train dataset
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
train dataset has 24 facts recorded in Dontopedia across 10 references, with 2 live disagreements.
24 facts·19 predicates·10 sources·2 in dispute
Mostly:rdf:type(4), has size(1), consists of(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (10)
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.
comparesCompares(2)
- Feature Distribution
ex:feature-distribution - Label Distribution
ex:label-distribution
appliesToApplies to(1)
- Data Augmentation
ex:data-augmentation
createdForCreated for(1)
- Dataloader Objects
ex:dataloader-objects
isDatasetIs Dataset(1)
- 500k Token Dataset
ex:500k-token-dataset
producesProduces(1)
- Data Preparation
ex:data-preparation
rdf:typeRdf:type(1)
- Train Split
ex:train-split
requiresRequires(1)
- Fine Tuning
ex:fine-tuning
trainedOnTrained on(1)
- Family of Models
ex:family-of-models
usedForUsed for(1)
- Dataloader Objects
ex:dataloader-objects
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.
22 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Dataset | [4] |
| Rdf:type | Dataset | [6] |
| Rdf:type | Dataset | [8] |
| Rdf:type | Dataset | [10] |
| Has Size | 95 | [1] |
| Consists of | Images | [1] |
| Computes Steps Per Epoch | 622 | [2] |
| Has Train Units | 653247430 | [2] |
| Has Unit Size | 8 | [2] |
| Has Val Tokens | 6459383 | [2] |
| Uses Packing | flat contiguous stream | [2] |
| Has Bytes | 653247430 | [3] |
| Contains Reinforcement Learning | false | [4] |
| Has Reward Mechanism | false | [4] |
| Has Penalty Mechanism | false | [4] |
| Has Example Count | 186015 | [5] |
| Processed in Phase | Training Process | [5] |
| Image Count | 566747 | [6] |
| Size Is Approximate | 566000 | [6] |
| Size Qualifier | large | [6] |
| Used by | Dataloader | [7] |
| Synonym | Training Set | [9] |
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.
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hasSizeblah/watt-activation/part-255
95
—
consistsOfblah/watt-activation/part-255
ex:images
—
computesStepsPerEpochblah/watt-activation/part-266
622
—
hasTrainUnitsblah/watt-activation/part-266
653247430
—
hasUnitSizeblah/watt-activation/part-266
8
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hasValTokensblah/watt-activation/part-266
6459383
—
usesPackingblah/watt-activation/part-266
flat contiguous stream
—
hasBytesblah/watt-activation/part-420
653247430
—
typeblah/omega/837
ex:Dataset
—
containsReinforcementLearningblah/omega/837
false
—
hasRewardMechanismblah/omega/837
false
—
hasPenaltyMechanismblah/omega/837
false
—
hasExampleCountblah/watt-activation/168
186015
—
processedInPhaseblah/watt-activation/168
ex:training-process
—
typeblah/watt-activation/251
ex:Dataset
—
labelblah/watt-activation/251
train dataset
—
imageCountblah/watt-activation/251
566747
—
sizeIsApproximateblah/watt-activation/251
566000
—
sizeQualifierblah/watt-activation/251
large
—
usedBybeam/9dc04f5c-41c0-4f03-9508-0f47a466d19e
ex:dataloader
—
typebeam/52f919f5-82fe-445f-9546-0c93b47bf484
ex:Dataset
—
synonymbeam/cc1315f0-7954-44ad-96b4-19d6a2409d50
ex:training-set
—
typebeam/219bb715-7cc6-43cc-a7a9-1d1f63a48ed8
ex:dataset
—
labelbeam/219bb715-7cc6-43cc-a7a9-1d1f63a48ed8
Training Dataset
References (10)
10 references
ctx:discord/blah/watt-activation/part-255ctx:discord/blah/watt-activation/part-266ctx:discord/blah/watt-activation/part-420ctx:discord/blah/omega/837- full textomega-837text/plain2 KB
doc:agent/omega-837/09ff7339-3969-4b55-8739-569141d3d630Show excerpt
[2026-01-12 20:47] therosegoblin: <@1438866165475708979> So. If you’re interested in the architecture I’m building here’s a quick overview of how it works. I have trained a family of Mistral base models through supervised learning on data …
ctx:discord/blah/watt-activation/168- full textwatt-activation-168text/plain3 KB
doc:agent/watt-activation-168/73ee12a6-c466-46d0-8fcc-aeb7b6f8614eShow excerpt
[2026-03-09 19:32] xenonfun: ``` [train] Tokenizing 186,015 examples... 20,000/186,015 (4,496,870 tokens) 40,000/186,015 (8,960,555 tokens) 60,000/186,015 (13,450,804 tokens) 80,000/186,015 (17,894,743 tokens) 100,000/186,015 …
ctx:discord/blah/watt-activation/251- full textwatt-activation-251text/plain1 KB
doc:agent/watt-activation-251/0d79165d-ca43-48df-b924-6b76b157d1a5Show excerpt
[2026-03-12 13:11] xenonfun: ✅ Phase 0 confirmed working — r_global rises monotonically from 0.07 → 0.96 across 16 steps on the production multimodal checkpoint. The architecture supports iterative generation. This is the green light to p…
ctx:claims/beam/9dc04f5c-41c0-4f03-9508-0f47a466d19e- full textbeam-chunktext/plain1 KB
doc:beam/9dc04f5c-41c0-4f03-9508-0f47a466d19eShow excerpt
#### Dropout Add dropout layers to your model to randomly drop out a fraction of the neurons during training. ```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset …
ctx:claims/beam/52f919f5-82fe-445f-9546-0c93b47bf484- full textbeam-chunktext/plain1 KB
doc:beam/52f919f5-82fe-445f-9546-0c93b47bf484Show excerpt
[Turn 8425] Assistant: To prevent overfitting in your dense retrieval model, you can implement several regularization techniques. Here are some specific methods you can use: ### 1. **Dropout** Dropout randomly sets a fraction of input unit…
ctx:claims/beam/cc1315f0-7954-44ad-96b4-19d6a2409d50- full textbeam-chunktext/plain933 B
doc:beam/cc1315f0-7954-44ad-96b4-19d6a2409d50Show excerpt
- Added an extra linear layer (`fc3`) to increase the depth of the model, allowing it to capture more complex patterns in the data. 4. **Weight Decay (L2 Regularization)**: - Included weight decay in the `optim.Adam` optimizer with a…
ctx:claims/beam/219bb715-7cc6-43cc-a7a9-1d1f63a48ed8- full textbeam-chunktext/plain1 KB
doc:beam/219bb715-7cc6-43cc-a7a9-1d1f63a48ed8Show excerpt
- **Feature Distribution**: Compare the distribution of features between the training and validation/test datasets. Significant differences in the distribution of key features can indicate skew. - **Label Distribution**: Check if the …
See also
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