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Datasets:
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wikipedia_articles_es_tokenized

Tasks:
Text Classification
Sentence Similarity
Token Classification
Languages:
Spanish
Size:
100M<n<1B
Tags:
wikipedia
spanish
es
segmentation
sentence-segmentation
document-segmentation
License:
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Wikipedia Article Segmentation ES — Tokenized Dataset

Overview

Wikipedia Article Segmentation ES — Tokenized is a large-scale, fully tokenized version of the Wikipedia Article Segmentation ES dataset.
It is designed for efficient training of sentence and document segmentation models, enabling high-throughput access through memory-mapped arrays.

This dataset provides pre-tokenized inputs, segmentation labels, and masks, removing the need for on-the-fly tokenization and sentence splitting during training.

Contents

Dataset Origin

This tokenized dataset is derived from the base dataset:

Wikipedia Article Segmentation ES

The base dataset consists of segmented Spanish Wikipedia articles, where each sample may contain multiple concatenated articles, preserving paragraph and sentence structure.

The tokenized version applies:

  • Sentence segmentation using SpaCy
  • Subword tokenization using a custom-trained BPE tokenizer
  • Fixed-size padding and masking
  • Memory-mapped storage for scalability

Task Categories

  • Text segmentation
  • Sentence boundary detection
  • Long-document modeling
  • Text classification
  • Sentence similarity
  • Document-level representation learning

Language

  • Spanish (es)

License

  • MIT

Wikipedia content is redistributed under its original license terms.

Dataset Splits

The dataset is divided into three subsets:

Split Name Description
Train wikipedia-es-A000 26,510 grouped samples
Validation wikipedia-es-A001 3,336 grouped samples
Test wikipedia-es-A002 6,557 grouped samples

Each split is tokenized independently using the same tokenizer configuration.

Tokenized Dataset Structure

Each tokenized dataset directory contains:

tokenized_dataset/
├── info.json
├── x.memmap        # Tokenized input IDs
├── y.memmap        # Sentence boundary labels
├── x_mask.memmap   # Attention masks
├── y_mask.memmap   # Sentence validity mask
├── y_cand.memmap   # Sentence candidate mask

All arrays are stored as NumPy memmaps for fast, low-memory access.

Metadata (info.json)

The info.json file describes the layout, data types, and tensor shapes of the tokenized dataset stored on disk.
It is required to correctly map the memory-mapped arrays and guarantees dataset integrity through a unique fingerprint.

Example Structure 

{
  "samples": 26510,
  "fingerprint": "...",
  "x": {
    "name": "x",
    "dtype": "int32",
    "samples": 26510,
    "element_shape": [max_sentences, max_tokens]
  },
  "y": {
    "name": "y",
    "dtype": "int8",
    "samples": 26510,
    "element_shape": [max_sentences]
  },
  "x_mask": {
    "name": "x_mask",
    "dtype": "int8",
    "samples": 26510,
    "element_shape": [max_sentences, max_tokens]
  },
  "y_mask": {
    "name": "y_mask",
    "dtype": "int8",
    "samples": 26510,
    "element_shape": [max_sentences]
  },
  "y_cand": {
    "name": "y_cand",
    "dtype": "int8",
    "samples": 26510,
    "element_shape": [max_sentences]
  }
}

Derived Attributes

The following attributes are inferred from the metadata and are consistent across the dataset:

max_sentences

Maximum number of sentences per sample.
Samples with fewer sentences are padded up to this limit.

max_tokens

Maximum number of tokens per sentence.
Sentences longer than this value are truncated.

These fixed dimensions allow efficient batching and fast memory-mapped access.

Data Fields (Per Sample)

Each sample in the tokenized dataset consists of the following tensors:

Field Shape Type Description
x max_sentences × max_tokens int32 Tokenized input IDs
x_mask max_sentences × max_tokens int8 Attention mask for valid tokens
y max_sentences int8 Sentence boundary labels (1 = boundary)
y_mask max_sentences int8 Mask indicating valid sentences
y_cand max_sentences int8 Candidate positions for sentence boundaries

Notes

  • All arrays are stored as NumPy memory-mapped files for efficient disk access.
  • Padding positions are always masked out using x_mask and y_mask.
  • y_cand restricts boundary prediction to structurally valid positions (e.g. paragraph breaks or article starts).
  • The dataset fingerprint ensures compatibility between the dataset and the tokenizer configuration.

This structure allows models to reason explicitly about sentence structure, boundaries, and padding, while maintaining high training throughput.

Loading the Tokenized Dataset

The tokenized dataset is designed to be loaded directly from disk using memory-mapped arrays.

from src.tokenized_dataset import TokenizedSegmentationDataset

dataset = TokenizedSegmentationDataset(
    tokenized_dataset="/path/to/tokenized_dataset",
    percentage=1.0,
    return_type=dict
)

Using a DataLoader 

loader = dataset.get_loader(
    batch_size=8,
    shuffle=True,
    num_workers=0
)

The loader yields fully prepared tensors, ready to be passed to a model without additional preprocessing.

Output Formats

The dataset supports two output formats, configurable via the return_type parameter.

# Dictionary format (default)
{
  "input": x,
  "input_mask": x_mask,
  "labels": y,
  "output_mask": y_mask,
  "candidate_mask": y_cand
}

# Tuple format
(x, y, x_mask, y_mask, y_cand)

The tuple format is intended for lightweight or legacy training loops.

Design Goals

This tokenized dataset is designed to:

  • Remove runtime tokenization and sentence segmentation overhead
  • Enable fast iteration over very large datasets
  • Support long-context document segmentation models
  • Minimize RAM usage through memory mapping
  • Provide explicit structural supervision for sentence boundaries

Reproducibility

The dataset is fully reproducible given:

  • The same Wikipedia ZIM snapshot
  • The same tokenizer configuration
  • The same sentence segmentation parameters
  • The same random seed

No heuristic filtering is applied beyond sentence segmentation and whitespace normalization, making the dataset suitable for controlled experiments and benchmarking.

Known Limitations

  • The number of sentences per sample is capped at max_sentences
  • Token sequences are truncated to max_tokens
  • Titles are not included in the tokenized representation
  • Internal Wikipedia references are not preserved
  • Sentence boundaries are restricted to predefined candidate positions

Intended Use

This dataset is intended for research and development in:

  • Sentence and document segmentation
  • Boundary detection models
  • Long-context language modeling
  • Structured document understanding
  • Spanish-language NLP benchmarks

Citation

If you use this dataset in academic or research work, please cite:

Alberto Palomo Alonso
Universidad de Alcalá — Escuela Politécnica Superior
Spanish Wikipedia (offline ZIM snapshot)

Author

Alberto Palomo Alonso
Universidad de Alcalá
Escuela Politécnica Superior

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