|
|
---
|
|
|
language:
|
|
|
- fr
|
|
|
license: cc-by-sa-4.0
|
|
|
size_categories: 100K<n<1M
|
|
|
task_categories:
|
|
|
- text-generation
|
|
|
task_ids:
|
|
|
- language-modeling
|
|
|
- masked-language-modeling
|
|
|
configs:
|
|
|
- config_name: default
|
|
|
data_files:
|
|
|
- split: train
|
|
|
path: "data/train/*.parquet"
|
|
|
dataset_info:
|
|
|
- config_name: "default"
|
|
|
features:
|
|
|
- name: title
|
|
|
dtype: string
|
|
|
- name: authors
|
|
|
dtype: string
|
|
|
- name: identifier
|
|
|
dtype: int64
|
|
|
- name: date_created
|
|
|
dtype: string
|
|
|
- name: wiki_url
|
|
|
dtype: string
|
|
|
- name: text
|
|
|
dtype: string
|
|
|
- name: quality_signals
|
|
|
dtype: string
|
|
|
- name: version_id
|
|
|
dtype: int64
|
|
|
splits:
|
|
|
- name: train
|
|
|
num_bytes: 7974052431
|
|
|
num_examples: 472517
|
|
|
download_size: 4584532829
|
|
|
dataset_size: 7974052431
|
|
|
---
|
|
|
|
|
|
# Wikisource FR Dataset
|
|
|
|
|
|
- [Wikisource FR Dataset](#wikisource-fr-dataset)
|
|
|
- [Dataset Structure](#dataset-structure)
|
|
|
- [Data Instances](#data-instances)
|
|
|
- [Data Fields](#data-fields)
|
|
|
- [Example Usage (Python)](#example-usage-python)
|
|
|
- [Intended Use][def]
|
|
|
- [Dataset Statistics](#dataset-statistics)
|
|
|
- [Token Statistics](#token-statistics)
|
|
|
- [Quality Signal: CCNet Perplexity](#quality-signal-ccnet-perplexity)
|
|
|
- [License](#license)
|
|
|
- [Aknowledgements](#aknowledgements)
|
|
|
- [Citation](#citation)
|
|
|
|
|
|
This dataset provides a cleaned plain-text version of French open texts from [fr.wikisource.org](https://fr.wikisource.org/). The content is distributed without HTML tags or MediaWiki templates, and only retains minimal Markdown syntax (headers, lists, tables) to facilitate downstream NLP and LLM usage.
|
|
|
|
|
|
The dataset is built using the [Wikimedia Enterprise Snapshot APIs](https://enterprise.wikimedia.com/api/) which allow retrieving complete Wikimedia projects as a database dumps file.
|
|
|
|
|
|
## Dataset Structure
|
|
|
|
|
|
### Data Instances
|
|
|
|
|
|
Each record corresponds to a single Wikisource document or work.
|
|
|
|
|
|
Example:
|
|
|
|
|
|
```text
|
|
|
{
|
|
|
'title': ' De la Chasse (Trad. Talbot)/06',
|
|
|
'authors': 'Xénophon',
|
|
|
'identifier': 1608939,
|
|
|
'date_created': ' 2013-11-03T08:40:48Z',
|
|
|
'wiki_url': ' https://fr.wikisource.org/wiki/De_la_Chasse_(Trad._Talbot)/06',
|
|
|
'text': '### CHAPITRE VI.\nDe l’armure des chiens, du temps propre à la quête, du garde-filet, ...',
|
|
|
'quality_signals': '{"ccnet_perplexity": 213.77, "num_tokens": 2455, "doc_length": 9363}',
|
|
|
}
|
|
|
```
|
|
|
|
|
|
### Data Fields
|
|
|
|
|
|
The data fields are consistent across all configurations:
|
|
|
|
|
|
- `title` (`str`): Title of the text.
|
|
|
- `authors` (`str`): Authors of the text.
|
|
|
- `identifier` (`int64`): ID of the text.
|
|
|
- `wiki_url` (`str`): URL of the text on Wikisource.
|
|
|
- `date_created` (`str`): Date of creation of the text.
|
|
|
- `text` (`str`): Content of the text.
|
|
|
- `quality_signals` (`str`): Quality signals of the text.
|
|
|
|
|
|
## Example Usage (Python)
|
|
|
|
|
|
Load the full dataset:
|
|
|
|
|
|
```python
|
|
|
import datasets
|
|
|
|
|
|
ds = datasets.load_dataset("LeMoussel/wikisource_fr", split="train")
|
|
|
```
|
|
|
|
|
|
## Intended Use
|
|
|
|
|
|
Suitable for pretraining French LLMs. This dataset is intended for:
|
|
|
|
|
|
- training and pretraining French language models (LLMs, MLMs),
|
|
|
|
|
|
- evaluating language models on literary and historical French texts,
|
|
|
|
|
|
- NLP research tasks such as text generation, summarization, or segmentation.
