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README.md

@@ -1,209 +1,209 @@
----
-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/*.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
-
-![CCNet Perplexity Histogram](images/wikisource_fr_ccnet_perplexity_histogram.png)
-
-- **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"
-}
-```
+---
+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
+
+![CCNet Perplexity Histogram](images/wikisource_fr_ccnet_perplexity_histogram.png)
+
+- **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"
+}
+```