Sentence Similarity
sentence-transformers
Safetensors
bert
embedding
context-curation
long-context
memory-management
retrieval
text-embeddings-inference
Instructions to use SkyIsNotGreen/splinter-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SkyIsNotGreen/splinter-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SkyIsNotGreen/splinter-encoder") 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 model.safetensors from SkyIsNotGreen/splinter-encoder: direct link, hf CLI and curl.
- Browser
- Download file 69.6 MB
-
https://huggingface.co/SkyIsNotGreen/splinter-encoder/resolve/main/model.safetensors
- Command line
-
hf download hf://SkyIsNotGreen/splinter-encoder/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/SkyIsNotGreen/splinter-encoder/resolve/main/model.safetensors
69.6 MB
- Xet hash:
- 0490b45acc75dff563ecb8fb0bedbbd88ac4680265220c3a26afd162c2ce3cb2
- Size of remote file:
- 69.6 MB
- SHA256:
- ed2dcbfb8a11ffc93708909afec66094bf03771ec60772ec508063d77fada477
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