|
|
|
|
|
|
It does not contain personal data and is exclusively composed of freely licensed texts from Wikisource.
|
|
|
|
|
|
## Dataset Statistics
|
|
|
|
|
|
The following statistics are provided to facilitate LLM pretraining planning. Exact values may slightly vary depending on the tokenizer and preprocessing strategy.
|
|
|
|
|
|
- Total size of the dataset (in memory): ~7.43 GB
|
|
|
- Total size of the dataset (on disk): ~4.27 GB
|
|
|
|
|
|
- Total number of documents: 472 517 texts
|
|
|
- Total number of characters: 7 422 890 508
|
|
|
- Average document length: ~15 709 characters
|
|
|
|
|
|
### Token Statistics
|
|
|
|
|
|
Estimated using [sentencepiece](https://github.com/google/sentencepiece) tokenizers commonly employed for French LLMs:
|
|
|
|
|
|
- Estimated total tokens: ~1.9B tokens
|
|
|
- Average number of tokens per document: ~4 120 tokens
|
|
|
|
|
|
### Quality Signal: CCNet Perplexity
|
|
|
|
|
|
`CCNet Perplexity` is a linguistic quality indicator used to measure how close a given text is to a high-quality reference corpus, typically Wikipedia.
|
|
|
It is commonly employed in large-scale dataset filtering pipelines for language model training, in order to identify noisy, malformed, or out-of-domain content.
|
|
|
|
|
|
In this dataset, the score is computed using the open-source implementation provided by OpenLLM-France: [CCNet Perplexity Library](https://github.com/OpenLLM-France/Lucie-dataset-filtering/tree/master/src/blmrdata/utils/ccnet)
|
|
|
|
|
|
The value is exposed in the `quality_signals` field under the key `ccnet_perplexity`.
|
|
|
|
|
|
#### Score Interpretation
|
|
|
|
|
|
- **Low perplexity (~100–300)**
|
|
|
Text is linguistically close to Wikipedia:
|
|
|
|
|
|
- well-formed syntax
|
|
|
- standard vocabulary
|
|
|
- coherent structure
|
|
|
- low noise level
|
|
|
|
|
|
- **High perplexity (>1000)**
|
|
|
Text significantly diverges from the reference corpus:
|
|
|
|
|
|
- poorly formatted content
|
|
|
- potential noise or spam
|
|
|
- OCR artifacts
|
|
|
- or highly specialized vocabulary uncommon in Wikipedia
|
|
|
|
|
|
Lower perplexity indicates that the text is more likely under the Wikipedia-trained language model, and therefore closer to the reference domain.
|
|
|
|
|
|
⚠️ A high perplexity score does not necessarily imply low semantic value, but rather a **linguistic distance** from the Wikipedia domain.
|
|
|
|
|
|
#### Observations for the Wikisource FR Dataset
|
|
|
|
|
|
|
|
|
|
|
|
- **Median CCNet Perplexity: 298.84**
|
|
|
|
|
|
This indicates that the majority of Wikisource documents exhibit a *linguistic quality comparable to Wikipedia*, which is consistent with the curated and editorial nature of the source.
|
|
|
|
|
|
- **Extreme values (up to ~183 437)**
|
|
|
These outliers most likely correspond to:
|
|
|
|
|
|
- documents with highly specialized or archaic vocabulary,
|
|
|
- residual formatting issues,
|
|
|
- atypical content structures (tables, lists, annotations),
|
|
|
- or extraction artifacts.
|
|
|
|
|
|
This signal can be leveraged to:
|
|
|
|
|
|
- filter documents based on quality thresholds,
|
|
|
- weight samples during training,
|
|
|
- or analyze quality distributions within the corpus.
|
|
|
|
|
|
## License
|
|
|
|
|
|
The texts originate from Wikisource and are governed by the licenses defined by the Wikimedia Foundation:
|
|
|
|
|
|
- [GNU Free Documentation License 1.3](https://www.gnu.org/licenses/fdl-1.3.html) (GFDL)
|
|
|
|
|
|
- [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/)
|
|
|
|
|
|
Some texts may be available only under the CC BY-SA license or may belong to the public domain. Please refer to Wikimedia [Terms of Use](https://foundation.wikimedia.org/wiki/Policy:Terms_of_Use) for details.
|
|
|
|
|
|
## Aknowledgements
|
|
|
|
|
|
Many thanks to the [Wikimedia Foundation](https://wikimediafoundation.org/) for providing open access to the data and maintaining a high-quality open knowledge ecosystem.
|
|
|
|
|
|
## Citation
|
|
|
|
|
|
```text
|
|
|
@online{wikisource_fr_dump,
|
|
|
author = "LeMoussel Labs",
|
|
|
title = "French plain text of Wikisource",
|
|
|
url = "https://huggingface.co/datasets/LeMoussel/wikisource_fr"
|
|
|
}
|
|
|
```
|
|
|
|
