Sentence Similarity
sentence-transformers
ONNX
Safetensors
bert
feature-extraction
gte
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Mihaiii/gte-micro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Mihaiii/gte-micro with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Mihaiii/gte-micro") 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 Mihaiii/gte-micro: direct link, hf CLI and curl.
- Browser
- Download file 17.5 MB
-
https://huggingface.co/Mihaiii/gte-micro/resolve/main/onnx/model_quantized.onnx
- Command line
-
hf download hf://Mihaiii/gte-micro/onnx/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/Mihaiii/gte-micro/resolve/main/onnx/model_quantized.onnx
17.5 MB
- Xet hash:
- f4cc66452f7b703c65fd601e0d646f562ffc642fd4af356c93d7eb9594887729
- Size of remote file:
- 17.5 MB
- SHA256:
- 49765c3ca33b8a7dfe0830aedbd394069c7fea50df7e2a17af53d20a6379a312
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