Feature Extraction
sentence-transformers
Safetensors
Korean
xlm-roberta
embeddings
bge-m3
korean
temporal-retrieval
lms
text-embeddings-inference
Instructions to use kev-KOH/time-embed-bge-m3-lms-temporal-v1-6-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kev-KOH/time-embed-bge-m3-lms-temporal-v1-6-1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kev-KOH/time-embed-bge-m3-lms-temporal-v1-6-1") 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
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