datasets

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

datasets is existing datasets for simulating real-world scenarios.

44 facts·29 predicates·13 sources·4 in dispute

Mostly:rdf:type(9), used for(2), has method(2)

Inbound mentions (33)

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.

`appliesTo`Applies to(2)

Parallel Executionex:parallel_execution
Vectorized Versionex:vectorized_version

`haveHacked`Have Hacked(2)

Developersex:developers
Usersex:users

`hosts`Hosts(2)

Kaggleex:kaggle
Kaggleex:kaggle

`iteratesOver`Iterates Over(2)

List Comprehensionex:list-comprehension
List Comprehensionex:list-comprehension

`allowsRunFullEpochOfDatasets`Allows Run Full Epoch of Datasets(1)

Multimodal Trainerex:multimodal-trainer

`applied_to`Applied to(1)

Datasets.applyex:datasets.apply

`appliedTo`Applied to(1)

Secure Tuning Functionex:secure-tuning-function

`applies_function_to`Applies Function to(1)

Python Codeex:python-code

`belongsToListedIn`Belongs to Listed in(1)

Some Columnex:some_column

`boundTo`Bound to(1)

Dataset Variableex:dataset-variable

`calledPerDataset`Called Per Dataset(1)

Secure Tuningex:secure_tuning

`confirmsReadiness`Confirms Readiness(1)

Xenonfunex:xenonfun

`contains`Contains(1)

Section Real World Data Collectionex:section-real-world-data-collection

`evidencedBy`Evidenced by(1)

Performance Gainex:performance-gain

`framesAsEasiestOptions`Frames As Easiest Options(1)

Message 2026 03 22 17 40ex:message-2026-03-22-17-40

`has-entity`Has Entity(1)

Source Documentex:source-document

`hasOntologicalDeficit`Has Ontological Deficit(1)

Science Communityex:science-community

`is_applied_to`Is Applied to(1)

Secure Tuning Functionex:secure-tuning-function

`isClassFor`Is Class for(1)

Tensor Datasetex:TensorDataset

`isDownloadingDatasets`Is Downloading Datasets(1)

Lisamegawatts Machineex:lisamegawatts-machine

`isParsingDatasets`Is Parsing Datasets(1)

Lisamegawatts Machineex:lisamegawatts-machine

`offersHelpWith`Offers Help With(1)

Omega Botex:omega-bot

`performsSearchFunction`Performs Search Function(1)

Qld Open Data Portalex:qld-open-data-portal

`processes`Processes(1)

Parallel Processing Sectionex:parallel-processing-section

`provides`Provides(1)

Stanford Nlp Groupex:stanford-nlp-group

`referencesEntity`References Entity(1)

Current Codeex:current_code

`usedForTraining`Used for Training(1)

Huggingface Co Qvac Datasetsex:huggingface-co-qvac-datasets

`usesLibrary`Uses Library(1)

Python Scriptex:python-script

`wantsToRunFullEpoch`Wants to Run Full Epoch(1)

Xenonfunex:xenonfun

Other facts (40)

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.

40 facts

Predicate	Value	Ref
Rdf:type	Python Library	[4]
Rdf:type	Concept	[5]
Rdf:type	Data Frame	[6]
Rdf:type	Dataframe	[7]
Rdf:type	Data Frame	[8]
Rdf:type	Data Frame	[9]
Rdf:type	Data Collection	[11]
Rdf:type	Variable	[12]
Rdf:type	Collection	[13]
Used for	Simulation	[5]
Used for	Measure Performance	[5]
Has Method	Iterrows	[9]
Has Method	Mean	[9]
Is Used in	Column Conversion	[10]
Is Used in	Parallel Processing	[10]
Are Standard for Eval	Numerical Reasoning	[1]
Include Audio	Audio Libri Speech Dataset	[2]
Support Train Val Split	true	[2]
Are Ready	true	[2]
Include Text	Text Fineweb Dataset	[2]
Include Images	Images Coco Dataset	[2]
Prepared for Benchmarking	true	[3]
Description	existing datasets for simulating real-world scenarios	[5]
Part of	Section Real World Data Collection	[5]
Results in	Measure Performance	[5]
Is Subject of	Secure Tuning Application	[6]
Is Initialized by	Pd.read Csv	[9]
Read From	Datasets.csv	[9]
Has Column	Some Column	[9]
Iterated Over	Iterrows	[9]
Iterated by	For Loop	[9]
Column Accessed	Some Column	[9]
Read From Csv	Datasets.csv	[9]
Has Column	Some Column	[10]
Processed by	Secure Tuning	[11]
Assumed Existence	Before Code Execution	[11]
Scope	Module Level	[11]
Assumed Defined	In User Environment	[11]
External Dependency	true	[11]
Plural Form of	Dataset Variable	[13]

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.

