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
File size: 349 Bytes
77330c4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"is_local": false,
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}
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