Upload README.md

README.md

@@ -1,68 +1,72 @@
----
-license: cc-by-4.0
-task_categories:
-- text-generation
-language:
-- fr
-pretty_name: 📚 FineWiki
-size_categories:
-- 100K<n<1M
-configs:
-- config_name: fr_removed
-  data_files:
-    - split: train
-      path: data/fr_removed.parquet
-- config_name: fr
-  data_files:
-    - split: train
-      path: data/fr.parquet
----
-
-<center>
-    <img src="https://huggingface.co/datasets/LeMoussel/finewiki/resolve/main/finewik-logo.png" alt="FineWiki: High-quality text pretraining dataset derived from the French edition of Wikipedia">
-</center>
-
-# 📚 FineWiki
-
-## Dataset Overview
-
-**FineWiki** is a high-quality French-language dataset designed for pretraining and NLP tasks. It is derived from the French edition of Wikipedia using the *Wikipedia Structured Contents* dataset released by the Wikimedia Foundation on [Kaggle](https://www.kaggle.com/datasets/wikimedia-foundation/wikipedia-structured-contents).
-
-Each entry is a structured JSON line representing a full Wikipedia article, parsed and cleaned from HTML snapshots provided by [Wikimedia Enterprise](https://enterprise.wikimedia.com/docs/snapshot/).
-
-The dataset has been carefully filtered and deduplicated. It retains only the most relevant textual content such as article summaries, short descriptions, main image URLs, infoboxes, and cleaned section texts. Non-textual or noisy elements (like references, citations, and markdown artifacts) have been removed to provide a cleaner signal for NLP model training.
-
-To encourage reusability and transparency, we also provide a version containing the articles **excluded** during filtering (config: `fr_removed`). This enables users to reapply their own filtering strategies. The full data = filtered + removed sets.
-
-## Data Structure
-
-* **Language**: French (`fr`)
-* **Fields**:
-
-  * `TODO`: TODO
-  * `description`: Short description
-
-## Source and Processing
-
-The original data is sourced from the [Wikipedia Structured Contents (Kaggle)](https://www.kaggle.com/datasets/wikimedia-foundation/wikipedia-structured-contents) dataset. It was extracted from HTML snapshots provided by Wikimedia Enterprise, then parsed and cleaned to retain only the most useful and structured textual elements for machine learning.
-
-The dataset has been carefully filtered and deduplicated. The filtering follows the same rules as those applied with [FineWeb2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) using the [Datatrove](https://github.com/huggingface/datatrove) library.
-
-This preprocessing step aims to improve readability, consistency, and structure, helping language models learn more effectively.
-
-## Data Splits
-
-Currently, the dataset is provided as a single `train` split. No predefined validation or test sets are included. Users are encouraged to create their own splits as needed.
-
-## How to Use
-
-You can load FineWiki using the 🤗 `datasets` library like this:
-
-```python
-from datasets import load_dataset
-
-dataset = load_dataset("LeMoussel/finewiki", split="train")
-
-# Example: print the first article
-print(dataset[0])
-```
+---
+license: cc-by-4.0
+task_categories:
+- text-generation
+language:
+- fr
+pretty_name: 📚 FineWiki
+size_categories:
+- 100K<n<1M
+configs:
+- config_name: fr_removed
+  data_files:
+    - split: train
+      path: data/fr_removed.parquet
+- config_name: fr
+  data_files:
+    - split: train
+      path: data/fr.parquet
+---
+
+<center>
+    <img src="https://huggingface.co/datasets/LeMoussel/finewiki/resolve/main/finewik-logo.png" alt="FineWiki: High-quality text pretraining dataset derived from the French edition of Wikipedia">
+</center>
+
+# 📚 FineWiki
+
+## Dataset Overview
+
+**FineWiki** is a high-quality French-language dataset designed for pretraining and NLP tasks. It is derived from the French edition of Wikipedia using the *Wikipedia Structured Contents* dataset released by the Wikimedia Foundation on [Kaggle](https://www.kaggle.com/datasets/wikimedia-foundation/wikipedia-structured-contents).
+
+Each entry is a structured JSON line representing a full Wikipedia article, parsed and cleaned from HTML snapshots provided by [Wikimedia Enterprise](https://enterprise.wikimedia.com/docs/snapshot/).
+
+The dataset has been carefully filtered and deduplicated. It retains only the most relevant textual content such as article summaries, short descriptions, main image URLs, infoboxes, and cleaned section texts. Non-textual or noisy elements (like references, citations, and markdown artifacts) have been removed to provide a cleaner signal for NLP model training.
+
+To encourage reusability and transparency, we also provide a version containing the articles **excluded** during filtering (config: `fr_removed`). This enables users to reapply their own filtering strategies. The full data = filtered + removed sets.
+
+## Data Structure
+
+* **Language**: French (`fr`)
+* **Fields**:
+
+  * `text` Article content.
+  * `id`: ID of the article.
+  * `url`: URL of the article.
+  * `date`: Date of the article.
+  * `file_path`: Reference of the original file in wiki namespace
+  * `description`: One-sentence description of the article for quick reference.
+
+## Source and Processing
+
+The original data is sourced from the [Wikipedia Structured Contents (Kaggle)](https://www.kaggle.com/datasets/wikimedia-foundation/wikipedia-structured-contents) dataset. It was extracted from HTML snapshots provided by Wikimedia Enterprise, then parsed and cleaned to retain only the most useful and structured textual elements for machine learning.
+
+The dataset has been carefully filtered and deduplicated. The filtering follows the same rules as those applied with [FineWeb2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) using the [Datatrove](https://github.com/huggingface/datatrove) library.
+
+This preprocessing step aims to improve readability, consistency, and structure, helping language models learn more effectively.
+
+## Data Splits
+
+Currently, the dataset is provided as a single `train` split. No predefined validation or test sets are included. Users are encouraged to create their own splits as needed.
+
+## How to Use
+
+You can load FineWiki using the 🤗 `datasets` library like this:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("LeMoussel/finewiki", split="train")
+
+# Example: print the first article
+print(dataset[0])
+```