Update README.md

README.md

@@ -1,3 +1,92 @@
 ---
+tags:
+  - mizo
+  - monolingual
+  - nlp
+  - open-source
+  - text-corpus
 license: cc-by-4.0
----
+dataset_info:
+  features:
+    - name: text
+      dtype: string
+  splits:
+    - name: train
+      num_examples: 4000000
+  size_in_bytes: 394000000
+---
+
+# Mizo-Language-Corpus-4M
+
+## Description
+The Mizo-Language-Corpus-4M is an open-source monolingual Mizo dataset containing 4 million sentences, curated by MWire Labs, Meghalaya, India. Derived from a larger 5.94 million-sentence corpus, this dataset has been meticulously processed to support natural language processing (NLP) research, promote linguistic equity, and foster open development in low-resource languages.
+
+## Dataset Statistics
+- **Total Sentences**: 4,000,000
+- **File Size**: ~394MB
+- **Language**: Mizo (Mon-Khmer, Austroasiatic)
+- **Source Corpus Size**: 5,944,581 sentences
+
+## License
+This dataset is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license, requiring attribution to MWire Labs and the original sources. Users must cite this dataset and its contributors in any derived work.
+
+## Attribution
+- **Curator**: MWire Labs, Meghalaya, India
+- **Contact**: [email protected]
+- **Sources**:
+  - IndicNLCorp (WMT-23 IndicNECorp1.0) for a significant portion of the raw text.
+  - Hugging Face datasets, including `HuggingFaceFW/finepdfs`.
+  - [https://dipr.mizoram.gov.in](https://dipr.mizoram.gov.in) (Directorate of Information & Public Relations, Government of Mizoram, India) for additional press release data.
+
+## Citation
+Please cite this dataset as:  
+MWire Labs. (2025). *Mizo-Language-Corpus-4M*. https://huggingface.co/datasets/MWireLabs/mizo-language-corpus-4M  
+Additionally, cite the original sources: IndicNLCorp (WMT-23), Hugging Face dataset contributors, and the Directorate of Information & Public Relations, Government of Mizoram.
+
+## Processing Details
+- **Deduplication**: Removed exact duplicate lines across the corpus using a set-based approach to ensure uniqueness.
+- **Cleaning**:
+  - Filtered noise (e.g., URLs, special characters beyond basic punctuation) with regular expressions.
+  - Split long paragraphs into sentences using punctuation boundaries (., !, ?).
+  - Removed lines with 3+ repeated characters (e.g., XXX, !!!) and those with 4 or fewer words.
+  - Eliminated lines with more than 4 digits to reduce noise.
+  - Targeted removal of garbled text (e.g., non-ASCII dense lines) and web artifacts (e.g., Photobucket, blogspot references).
+- **Validation**: Random sampling and manual review confirmed data quality.
+
+## How to Pull the Dataset
+To access the Mizo-Language-Corpus-4M, use the following methods:
+
+### Using the `datasets` Library
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset(
+    "MWireLabs/mizo-language-corpus-4M",
+    data_files="mizo-language-corpus-4m.txt",
+    split="train"
+)
+
+# Verify the data
+print(f"Total examples: {len(dataset)}")
+print(f"\nFirst 3 examples:")
+for i in range(min(3, len(dataset))):
+    print(dataset[i])
+print(f"\nDataset features: {dataset.features}")
+
+Using the Hugging Face Hub CLI
+
+1. Install the Hugging Face CLI: pip install huggingface_hub
+2. Log in: huggingface-cli login
+3. Download the file: huggingface-cli download MWireLabs/mizo-language-corpus-4M mizo-language-corpus-4m.txt --local-dir ./
+
+
+## Intended Use
+This corpus is intended for training and evaluating Mizo language models, including masked language modeling, sentiment analysis, named entity recognition, and other NLP tasks. It supports academic research, educational tools, and regional language technology development.
+
+## Limitations
+- Monolingual focus may limit bilingual applications.
+- Some cultural nuances might be underrepresented due to cleaning filters.
+
+## Contributions
+We welcome feedback, improvements, and additional data contributions. Contact [email protected] to collaborate.