Sentence Similarity
sentence-transformers
PyTorch
ONNX
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use vectoriseai/gte-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vectoriseai/gte-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vectoriseai/gte-base") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download onnx/model_quantized.onnx from vectoriseai/gte-base: direct link, hf CLI and curl.
- Browser
- Download file 110 MB
-
https://huggingface.co/vectoriseai/gte-base/resolve/main/onnx/model_quantized.onnx
- Command line
-
hf download hf://vectoriseai/gte-base/onnx/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/vectoriseai/gte-base/resolve/main/onnx/model_quantized.onnx
110 MB
- Xet hash:
- 1e7e91a1e7ad2e2d942d937a12d286bd6b5ded95f465d2f49ae85065ae16152e
- Size of remote file:
- 110 MB
- SHA256:
- 699c5233f2ed9e7230af2d0cb7a50d364fa40d0f72f8312cad74d86a38676637
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