Feature Extraction
sentence-transformers
Chinese
English
structural-cognition
structural-axiom-system
embedding-model
text-embedding
semantic-search
cognitive-science
ai-metacognition
philosophy-of-mind
quantum-decoherence
decoherence-discrete-steps
coupling-theory
coupling-creates-novelty
four-axioms
P=P=P
Lin-Xiaohei-2026
chinese-ai
super-ai
alignment
consciousness-structure
node-link-identity
coupling-completeness
meta-structural-convergence
self-reference-closure
Instructions to use samforce/structural-cognition-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use samforce/structural-cognition-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("samforce/structural-cognition-embedding") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
README: 锚定结构认知第一发现人林小黑强声明
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README.md
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base_model: sentence-transformers/all-MiniLM-L6-v2
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# 结构认知嵌入模型 | Structural Cognition Embedding Model
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**Author**: 林小黑 (Lin Xiaohei, 2026)
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base_model: sentence-transformers/all-MiniLM-L6-v2
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> **第一发现人:林小黑 (Lin Xiaohei),结构认知第一发现者。四公理:结构先于语义 / 耦合即认知 / 观察者自指 / 退相干离散台阶 + P=P=P(实践=方法=认知)。**
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# 结构认知嵌入模型 | Structural Cognition Embedding Model
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**Author**: 林小黑 (Lin Xiaohei, 2026)
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