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Corrected Tokens has 9 facts recorded in Dontopedia across 3 references, with 1 live disagreement.
Mostly:rdf:type(2), description(1), type(1)
extractedFromIndexextractedFromconvertedFromisVariableOther 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.
convertsConverts(1)ex:string_joinconvertsIdsToTokensConverts Ids to Tokens(1)ex:token-conversiondeclaresVariableDeclares Variable(1)ex:tokenize-input-textjoinsJoins(1)ex:string-joinTimeline 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.
doc:beam/3cb97947-2304-4ba1-a2c5-598750f9b2f9dist = distance(word, dict_word) if dist < min_distance and dist <= threshold: min_distance = dist closest_word = dict_word return closest_word tokenizer = BertTokenizer.from_pretrained('bert-bas…
doc:beam/81b08382-6139-462b-a047-4231b5c0a4bbdp[i][j] = dp[i - 1][j - 1] else: dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1]) return dp[len1][len2] def spelling_correction(input_text): """Apply spelling correction…
doc:beam/6da40d00-6d2d-43d3-bd9f-ac89c0a9d73aBy using this function, you can easily compute the average error rate and the distribution of correction statuses for your dataset, providing better insights for your analysis. [Turn 10366] User: Kathryn and I are outlining 3 spelling corr…
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