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---
tags:
- mizo
- monolingual
- nlp
- open-source
- text-corpus
- tibeto-burman
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 MWireLabs, 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
- **Total Tokens**: ~93,000,000
- **Average Tokens per Sentence**: 23.3
- **File Size**: ~394MB
- **Language**: Mizo (Tibeto-Burman)
- **Source Corpus Size**: 5,944,581 sentences (138.7M tokens)
## License
This dataset is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license, requiring attribution to MWireLabs and the original sources. Users must cite this dataset and its contributors in any derived work.
## Attribution
- **Curator**: MWireLabs, Meghalaya, India
- **Contact**: Through HuggingFace
- **Sources**:
- IndicNLCorp (WMT-23 IndicNECorp1.0) for a significant portion of the raw text.
- Hugging Face datasets, including `HuggingFaceFW/fineweb`.
- [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:
```bibtex
@misc{mizoCorpus4M2025,
title={Mizo-Language-Corpus-4M},
author={MWireLabs},
year={2025},
publisher={HuggingFace},
howpublished={\url{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 Use the Dataset
### 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:
```bash
pip install huggingface_hub
```
2. Log in:
```bash
huggingface-cli login
```
3. Download the file:
```bash
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
- Text classification
- Machine translation
- Other NLP tasks
It supports academic research, educational tools, and regional language technology development.
## Related Resources
- **Model trained on this data**: [Mizo-RoBERTa](https://huggingface.co/MWireLabs/Mizo-RoBERTa) - RoBERTa-base model for Mizo language
- **Organization**: [MWireLabs on HuggingFace](https://huggingface.co/MWireLabs)
## Limitations
- Monolingual focus may limit bilingual applications.
- Some cultural nuances might be underrepresented due to cleaning filters.
- Dataset represents formal and written Mizo more than conversational language.
## Contributions
We welcome feedback, improvements, and additional data contributions. Please contact us through the HuggingFace community discussions or issues section.
---
**MWireLabs** - Building AI for Northeast India 🚀 |