Feature Extraction
sentence-transformers
Safetensors
Korean
English
qwen3
embedding
retrieval
cybersecurity
mitre-attack
korean
text-embeddings-inference
Instructions to use 78ResearchLab/PurpleHound-Embed-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use 78ResearchLab/PurpleHound-Embed-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("78ResearchLab/PurpleHound-Embed-v1") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from 78ResearchLab/PurpleHound-Embed-v1: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/78ResearchLab/PurpleHound-Embed-v1/resolve/main/tokenizer.json
- Command line
-
hf download hf://78ResearchLab/PurpleHound-Embed-v1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/78ResearchLab/PurpleHound-Embed-v1/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- fd0498b050f369b0b10254fe00e8636bb265b71b8690310591a18ac3676d9372
- Size of remote file:
- 11.4 MB
- SHA256:
- 7bbd7da4557f4f46591cf4eec87298afe7a5015f11a8449304b84821f2475d0c
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