---
license: mit
task_categories:
- text-classification
- sentence-similarity
- token-classification
language:
- es
tags:
- wikipedia
- spanish
- es
- segmentation
- sentence-segmentation
- document-segmentation
- sentence-transformer
- long-context
- bpe
- tokenized
- memory-mapped
- nlp
pretty_name: Wikipedia Article Segmentation ES - Tokenized
size_categories:
- 100M<n<1B
---
# 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

- [Overview](#overview)
- [Dataset Origin](#dataset-origin)
- [Task Categories](#task-categories)
- [Language](#language)
- [License](#license)
- [Dataset Splits](#dataset-splits)
- [Tokenized Dataset Structure](#tokenized-dataset-structure)
- [Metadata (`info.json`)](#metadata-infojson)
  - [Example Structure](#example-structure)
- [Derived Attributes](#derived-attributes)
  - [`max_sentences`](#max_sentences)
  - [`max_tokens`](#max_tokens)
- [Data Fields (Per Sample)](#data-fields-per-sample)
  - [Notes](#notes)
- [Loading the Tokenized Dataset](#loading-the-tokenized-dataset)
- [Using a DataLoader](#using-a-dataloader)
- [Output Formats](#output-formats)
- [Design Goals](#design-goals)
- [Reproducibility](#reproducibility)
- [Known Limitations](#known-limitations)
- [Intended Use](#intended-use)
- [Citation](#citation)
- [Author](#author)


---

## 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:

```text
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

```json
{
  "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.

```python
from src.tokenized_dataset import TokenizedSegmentationDataset

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

---

## Using a DataLoader

````python
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.

````python
# 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