Texts List
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Texts List has 5 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
Mostly:rdf:type(2), contains(1), element type(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (5)
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.
computedFromComputed From(1)
- Tokenized Texts
ex:tokenized-texts
createsListCreates List(1)
- Batch Processing
ex:batch-processing
hasArgumentHas Argument(1)
- Tokenizer Call
ex:tokenizer-call
hasVariableHas Variable(1)
- Batch Inference Test
ex:batch-inference-test
parameterParameter(1)
- Perform Batch Inference Function
ex:perform-batch-inference-function
Other facts (5)
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.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | List | [1] |
| Rdf:type | Input Data | [2] |
| Contains | 5000 | [1] |
| Element Type | string | [1] |
| Created by | List Multiplication | [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.
References (2)
ctx:claims/beam/cf0f131f-3746-4a4d-8090-55a6c610aac6- full textbeam-chunktext/plain1 KB
doc:beam/cf0f131f-3746-4a4d-8090-55a6c610aac6Show excerpt
# Test the batch inference function texts = ["This is a sample text"] * 5000 # Create a list of 5000 texts start_time = time.time() outputs = perform_batch_inference(texts) end_time = time.time() print(f"Inference time: {end_time - start_t…
ctx:claims/beam/e8aa5db9-3e5f-4e4b-b042-f2179d9b2b8f- full textbeam-chunktext/plain1 KB
doc:beam/e8aa5db9-3e5f-4e4b-b042-f2179d9b2b8fShow excerpt
tokenized_texts = [tokenize_text(text) for text in texts] # Evaluate accuracy def evaluate_accuracy(tokenized_texts, ground_truth): correct = 0 total = 0 for tokenized, truth in zip(tokenized_texts, ground_truth): for t…
See also
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