44 undated

—

areStandardForEvalblah/unturf/part-6

ex:numerical-reasoning

—

includeAudioblah/watt-activation/part-242

ex:audio-libri-speech-dataset

—

supportTrainValSplitblah/watt-activation/part-242

true

—

areReadyblah/watt-activation/part-242

true

—

includeTextblah/watt-activation/part-242

ex:text-fineweb-dataset

—

includeImagesblah/watt-activation/part-242

ex:images-coco-dataset

—

preparedForBenchmarkingblah/watt-activation/part-505

true

—

typebeam/a287a209-7227-4d35-88d1-e63467e5486c

ex:PythonLibrary

—

typebeam/1a368862-9cd8-42f7-9010-39fa78414257

ex:Concept

—

descriptionbeam/1a368862-9cd8-42f7-9010-39fa78414257

existing datasets for simulating real-world scenarios

—

usedForbeam/1a368862-9cd8-42f7-9010-39fa78414257

ex:simulation

—

partOfbeam/1a368862-9cd8-42f7-9010-39fa78414257

ex:section-real-world-data-collection

—

usedForbeam/1a368862-9cd8-42f7-9010-39fa78414257

ex:measure-performance

—

resultsInbeam/1a368862-9cd8-42f7-9010-39fa78414257

ex:measure-performance

—

typebeam/da6cd555-a414-4790-9a90-ae71c80793a3

ex:DataFrame

—

is_subject_ofbeam/da6cd555-a414-4790-9a90-ae71c80793a3

ex:secure-tuning-application

—

typebeam/1c4871a0-44bd-488f-a027-7e91230cbb93

ex:dataframe

—

labelbeam/1c4871a0-44bd-488f-a027-7e91230cbb93

datasets

—

typebeam/53b6e60a-57f4-4a01-b2a5-ba77515229e4

ex:DataFrame

—

labelbeam/53b6e60a-57f4-4a01-b2a5-ba77515229e4

datasets

—

typebeam/4a0dca96-fee2-4f59-802b-b2430a492797

ex:DataFrame

—

isInitializedBybeam/4a0dca96-fee2-4f59-802b-b2430a492797

ex:pd.read_csv

—

readFrombeam/4a0dca96-fee2-4f59-802b-b2430a492797

ex:datasets.csv

—

hasColumnbeam/4a0dca96-fee2-4f59-802b-b2430a492797

ex:some_column

—

hasMethodbeam/4a0dca96-fee2-4f59-802b-b2430a492797

ex:iterrows

—

iteratedOverbeam/4a0dca96-fee2-4f59-802b-b2430a492797

ex:iterrows

—

hasMethodbeam/4a0dca96-fee2-4f59-802b-b2430a492797

ex:mean

—

iteratedBybeam/4a0dca96-fee2-4f59-802b-b2430a492797

ex:for_loop

—

columnAccessedbeam/4a0dca96-fee2-4f59-802b-b2430a492797

ex:some_column

—

readFromCSVbeam/4a0dca96-fee2-4f59-802b-b2430a492797

ex:datasets.csv

—

is-used-inbeam/4f3f0e67-2593-4f7f-9625-25393b3512e1

ex:column-conversion

—

is-used-inbeam/4f3f0e67-2593-4f7f-9625-25393b3512e1

ex:parallel-processing

—

has-columnbeam/4f3f0e67-2593-4f7f-9625-25393b3512e1

ex:some-column

—

typebeam/d25ccc1d-5d3e-46ea-8f10-a328695c2697

ex:Data_Collection

—

processedBybeam/d25ccc1d-5d3e-46ea-8f10-a328695c2697

ex:secure_tuning

—

assumedExistencebeam/d25ccc1d-5d3e-46ea-8f10-a328695c2697

ex:before_code_execution

—

scopebeam/d25ccc1d-5d3e-46ea-8f10-a328695c2697

ex:module_level

—

assumedDefinedbeam/d25ccc1d-5d3e-46ea-8f10-a328695c2697

ex:in_user_environment

—

externalDependencybeam/d25ccc1d-5d3e-46ea-8f10-a328695c2697

true

—

typebeam/64905869-24bb-45f8-b86a-4196d76ab3c4

ex:Variable

—

labelbeam/64905869-24bb-45f8-b86a-4196d76ab3c4

datasets

—

typebeam/dd276301-ccba-4bf0-8c83-855e2c5ddb6c

ex:Collection

—

labelbeam/dd276301-ccba-4bf0-8c83-855e2c5ddb6c

datasets

—

pluralFormOfbeam/dd276301-ccba-4bf0-8c83-855e2c5ddb6c

ex:dataset-variable

References (13)

13 references

[1]Part 61 fact
ctx:discord/blah/unturf/part-6
[2]Part 2425 facts
ctx:discord/blah/watt-activation/part-242
[3]Part 5051 fact
ctx:discord/blah/watt-activation/part-505
[4]A287a209 7227 4d35 88d1 E63467e5486c1 fact
ctx:claims/beam/a287a209-7227-4d35-88d1-e63467e5486c
- full textbeam-chunk
  text/plain1 KBdoc:beam/a287a209-7227-4d35-88d1-e63467e5486c
  Show excerpt
  Here's the complete example: ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments from datasets import load_dataset import torch # Load your dataset dataset = load_dataset("your_…
