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README.md

@@ -1,37 +1,93 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: text
-    dtype: string
-  - name: char_length
-    dtype: int32
-  - name: word_count
-    dtype: int32
-  - name: devanagari_ratio
-    dtype: float32
-  - name: source
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 2817272790
-    num_examples: 4736157
-  - name: validation
-    num_bytes: 28924858
-    num_examples: 48328
-  - name: test
-    num_bytes: 28838045
-    num_examples: 48329
-  download_size: 1123917983
-  dataset_size: 2875035693
+language:
+- ne
+license: cc0-1.0
+task_categories:
+- text-generation
+- fill-mask
+task_ids:
+- language-modeling
+pretty_name: CC-100 Nepali (Cleaned)
+size_categories:
+- 1M<n<10M
+tags:
+- nepali
+- devanagari
+- low-resource
+- cc100
+- monolingual
 configs:
 - config_name: default
   data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+    - split: train
+      path: data/train-*.parquet
+    - split: validation
+      path: data/validation-*.parquet
+    - split: test
+      path: data/test-*.parquet
+dataset_info:
+  features:
+    - name: id
+      dtype: string
+    - name: text
+      dtype: string
+    - name: char_length
+      dtype: int32
+    - name: word_count
+      dtype: int32
+    - name: devanagari_ratio
+      dtype: float32
+    - name: source
+      dtype: string
+  splits:
+    - name: train
+      num_examples: 4736157
+    - name: validation
+      num_examples: 48328
+    - name: test
+      num_examples: 48329
 ---
+
+# CC-100 Nepali — Cleaned & Deduplicated
+
+Cleaned, language-filtered, and deduplicated Nepali monolingual text from
+[CC-100](https://data.statmt.org/cc-100/) suitable for transformer pretraining.
+
+## Statistics
+
+| Split | Sentences |
+|---|---|
+| train | 4,736,157 |
+| validation | 48,328 |
+| test | 48,329 |
+| **total** | **4,832,814** |
+
+Created: 2026-04-02
+
+## Pipeline
+
+1. Unicode normalisation (NFC + ftfy)
+2. Rule-based filters (length, Devanagari ratio ≥ 0.5, boilerplate)
+3. Language ID — fastText lid.176.bin, confidence ≥ 0.7
+4. Exact deduplication (MD5)
+5. Near-deduplication (char 13-gram bloom filter)
+6. 98/1/1 train/val/test split, seed 42
+
+## Usage
+
+```python
+from datasets import load_dataset
+ds = load_dataset("Titung/cc100-nepali-cleaned")
+# Filter high-quality sentences
+high_q = ds["train"].filter(lambda x: x["devanagari_ratio"] > 0.8 and x["word_count"] >= 5)
+```
+
+## Citation
+
+```bibtex
+@inproceedings{conneau-etal-2020-unsupervised,
+  title  = {Unsupervised Cross-lingual Representation Learning at Scale},
+  author = {Conneau, Alexis et al.},
+  year   = {2020}
+}
+```