Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3_pseudo_moe
sentence-similarity
custom_code
Instructions to use geevec-ai/geevec-embeddings-1.0-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use geevec-ai/geevec-embeddings-1.0-lite with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("geevec-ai/geevec-embeddings-1.0-lite", 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] - Transformers
How to use geevec-ai/geevec-embeddings-1.0-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="geevec-ai/geevec-embeddings-1.0-lite", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("geevec-ai/geevec-embeddings-1.0-lite", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1cb215025defc7b847095c89c424a1623e52396d5381c61fb9a0211da8f043b6
- Size of remote file:
- 11.4 MB
- SHA256:
- e6428e34b7a63a1fabc410111a202455917973a614eacac5e7bc9eeb8a87ceff
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