[5]1a368862 9cd8 42f7 9010 39fa784142576 facts
ctx:claims/beam/1a368862-9cd8-42f7-9010-39fa78414257
- full textbeam-chunk
  text/plain1 KBdoc:beam/1a368862-9cd8-42f7-9010-39fa78414257
  Show excerpt
  - The `apply_strategy` function applies a strategy and collects performance data using the `collect_data` function. 5. **Evaluate Performance**: - The `evaluate_performance` function compares the performance of each strategy to the t…
[6]Da6cd555 A414 4790 9a90 Ae71c80793a32 facts
ctx:claims/beam/da6cd555-a414-4790-9a90-ae71c80793a3
- full textbeam-chunk
  text/plain1008 Bdoc:beam/da6cd555-a414-4790-9a90-ae71c80793a3
  Show excerpt
  Based on the breakdown and estimation, 14 hours may not be sufficient to finalize 80% of your secure tuning protocols. It would be prudent to increase the allocated time to 16 hours or adjust the scope of the task to fit within the 14-hour …
[7]1c4871a0 44bd 488f A027 7e91230cbb932 facts
ctx:claims/beam/1c4871a0-44bd-488f-a027-7e91230cbb93
- full textbeam-chunk
  text/plain1 KBdoc:beam/1c4871a0-44bd-488f-a027-7e91230cbb93
  Show excerpt
  # Implement secure tuning logic here # Example: Check if a condition is met compliant = row['some_column'] > 0 # Replace with actual logic return compliant # Apply secure tuning to datasets using parallel processing num_co…
[8]53b6e60a 57f4 4a01 B2a5 Ba77515229e42 facts
ctx:claims/beam/53b6e60a-57f4-4a01-b2a5-ba77515229e4
- full textbeam-chunk
  text/plain1 KBdoc:beam/53b6e60a-57f4-4a01-b2a5-ba77515229e4
  Show excerpt
  num_cores = 4 # Adjust based on your system's capabilities tuned_datasets = Parallel(n_jobs=num_cores)(delayed(secure_tuning)(row) for _, row in datasets.iterrows()) # Convert the list of results back to a DataFrame tuned_datasets = pd.Da…
[9]4a0dca96 Fee2 4f59 802b B2430a49279710 facts
ctx:claims/beam/4a0dca96-fee2-4f59-802b-b2430a492797
- full textbeam-chunk
  text/plain1 KBdoc:beam/4a0dca96-fee2-4f59-802b-b2430a492797
  Show excerpt
  datasets = pd.read_csv('datasets.csv') # Convert columns to appropriate data types datasets['some_column'] = pd.to_numeric(datasets['some_column'], errors='coerce') # Define secure tuning function def secure_tuning(row): # Implement s…
[10]4f3f0e67 2593 4f7f 9625 25393b3512e13 facts
ctx:claims/beam/4f3f0e67-2593-4f7f-9625-25393b3512e1
- full textbeam-chunk
  text/plain1 KBdoc:beam/4f3f0e67-2593-4f7f-9625-25393b3512e1
  Show excerpt
  # Convert columns to appropriate data types datasets['some_column'] = pd.to_numeric(datasets['some_column'], errors='coerce') # Define secure tuning function def secure_tuning(row): # Implement secure tuning logic here # Example: C…
[11]D25ccc1d 5d3e 46ea 8f10 A328695c26976 facts
ctx:claims/beam/d25ccc1d-5d3e-46ea-8f10-a328695c2697
- full textbeam-chunk
  text/plain1 KBdoc:beam/d25ccc1d-5d3e-46ea-8f10-a328695c2697
  Show excerpt
  [Turn 9584] User: I'm trying to improve the compliance rate of our secure tuning protocols, currently at 96%, but I'm not sure what optimizations to make, can you review my code and suggest improvements? ```python import numpy as np # Defi…
[12]64905869 24bb 45f8 B86a 4196d76ab3c42 facts
ctx:claims/beam/64905869-24bb-45f8-b86a-4196d76ab3c4
[13]Dd276301 Ccba 4bf0 8c83 855e2c5ddb6c3 facts
ctx:claims/beam/dd276301-ccba-4bf0-8c83-855e2c5ddb6c
- full textbeam-chunk
  text/plain1 KBdoc:beam/dd276301-ccba-4bf0-8c83-855e2c5ddb6c
  Show excerpt
  # Implement secure tuning logic here return np.random.rand(len(dataset)) # Apply secure tuning to datasets tuned_datasets = [secure_tuning(dataset) for dataset in datasets] # Calculate compliance rate compliance_rate = np.mean([np…

