Sentence Similarity
sentence-transformers
ONNX
Safetensors
modernbert
feature-extraction
embeddings
matryoshka
multilingual
text-embeddings-inference
hindsight
Instructions to use hindsight-ai/hs-embed-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hindsight-ai/hs-embed-1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hindsight-ai/hs-embed-1") 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 tokenizer.json from hindsight-ai/hs-embed-1: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/hindsight-ai/hs-embed-1/resolve/main/tokenizer.json
- Command line
-
hf download hf://hindsight-ai/hs-embed-1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/hindsight-ai/hs-embed-1/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- 4979ea4051b704efcb2a1a34961c5f93d2fa004f33c607d894c7ebe32511f588
- Size of remote file:
- 34.4 MB
- SHA256:
- f34b44a6dc8a0eef651bfa10e8325057530152606a69d9c3b6ae02efb1558f0a
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