Instructions to use geevec-ai/geevec-embeddings-1.0 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 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("geevec-ai/geevec-embeddings-1.0") 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 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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("geevec-ai/geevec-embeddings-1.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download imgs/BEIR.png from geevec-ai/geevec-embeddings-1.0: direct link, hf CLI and curl.
- Browser
- Download file 245 kB
-
https://huggingface.co/geevec-ai/geevec-embeddings-1.0/resolve/main/imgs/BEIR.png
- Command line
-
hf download hf://geevec-ai/geevec-embeddings-1.0/imgs/BEIR.png
-
curl -L -o BEIR.png https://huggingface.co/geevec-ai/geevec-embeddings-1.0/resolve/main/imgs/BEIR.png
245 kB

- Xet hash:
- 174e9a21a440aba74f7d75deb7a25a2b1072e1a86604869dc96067974536d6ea
- Size of remote file:
- 245 kB
- SHA256:
- 045131087f7a82c3980b43a2decfd48aa493dcdcc235c023ed1e4c06864cea30
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