Dontopedia

Detached Tensor

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

Detached Tensor has 3 facts recorded in Dontopedia across 2 references.

3 facts·3 predicates·2 sources
Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (2)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

appliedToApplied to(1)

producesProduces(1)

Other facts (3)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

3 facts
PredicateValueRef
Converted to NumpyNumpy Array[1]
UndergoesNumpy Conversion[1]
Converted toNumpy Array[2]

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

3 undated
—
convertedToNumpybeam/0d778d3d-86d2-4e66-b864-c688d77dde22
ex:numpy-array
—
undergoesbeam/0d778d3d-86d2-4e66-b864-c688d77dde22
ex:numpy-conversion
—
converted-tobeam/5d8a681b-1fe3-4aff-8534-8603ba9d9bfc
ex:numpy-array

References (2)

2 references
  1. [1]0d778d3d 86d2 4e66 B864 C688d77dde222 facts
    ctx:claims/beam/0d778d3d-86d2-4e66-b864-c688d77dde22
    • full textbeam-chunk
      text/plain1 KBdoc:beam/0d778d3d-86d2-4e66-b864-c688d77dde22
      Show excerpt
      def add_token(self, token): self.tokens.append(token) self.token_count += 1 def get_context(self): if self.token_count in self.cache: return self.cache[self.token_count] context = list(s…
  2. [2]5d8a681b 1fe3 4aff 8534 8603ba9d9bfc1 fact
    ctx:claims/beam/5d8a681b-1fe3-4aff-8534-8603ba9d9bfc
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5d8a681b-1fe3-4aff-8534-8603ba9d9bfc
      Show excerpt
      inputs = tokenizer(term, return_tensors='pt', padding=True, truncation=True) outputs = model(**inputs) embeddings = outputs.last_hidden_state.mean(dim=1) # Mean pooling return embeddings ``` ### Step 4: Retrieve Synonyms B…

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