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Skip Special Tokens

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

Skip Special Tokens has 14 facts recorded in Dontopedia across 7 references, with 2 live disagreements.

14 facts·7 predicates·7 sources·2 in dispute

Mostly:rdf:type(5), has value(4), argument value(1)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

Has Valuein disputehasValue

  • true[2]sourceall time · D59bebd7 3375 41f4 Baef 97a26916a897
  • true[3]all time · 0f668a3a 349a 49b5 Bde3 839e439e5464
  • True[4]all time · 79401ce7 B88b 4739 B589 61c2e1897bce
  • true[5]sourceall time · E17dfbaf Ae88 4a1c 897d 71a2620730b3

Argument ValueargumentValue

  • true[1]sourceall time · 479453f6 Dab2 4d85 9f18 0cb20af42271

Argument NameargumentName

  • skip_special_tokens[1]sourceall time · 479453f6 Dab2 4d85 9f18 0cb20af42271

Is Decode OptionisDecodeOption

  • true[5]sourceall time · E17dfbaf Ae88 4a1c 897d 71a2620730b3

Rdfs:labelrdfs:label

  • skip_special_tokens[4]all time · 79401ce7 B88b 4739 B589 61c2e1897bce

Is Set toisSetTo

  • true[6]all time · 4b7147d6 1149 49f0 Aeec C5c3a39f9c97

Inbound mentions (4)

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.

hasParameterHas Parameter(2)

calledWithCalled With(1)

usesKeywordArgumentUses Keyword Argument(1)

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.

14 undated
—
argumentNamebeam/479453f6-dab2-4d85-9f18-0cb20af42271
skip_special_tokens
—
argumentValuebeam/479453f6-dab2-4d85-9f18-0cb20af42271
true
—
hasValuebeam/d59bebd7-3375-41f4-baef-97a26916a897
true
—
hasValuebeam/0f668a3a-349a-49b5-bde3-839e439e5464
true
—
hasValuebeam/79401ce7-b88b-4739-b589-61c2e1897bce
True
—
hasValuebeam/e17dfbaf-ae88-4a1c-897d-71a2620730b3
true
—
isDecodeOptionbeam/e17dfbaf-ae88-4a1c-897d-71a2620730b3
true
—
isSetTobeam/4b7147d6-1149-49f0-aeec-c5c3a39f9c97
true
—
labelbeam/79401ce7-b88b-4739-b589-61c2e1897bce
skip_special_tokens
—
typebeam/d59bebd7-3375-41f4-baef-97a26916a897
ex:configuration-parameter
—
typebeam/f65cac65-1aba-4d49-bd0b-30f129893de6
ex:DecodingOption
—
typebeam/479453f6-dab2-4d85-9f18-0cb20af42271
ex:KeywordArgument
—
typebeam/79401ce7-b88b-4739-b589-61c2e1897bce
ex:Parameter
—
typebeam/0f668a3a-349a-49b5-bde3-839e439e5464
ex:Parameter

References (7)

7 references
  1. [1]beam-chunk3 facts
    customctx:claims/beam/479453f6-dab2-4d85-9f18-0cb20af42271
    • full textbeam-chunk
      text/plain1 KBdoc:beam/479453f6-dab2-4d85-9f18-0cb20af42271
      Show excerpt
      reformulated_query = suggestions[0] else: reformulated_query = query else: reformulated_query = query end_time = time.time() return reformulated_query, end_time - start_time # Define a fu…
  2. [2]beam-chunk2 facts
    customctx:claims/beam/d59bebd7-3375-41f4-baef-97a26916a897
    • full textbeam-chunk
      text/plain1 KBdoc:beam/d59bebd7-3375-41f4-baef-97a26916a897
      Show excerpt
      predicted_labels = [tokenizer.decode(pred, skip_special_tokens=True) for pred in predictions] # Ground truth labels true_labels = [item['text'] for item in tokenized_datasets['test']] # Calculate accuracy accuracy = accuracy_score(true_la…
  3. [3]0f668a3a 349a 49b5 Bde3 839e439e54642 facts
    customctx:claims/beam/0f668a3a-349a-49b5-bde3-839e439e5464
  4. [4]79401ce7 B88b 4739 B589 61c2e1897bce3 facts
    customctx:claims/beam/79401ce7-b88b-4739-b589-61c2e1897bce
  5. [5]beam-chunk2 facts
    customctx:claims/beam/e17dfbaf-ae88-4a1c-897d-71a2620730b3
    • full textbeam-chunk
      text/plain1 KBdoc:beam/e17dfbaf-ae88-4a1c-897d-71a2620730b3
      Show excerpt
      2. **Tokenization**: Tokenization can also be a bottleneck. Ensure you are using efficient tokenization settings. 3. **Batch Processing**: If possible, process queries in batches to reduce overhead. ### Example Optimization If the `model.…
  6. [6]4b7147d6 1149 49f0 Aeec C5c3a39f9c971 fact
    customctx:claims/beam/4b7147d6-1149-49f0-aeec-c5c3a39f9c97
  7. [7]beam-chunk1 fact
    customctx:claims/beam/f65cac65-1aba-4d49-bd0b-30f129893de6
    • full textbeam-chunk
      text/plain1 KBdoc:beam/f65cac65-1aba-4d49-bd0b-30f129893de6
      Show excerpt
      tokenizer = AutoTokenizer.from_pretrained(model_name) class LLMBasedReformulator(TransformerMixin): def fit(self, X, y=None): return self def transform(self, X): # Implement LLM-based reformulation logic here …

See also

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