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 1_Pooling/config.json from IntelliProcure/TENDRIL-most-embed-de: direct link, hf CLI and curl.
- Browser
- Download file 91 Bytes
-
https://huggingface.co/IntelliProcure/TENDRIL-most-embed-de/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://IntelliProcure/TENDRIL-most-embed-de/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/IntelliProcure/TENDRIL-most-embed-de/resolve/main/1_Pooling/config.json
91 Bytes
| { | |
| "embedding_dimension": 2048, | |
| "pooling_mode": "mean", | |
| "include_prompt": true | |
| } |