val_dataset
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
val_dataset has 18 facts recorded in Dontopedia across 5 references, with 2 live disagreements.
Mostly:contains(6), rdf:type(2), has image count(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (3)
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.
datasetDataset(1)
- Val Loader
ex:val-loader
has-validation-datasetHas Validation Dataset(1)
- Training Config
ex:training-config
usesDatasetUses Dataset(1)
- Val Loader
ex:val-loader
Other facts (17)
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 |
|---|---|---|
| Contains | Val Inputs | [3] |
| Contains | Val Labels | [3] |
| Contains | Val Inputs | [4] |
| Contains | Val Targets | [4] |
| Contains | Val Inputs | [5] |
| Contains | Val Targets | [5] |
| Rdf:type | Tensor Dataset | [4] |
| Rdf:type | Tensor Dataset | [5] |
| Has Image Count | 5000 | [1] |
| Total Tokens | 677661 | [2] |
| Epoch Time Min At12ktoks | 1 | [2] |
| Has Eot Tokens | 3000 | [2] |
| Has Num Examples | 3000 | [2] |
| Steps Per Epoch | 82 | [2] |
| Is Instance of | Tensor Dataset | [3] |
| Intended for | Data Loader | [4] |
| Pairs | Val Inputs and Val Targets | [4] |
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 (5)
ctx:discord/blah/watt-activation/part-252ctx:discord/blah/watt-activation/part-168ctx:claims/beam/56ec773d-331c-4612-b327-318a1a96426f- full textbeam-chunktext/plain1 KB
doc:beam/56ec773d-331c-4612-b327-318a1a96426fShow excerpt
```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset # Example data preparation inputs = torch.randn(3000, 128) # Example input data labels = torch.randn(3000, 1) …
ctx:claims/beam/2739fb08-c4fc-4bb6-b143-e05bc2133eae- full textbeam-chunktext/plain1 KB
doc:beam/2739fb08-c4fc-4bb6-b143-e05bc2133eaeShow excerpt
```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error class MyMod…
ctx:claims/beam/16f65671-d07e-48d2-acab-39f052189088- full textbeam-chunktext/plain1 KB
doc:beam/16f65671-d07e-48d2-acab-39f052189088Show excerpt
return x # Initialize scorer, optimizer, and loss function scorer = ComplexityScorer() optimizer = optim.Adam(scorer.parameters(), lr=1e-5, weight_decay=1e-5) loss_fn = nn.MSELoss() # Example data inputs = torch.randn(1000, 128) t…
See also
Keep researching
Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.