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/MMTEB_ENG_V2.png from geevec-ai/geevec-embeddings-1.0: direct link, hf CLI and curl.
- Browser
- Download file 182 kB
-
https://huggingface.co/geevec-ai/geevec-embeddings-1.0/resolve/main/imgs/MMTEB_ENG_V2.png
- Command line
-
hf download hf://geevec-ai/geevec-embeddings-1.0/imgs/MMTEB_ENG_V2.png
-
curl -L -o MMTEB_ENG_V2.png https://huggingface.co/geevec-ai/geevec-embeddings-1.0/resolve/main/imgs/MMTEB_ENG_V2.png
182 kB

- Xet hash:
- 3a461528e7d834f9238d7ae970e64ea2eba0cf005cabbd0c36a77926cd5acae1
- Size of remote file:
- 182 kB
- SHA256:
- db9b09bece8590a3169f3c2f60e771bb470b239a3edda255591c2b9aca72fc1a
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