Update README.md

README.md

@@ -1,20 +1,34 @@
 ---
+language:
+- multilingual
 task_categories:
 - text-classification
+tags:
+- language-identification
+- lid
 ---
 
 # Ensemble Additional Data
 
-This dataset contains cleaned text data organized by language, can be used as a benchmark for evaluating language identification (LID) models.
+This dataset contains cleaned text data organized by language, and can be used as a benchmark for evaluating language identification (LID) models.
 
-This dataset consists of samples from multiple existing benchmark dataset and multilingual text dataset. The full list is provided [here](https://docs.google.com/spreadsheets/d/1G12FaSMelNX87dclhm9dE3d5ZRO99B2hoFE1ufB2Zvg/edit?usp=sharing)
-- **Languages:** \~ 1800 languages.
-- **Licenses:** The full list of licenses is provided in the sheet, all of them can be used for commercial purposes
+## Overview
+
+This dataset consists of samples from multiple existing benchmark and multilingual text datasets. The full list of sources and licenses is provided [here](https://docs.google.com/spreadsheets/d/1G12FaSMelNX87dclhm9dE3d5ZRO99B2hoFE1ufB2Zvg/edit?usp=sharing).
+
+- **Languages:** ~1,800 languages
+- **Samples:** Over 1M
+- **Licenses:** All sources are commercially usable — full license list provided in the sheet above
 
 ## Dataset Structure
+
 - Each parquet file corresponds to one language (e.g. `eng.parquet`, `fra.parquet`)
 - Columns: `text`, `language`
-- Samples: over 1M
 
-The dataset is extracted from sources, carefully deduplicate, remove programming languages, abnormal symbols, numbers, check for semantic duplication with [SemHash](https://github.com/MinishLab/semhash), remove mislabeling with [CleanLab](https://github.com/cleanlab/cleanlab)
+## Data Cleaning Pipeline
 
+The dataset was carefully processed through the following steps:
+1. **Deduplication** — exact duplicate removal across all sources
+2. **Filtering** — removal of programming language code, abnormal symbols, and numeric-only samples
+3. **Semantic deduplication** — near-duplicate removal using [SemHash](https://github.com/MinishLab/semhash)
+4. **Mislabel correction** — noisy label detection and removal using [CleanLab](https://github.com/cleanlab/cleanlab)