saidutta69/odia_pretrain_dataset_v2

Odia Pretrain Dataset v2

12.3 million clean Odia documents. 30+ sources. Zero noise. Zero gating. Just data that actually works for pretraining.

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The Origin Story (a.k.a. The Time We Filtered 98% of a Clean Dataset and Learned Our Lesson)

v1 was built from spite. v2 was built from more data.

We took v1 (25+ sources, 10.3M rows) as the foundation and asked: what else can we add?

• monsoon-nlp/odia-cleaned -- fully deduplicated against v1 (0 new rows)
• Wikipedia Odia -- 641 unique articles not already in v1
• IndicCorpV2 Odia -- the heavyweight addition: 2 million unique rows of Odia text from AI4Bharat's collection

After deduplicating across all sources, we landed at 12.3M unique rows -- a 19% increase over v1 with minimal quality compromise.

The result is v2: v1's diversity plus everything new we could find, with nothing duplicated.

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What's In This Beast

Metric	Value
Total Rows	12,291,203
Data Sources	30+ (v1's 25+ sources + monsoon-nlp + Wikipedia Odia + IndicCorpV2)
License	CC-BY-4.0 (commercially usable)
Access Required	None
File Format	Parquet (32 shards)

Source Breakdown

Source	Rows	What It Is
v1 (25+ sources)	10,290,562	News, web crawl, IndicNLP, Varta, Samanantar, xp3, OdiaQA, Wikipedia, Alpaca, and more
monsoon-nlp/odia-cleaned	0 (100% deduped)	Fully overlapped with v1
Wikipedia (Odia)	641	Odia Wikipedia articles not already in v1
IndicCorpV2 (Odia)	2,000,000	Odia subset of AI4Bharat IndicCorpV2

Data Schema

Field	Type	Description
text	string	Clean Odia text
source	string	monsoon-nlp, wikipedia, or indiccorp_v2
type	string	Always "pretrain"
char_count	int64	Character length
en_aligned	bool	English alignment available

Quick Start

from datasets import load_dataset

# Load. No forms. No gatekeeping. Just data.
ds = load_dataset("saidutta69/odia_pretrain_dataset_v2", split="train")
print(f"Rows: {len(ds)}")

# Filter by source
monsoon = ds.filter(lambda x: x["source"] == "monsoon-nlp")
wikipedia = ds.filter(lambda x: x["source"] == "wikipedia")
indic = ds.filter(lambda x: x["source"] == "indiccorp_v2")

# Character length distribution
import statistics
lengths = [x["char_count"] for x in ds]
print(f"Mean: {statistics.mean(lengths):.0f}, Median: {statistics.median(lengths):.0f}")

License

CC-BY-4.0 -- Use it commercially. Use it for research. Train your Odia model. We don't gate things here.

Acknowledgments

• monsoon-nlp for the original cleaned Odia corpus that did the hard work before we touched it
• The person who called us out on the 98% filtration -- this dataset wouldn't exist without that reality check
• Odia Wikipedia contributors for keeping the language alive online
• AI4Bharat for IndicCorpV2
• GCP VMs that got absolutely wrecked by 10M-row processing loops and kept coming back for more

Citation

@misc{odia_pretrain_dataset_v2,
  author = {Sai Dutta Abhishek Dash},
  title = {Odia Pretrain Dataset v2: The One That Keeps Its Promises},
  year = {2026},
  publisher = {Hugging Face},
  note = {10.3M rows of clean Odia text, minimal filtering, no gatekeeping},
  howpublished = {\url{https://huggingface.co/datasets/saidutta69/odia_pretrain_dataset_v2}},
}

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"We filtered 98%. You told us we were wrong. We fixed it. This one's for you."