techatcore/zeiss-re-v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Techatcore/zeiss-re-v2/resolve/main/tokenizer.json
717 kB
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