Sentence Similarity
sentence-transformers
PyTorch
ONNX
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use barisaydin/gte-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use barisaydin/gte-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("barisaydin/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 pytorch_model.bin from barisaydin/gte-base: direct link, hf CLI and curl.
- Browser
- Download file 219 MB
-
https://huggingface.co/barisaydin/gte-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://barisaydin/gte-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/barisaydin/gte-base/resolve/main/pytorch_model.bin
219 MB
- Xet hash:
- 1923b187e27798d9e00ce117ecf4a04af3b3573881dcf4839539ed1b1d4b1f7d
- Size of remote file:
- 219 MB
- SHA256:
- 0e9f33b61f645b1ea2c8bf328601259473dd7e9d837b67c1a1eabb28abe1c5be
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