Minuri/diverse_sinhala_dataset
Diverse Sinhala Dataset A large-scale, cleaned, deduplicated, and domain-classified Sinhala text corpus compiled for continual pretraining of large language models. Constructed as part of a diversity-driven Sinhala language model adaptation study. This repository serves as pipeline storage for the full corpus construction process, from merged cleaned sentences through to the final high-confidence domain-classified corpus. Files File Rows Columns Description… See the full description on the dataset page: https://huggingface.co/datasets/Minuri/diverse_sinhala_dataset.
Diverse Sinhala Dataset
A large-scale, cleaned, deduplicated, and domain-classified Sinhala text corpus compiled for continual pretraining of large language models. Constructed as part of a diversity-driven Sinhala language model adaptation study.
This repository serves as pipeline storage for the full corpus construction process, from merged cleaned sentences through to the final high-confidence domain-classified corpus.
Files
Pipeline
4 cleaned source datasets
↓
Exact deduplication (cross-source)
↓ exact_deduplicated_corpus.csv (12.65M)
MinHash LSH near-deduplication
↓ near_deduplicated_corpus.csv (12.38M)
XLM-RoBERTa domain classification (8 domains, 94% macro-F1)
↓ full_dataset_classified.csv (12.38M)
High-confidence filter
↓ high_confidence_7_7M.csv (7.70M) ← final usable corpusSource Datasets
Domains (8 classes)
Classified using a fine-tuned XLM-RoBERTa model (94% macro-F1): News, Education, Government / Legal, Health, Religion / Culture, Sports, Entertainment, General / Other
Usage
import pandas as pd
from huggingface_hub import hf_hub_download
# Load the final high-confidence corpus
path = hf_hub_download(
repo_id="Minuri/diverse_sinhala_dataset",
filename="high_confidence_7_7M.csv",
repo_type="dataset"
)
df = pd.read_csv(path)
print(df.head())Downstream Datasets
This corpus was used to sample the following pretraining corpora:
Sources & Licenses
This dataset is released under CC BY-SA 4.0 in compliance with the ShareAlike terms of Wikipedia and NSINA.
