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
Update scripts/flagembedding_demo.py
Browse files
scripts/flagembedding_demo.py
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from FlagEmbedding import FlagModel
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model = FlagModel("openbmb/
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query_instruction_for_retrieval="Query: ",
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pooling_method="mean",
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trust_remote_code=True,
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from FlagEmbedding import FlagModel
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model = FlagModel("openbmb/MiniCPM-Embedding-Light",
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query_instruction_for_retrieval="Query: ",
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pooling_method="mean",
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trust_remote_code=True,
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