Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Core ML
ONNX
Safetensors
OpenVINO
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use thenlper/gte-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use thenlper/gte-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("thenlper/gte-small") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
CoreML model seems wrong (dimension is 128 not 512)
#9
by jens-alfke - opened
Hi, can you give the link to the model above?
It's the one checked into the repo, coreml/feature-extraction/float32_model.mlpackage.
It appears to be the correct model -- once I got it working in an app, it produces identical outputs to GTE-Small running in PyTorch. But I can't give it more than 128 input tokens or CoreML returns an error.
