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/COIR.png from geevec-ai/geevec-embeddings-1.0: direct link, hf CLI and curl.
- Browser
- Download file 165 kB
-
https://huggingface.co/geevec-ai/geevec-embeddings-1.0/resolve/main/imgs/COIR.png
- Command line
-
hf download hf://geevec-ai/geevec-embeddings-1.0/imgs/COIR.png
-
curl -L -o COIR.png https://huggingface.co/geevec-ai/geevec-embeddings-1.0/resolve/main/imgs/COIR.png
165 kB

- Xet hash:
- fce8e31cd30d18b76c14b4acfe30263b949e20f1a29c9a56787ab7fb75ef7ef3
- Size of remote file:
- 165 kB
- SHA256:
- 8b3c24dda43e401808919d63ca14d4bf6eb0ac739d5fa87815331fe1a91c39c7
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