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datasets

Inbound mentions (33)

appliesToApplies to(2)

haveHackedHave Hacked(2)

hostsHosts(2)

iteratesOverIterates Over(2)

allowsRunFullEpochOfDatasetsAllows Run Full Epoch of Datasets(1)

applied_toApplied to(1)

appliedToApplied to(1)

applies_function_toApplies Function to(1)

belongsToListedInBelongs to Listed in(1)

boundToBound to(1)

calledPerDatasetCalled Per Dataset(1)

confirmsReadinessConfirms Readiness(1)

containsContains(1)

evidencedByEvidenced by(1)

framesAsEasiestOptionsFrames As Easiest Options(1)

has-entityHas Entity(1)

hasOntologicalDeficitHas Ontological Deficit(1)

is_applied_toIs Applied to(1)

isClassForIs Class for(1)

isDownloadingDatasetsIs Downloading Datasets(1)

isParsingDatasetsIs Parsing Datasets(1)

offersHelpWithOffers Help With(1)

performsSearchFunctionPerforms Search Function(1)

processesProcesses(1)

providesProvides(1)

referencesEntityReferences Entity(1)

usedForTrainingUsed for Training(1)

usesLibraryUses Library(1)

wantsToRunFullEpochWants to Run Full Epoch(1)

Other facts (40)

Timeline

References (13)

See also

Keep researching

`appliesTo`Applies to(2)

`haveHacked`Have Hacked(2)

`hosts`Hosts(2)

`iteratesOver`Iterates Over(2)

`allowsRunFullEpochOfDatasets`Allows Run Full Epoch of Datasets(1)

`applied_to`Applied to(1)

`appliedTo`Applied to(1)

`applies_function_to`Applies Function to(1)

`belongsToListedIn`Belongs to Listed in(1)

`boundTo`Bound to(1)

`calledPerDataset`Called Per Dataset(1)

`confirmsReadiness`Confirms Readiness(1)

`contains`Contains(1)

`evidencedBy`Evidenced by(1)

`framesAsEasiestOptions`Frames As Easiest Options(1)

`has-entity`Has Entity(1)

`hasOntologicalDeficit`Has Ontological Deficit(1)

`is_applied_to`Is Applied to(1)

`isClassFor`Is Class for(1)

`isDownloadingDatasets`Is Downloading Datasets(1)

`isParsingDatasets`Is Parsing Datasets(1)

`offersHelpWith`Offers Help With(1)

`performsSearchFunction`Performs Search Function(1)

`processes`Processes(1)

`provides`Provides(1)

`referencesEntity`References Entity(1)

`usedForTraining`Used for Training(1)

`usesLibrary`Uses Library(1)

`wantsToRunFullEpoch`Wants to Run Full Epoch(1)