Similarity Computation
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Similarity Computation has 21 facts recorded in Dontopedia across 6 references, with 5 live disagreements.
21 facts·11 predicates·6 sources·5 in dispute
Mostly:rdf:type(5), uses function(2), function args(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (7)
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.
hasStepHas Step(2)
- Sequence of Operations
ex:sequence-of-operations - Training Sequence
ex:training-sequence
commentsComments(1)
- Comment Similarity Scores
ex:comment-similarity-scores
containsStepContains Step(1)
- Training Loop
ex:training-loop
is-used-forIs Used for(1)
- Function Output
ex:function-output
sequenceSequence(1)
- Training Sequence
ex:training-sequence
supportsSupports(1)
- Tf Idf Matrix
ex:tf-idf-matrix
Other facts (19)
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.
19 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Computation | [1] |
| Rdf:type | Operation | [2] |
| Rdf:type | Torch Operation | [4] |
| Rdf:type | List Comprehension | [5] |
| Rdf:type | Operation | [6] |
| Uses Function | torch.cosine_similarity | [2] |
| Uses Function | torch.cosine_similarity | [4] |
| Function Args | Query Encodings | [2] |
| Function Args | Passage Encodings | [2] |
| Has Input | queries | [4] |
| Has Input | passages | [4] |
| Computes Between | Query Tensor | [4] |
| Computes Between | Passage Tensor | [4] |
| Code | similarity_scores = torch.cosine_similarity(query_encodings, passage_encodings) | [2] |
| Produces | Similarity Scores | [2] |
| Result Is | Similarity Scores Entity | [2] |
| Causes | Loss Computation | [3] |
| Iterates Over | Synonym List | [5] |
| Computes | dot-product | [5] |
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.
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typebeam/9016225f-e83c-48c0-90be-7022b351ca10
ex:Computation
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labelbeam/9016225f-e83c-48c0-90be-7022b351ca10
Similarity Computation
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typebeam/66120f60-83ce-466d-9a19-6cadefd30586
ex:Operation
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codebeam/66120f60-83ce-466d-9a19-6cadefd30586
similarity_scores = torch.cosine_similarity(query_encodings, passage_encodings)
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usesFunctionbeam/66120f60-83ce-466d-9a19-6cadefd30586
torch.cosine_similarity
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functionArgsbeam/66120f60-83ce-466d-9a19-6cadefd30586
ex:query_encodings
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functionArgsbeam/66120f60-83ce-466d-9a19-6cadefd30586
ex:passage_encodings
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producesbeam/66120f60-83ce-466d-9a19-6cadefd30586
ex:similarity_scores
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resultIsbeam/66120f60-83ce-466d-9a19-6cadefd30586
ex:similarity-scores-entity
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causesbeam/af659f61-d237-4091-a8b5-4a63d8ff2fae
ex:loss-computation
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typebeam/fa1ef1c1-24c6-4f98-8255-600e4bf6a46c
ex:torch-operation
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usesFunctionbeam/fa1ef1c1-24c6-4f98-8255-600e4bf6a46c
torch.cosine_similarity
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hasInputbeam/fa1ef1c1-24c6-4f98-8255-600e4bf6a46c
queries
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hasInputbeam/fa1ef1c1-24c6-4f98-8255-600e4bf6a46c
passages
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computesBetweenbeam/fa1ef1c1-24c6-4f98-8255-600e4bf6a46c
ex:query-tensor
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computesBetweenbeam/fa1ef1c1-24c6-4f98-8255-600e4bf6a46c
ex:passage-tensor
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typebeam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
ex:ListComprehension
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iteratesOverbeam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
ex:synonym-list
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computesbeam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
dot-product
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typebeam/5a341bff-d52b-440b-bc06-6e3ef9eee8be
ex:Operation
—
labelbeam/5a341bff-d52b-440b-bc06-6e3ef9eee8be
Compute cosine similarity
References (6)
6 references
ctx:claims/beam/9016225f-e83c-48c0-90be-7022b351ca10- full textbeam-chunktext/plain951 B
doc:beam/9016225f-e83c-48c0-90be-7022b351ca10Show excerpt
- The similarity scores between the query and documents are computed using the cached TF-IDF matrix. ### Applying Caching to Other Parts You can apply similar caching techniques to other parts of your retrieval pipeline: - **Query Par…
ctx:claims/beam/66120f60-83ce-466d-9a19-6cadefd30586ctx: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/fa1ef1c1-24c6-4f98-8255-600e4bf6a46c- full textbeam-chunktext/plain1 KB
doc:beam/fa1ef1c1-24c6-4f98-8255-600e4bf6a46cShow excerpt
max_length=context_window, padding='max_length', truncation=True, return_attention_mask=True, return_tensors='pt' ) return { 'query': query, …
ctx:claims/beam/5e1fccc0-109f-4d58-b6c4-6482a168aad7- full textbeam-chunktext/plain1 KB
doc:beam/5e1fccc0-109f-4d58-b6c4-6482a168aad7Show excerpt
for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_contextual_embeddings(syn)) for syn in synonyms] closest_synonyms.extend([synon…
ctx:claims/beam/5a341bff-d52b-440b-bc06-6e3ef9eee8be
See also
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