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 1_Pooling/config.json from 78ResearchLab/PurpleHound-Embed-v1: direct link, hf CLI and curl.
- Browser
- Download file 313 Bytes
-
https://huggingface.co/78ResearchLab/PurpleHound-Embed-v1/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://78ResearchLab/PurpleHound-Embed-v1/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/78ResearchLab/PurpleHound-Embed-v1/resolve/main/1_Pooling/config.json
313 Bytes
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": true, | |
| "include_prompt": true | |
| } |