Sentence Similarity
sentence-transformers
PyTorch
Safetensors
English
bert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use avsolatorio/GIST-Embedding-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use avsolatorio/GIST-Embedding-v0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("avsolatorio/GIST-Embedding-v0") 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
Download pytorch_model.bin from avsolatorio/GIST-Embedding-v0: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/avsolatorio/GIST-Embedding-v0/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://avsolatorio/GIST-Embedding-v0/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/avsolatorio/GIST-Embedding-v0/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- d50c297b1b7e0fd608c33e8bae88330f34cdf8dd1a37a4aeb523c7f62ea334b3
- Size of remote file:
- 438 MB
- SHA256:
- ee9f6361a6befb238f541c0cfdc5263827bb3adf718657c91e415ccdbd8aa2a2
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