Sentence Similarity
sentence-transformers
Safetensors
Transformers
new
feature-extraction
mteb
multilingual
text-embeddings-inference
custom_code
Eval Results (legacy)
Instructions to use yankexe/gte-multilingual-base-air-gapped with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yankexe/gte-multilingual-base-air-gapped with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yankexe/gte-multilingual-base-air-gapped", 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 yankexe/gte-multilingual-base-air-gapped with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yankexe/gte-multilingual-base-air-gapped", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 978575da165aa18e556a53cca82c94fd3d308a2fca20ed3b72bde701709d4d05
- Size of remote file:
- 611 MB
- SHA256:
- f5a35a10faa54da7717870af1517c9b41e9bd8e3880bc5a8e9363d4c3c63e9b0
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