load_labels
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-05.)
load_labels has 23 facts recorded in Dontopedia across 2 references, with 6 live disagreements.
Mostly:returns(3), rdf:type(2), returns multiple values(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound 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.
containsFunctionContains Function(1)
- Example
ex:example
dependsOnDepends on(1)
- Extract Features Function
ex:extract-features-function
hasComponentHas Component(1)
- Document Classification System
ex:document-classification-system
isReferencedByIs Referenced by(1)
- Label File Variable
ex:label_file-variable
Other facts (22)
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 |
|---|---|---|
| Returns | file_paths | [1] |
| Returns | labels | [1] |
| Returns | File Paths and Labels | [2] |
| Rdf:type | Python Function | [1] |
| Rdf:type | Function | [2] |
| Returns Multiple Values | 2 | [1] |
| Returns Multiple Values | Two Values | [2] |
| Extracts Column | File Path Column | [2] |
| Extracts Column | Label Column | [2] |
| Returns Tuple of | File Paths Array | [2] |
| Returns Tuple of | Labels Array | [2] |
| Parses Data Frame Column | File Path Column | [2] |
| Parses Data Frame Column | Label Column | [2] |
| Called With | label_file | [1] |
| Has Parameter | Label File | [2] |
| Reads From | Csv File | [2] |
| Has Comment | Comment Load Labeled Data | [2] |
| Uses Method | Pandas Read Csv | [2] |
| Returns Tuple | true | [2] |
| Designed for | Labeled Data Processing | [2] |
| Expected Input Format | Csv Format | [2] |
| Defined With | Def Keyword | [2] |
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/3357fa78-fc66-4edb-b217-59cc430fe2b9- full textbeam-chunktext/plain1 KB
doc:beam/3357fa78-fc66-4edb-b217-59cc430fe2b9Show excerpt
file_ext = os.path.splitext(file)[1].lower() file_path = os.path.join(doc_path, file) if re.match(r'\.txt$', file_ext): with open(file_path, 'r', encoding='utf-8') as f: content =…
ctx:claims/beam/e3b7ad28-c610-499f-b527-47a2d7f6872f- full textbeam-chunktext/plain1 KB
doc:beam/e3b7ad28-c610-499f-b527-47a2d7f6872fShow excerpt
Let's walk through an example that combines semi-supervised learning and active learning to handle documents without clear labels. #### Step 1: Load and Prepare Data ```python import os import re import pandas as pd from sklearn.feature_e…
See also
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