Sentence Similarity
sentence-transformers
Safetensors
German
ministral3
feature-extraction
retrieval
public-procurement
sustainability
Instructions to use IntelliProcure/TENDRIL-most-embed-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use IntelliProcure/TENDRIL-most-embed-de with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("IntelliProcure/TENDRIL-most-embed-de") sentences = [ "Das ist eine glückliche Person", "Das ist ein glücklicher Hund", "Das ist eine sehr glückliche Person", "Heute ist ein sonniger Tag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from IntelliProcure/TENDRIL-most-embed-de: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/IntelliProcure/TENDRIL-most-embed-de/resolve/main/tokenizer.json
- Command line
-
hf download hf://IntelliProcure/TENDRIL-most-embed-de/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/IntelliProcure/TENDRIL-most-embed-de/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 40eed62c9f36f0e5ed23205280afce55d25203959891b35bae12ee85cfb6c850
- Size of remote file:
- 17.1 MB
- SHA256:
- 7292c68f922b5f3ac710b801024c1e9115ec566d88b92b2dd676ed3bd21d9c30
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