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-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BCCard/MoAI-Embedding-0.6B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BCCard/MoAI-Embedding-0.6B") 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-0.6B with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Download figures/evaluation-test-1-2.png from BCCard/MoAI-Embedding-0.6B: direct link, hf CLI and curl.
- Browser
- Download file 232 kB
-
https://huggingface.co/BCCard/MoAI-Embedding-0.6B/resolve/main/figures/evaluation-test-1-2.png
- Command line
-
hf download hf://BCCard/MoAI-Embedding-0.6B/figures/evaluation-test-1-2.png
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curl -L -o evaluation-test-1-2.png https://huggingface.co/BCCard/MoAI-Embedding-0.6B/resolve/main/figures/evaluation-test-1-2.png
232 kB

- Xet hash:
- 2beeb432cd44961357b6f87fec2ed89d999f43dc7eea74c52b58807151751094
- Size of remote file:
- 232 kB
- SHA256:
- 517dfd35e862a4a677bf6c8ddfd95d690cd82699402a3a740a75bffbeb5893bb
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