Feature Extraction
Transformers
Safetensors
sentence-transformers
minicpm
mteb
custom_code
Eval Results (legacy)
Instructions to use openbmb/MiniCPM-Embedding-Light with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-Embedding-Light with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="openbmb/MiniCPM-Embedding-Light", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/MiniCPM-Embedding-Light", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use openbmb/MiniCPM-Embedding-Light with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("openbmb/MiniCPM-Embedding-Light", trust_remote_code=True) 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
Commit History
Merge branch 'main' of hf.co:openbmb/MiniCPM-Embedding-Light 8cabb11
a 0413b4c
Update config.json ed7db42 verified
citation 91b71e3
Update scripts/transformers_demo.py 321b9bb verified
Update scripts/test_mteb.py 1d0117a verified
Update scripts/sentence_transformers_demo.py 5643d0d verified
Update scripts/infinity_demo.py aafaf12 verified
Update scripts/flagembedding_demo.py 455038a verified
readme 95ba296
typo 3ab2b19
init 75f07f8
1 commited on