Query Embeddings
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
Query Embeddings has 4 facts recorded in Dontopedia across 3 references, with 1 live disagreement.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (6)
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.
comparesCompares(2)
- Cosine Similarity Inputs
ex:cosine-similarity-inputs - Cosine Similarity Loss
ex:cosine-similarity-loss
accessedByAccessed by(1)
- Last Hidden State
ex:last-hidden-state
computedFromComputed From(1)
- Similarity Scores
ex:similarity-scores
Other facts (4)
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.
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.
References (3)
ctx:claims/beam/b2fa8237-a2ba-45f1-b609-1096fd02ce18- full textbeam-chunktext/plain1 KB
doc:beam/b2fa8237-a2ba-45f1-b609-1096fd02ce18Show excerpt
vectorizer = TfidfVectorizer() tfidf_matrix = vectorizer.fit_transform(documents) query_vector = vectorizer.transform([query]) similarity_scores = (query_vector * tfidf_matrix.T).toarray() return similarity_scores def h…
ctx:claims/beam/af659f61-d237-4091-a8b5-4a63d8ff2fae- full textbeam-chunktext/plain1 KB
doc:beam/af659f61-d237-4091-a8b5-4a63d8ff2faeShow excerpt
query_embeddings = model(**query_encodings)['last_hidden_state'][:, 0, :] passage_embeddings = model(**passage_encodings)['last_hidden_state'][:, 0, :] # Apply dropout query_embeddings = dropout(query_embedd…
ctx:claims/beam/7791191d-1137-4a89-a9b4-1a376dfcb591- full textbeam-chunktext/plain1 KB
doc:beam/7791191d-1137-4a89-a9b4-1a376dfcb591Show excerpt
# Zero gradients optimizer.zero_grad() print(f"Epoch {epoch+1}/{5}, Loss: {loss.item():.4f}") # Save the model torch.save(model.state_dict(), 'rag_model.pth') ``` ### Explanation 1. **Compute Query Complexity**: -…
See also
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