Sentence Similarity
sentence-transformers
Safetensors
PEFT
Korean
qwen3
feature-extraction
text-embedding
information-retrieval
korean
finance
lora
text-embeddings-inference
Instructions to use BCCard/MoAI-Embedding-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BCCard/MoAI-Embedding-4B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BCCard/MoAI-Embedding-4B") 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] - PEFT
How to use BCCard/MoAI-Embedding-4B with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b8c78239eacdb84157873260eff7fbcc035af6eaa472c045d96fcee0bd60488f
- Size of remote file:
- 11.4 MB
- SHA256:
- fc6c7e9913a0b4e773a2e04584fbf47b2e00e875d1f5689bd11a166bae1c376b
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