tokenized_datasets
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
tokenized_datasets has 14 facts recorded in Dontopedia across 4 references, with 4 live disagreements.
Mostly:rdf:type(3), has member(3), contains split(2)
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.
derivedFromDerived From(2)
- Eval Dataset
ex:eval-dataset - Train Dataset
ex:train-dataset
isComputedFromIs Computed From(1)
- True Labels
ex:true-labels
iteratesOverIterates Over(1)
- List Comprehension
ex:list-comprehension
producesProduces(1)
- Tokenization Operation
ex:tokenization-operation
Other facts (12)
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 | Dataset Collection | [2] |
| Rdf:type | Tokenized Dataset | [3] |
| Rdf:type | Tokenized Dataset Collection | [4] |
| Has Member | Tokenized Datasets Train | [2] |
| Has Member | Tokenized Datasets Validation | [2] |
| Has Member | Tokenized Datasets Test | [2] |
| Contains Split | Train Dataset | [4] |
| Contains Split | Eval Dataset | [4] |
| Has Key | Test Key | [1] |
| Has Attribute | Test | [1] |
| Has Structure | Dictionary Like | [1] |
| Result of | Tokenization Operation | [3] |
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 (4)
ctx:claims/beam/d59bebd7-3375-41f4-baef-97a26916a897- full textbeam-chunktext/plain1 KB
doc:beam/d59bebd7-3375-41f4-baef-97a26916a897Show excerpt
predicted_labels = [tokenizer.decode(pred, skip_special_tokens=True) for pred in predictions] # Ground truth labels true_labels = [item['text'] for item in tokenized_datasets['test']] # Calculate accuracy accuracy = accuracy_score(true_la…
ctx:claims/beam/75f58362-300a-4d5c-94a5-4285b391366e- full textbeam-chunktext/plain1 KB
doc:beam/75f58362-300a-4d5c-94a5-4285b391366eShow excerpt
#### 3. Define Training Arguments ```python # Define training arguments training_args = TrainingArguments( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=2, # Smaller batch size for CPU per_device_…
ctx:claims/beam/529ed2d2-aaf0-4ebb-a482-7fd789500505- full textbeam-chunktext/plain1 KB
doc:beam/529ed2d2-aaf0-4ebb-a482-7fd789500505Show excerpt
- Utilize efficient libraries and frameworks that are optimized for CPU usage, such as TensorFlow or PyTorch. ### Example Implementation Here's an example of how you can fine-tune Llama 2 13B on a CPU with these strategies: #### 1. Lo…
ctx:claims/beam/a287a209-7227-4d35-88d1-e63467e5486c- full textbeam-chunktext/plain1 KB
doc:beam/a287a209-7227-4d35-88d1-e63467e5486cShow 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_…
See also
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