Datasets:

Alverciito
/

wikipedia_articles_es_tokenized

@@ -1,312 +1,337 @@
-# 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

+---
+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