Feature Extraction
Transformers
Safetensors
sentence-transformers
Chinese
English
mteb
custom_code
Eval Results (legacy)
Instructions to use openbmb/MiniCPM-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-Embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="openbmb/MiniCPM-Embedding", trust_remote_code=True)# Load model directly from transformers import MiniCPM model = MiniCPM.from_pretrained("openbmb/MiniCPM-Embedding", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use openbmb/MiniCPM-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("openbmb/MiniCPM-Embedding", 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
File size: 315 Bytes
143fca0 13ce72b 143fca0 | 1 2 3 4 5 6 7 8 9 10 | {
"word_embedding_dimension": 2304,
"pooling_mode_cls_token": false,
"pooling_mode_mean_tokens": true,
"pooling_mode_max_tokens": false,
"pooling_mode_mean_sqrt_len_tokens": false,
"pooling_mode_weightedmean_tokens": false,
"pooling_mode_lasttoken": false,
"include_prompt": true
} |