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
File size: 817 Bytes
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message: "If you use this software or its measurements, cite it as below."
title: "HiveBench"
abstract: >-
Evaluation suite and Studio sidecar for Splinter-Memory. Ships the white
paper's falsifiable predictions (P1-P11) as executable, offline tests with
measured verdicts, deterministic retrieval diagnostics that avoid
LLM-as-judge circularity in the evidence path, and replayable paired A/B
batteries (splinter-curated context vs a naive FIFO window).
type: software
authors:
- name: "sky-is-green"
alias: "sky-is-green"
version: "0.1.0"
date-released: "2026-09-22"
license: MIT
repository-code: "https://github.com/sky-is-green/hivebench"
url: "https://github.com/sky-is-green/hivebench"
keywords:
- evaluation
- benchmark
- context-management
- llm
- reproducibility
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