OmAlve/vaarta-cpt-dataset

Vaarta CPT Dataset

Multilingual continued pretraining corpus used to train the Vaarta family of Marathi-first language models. Contains ~320K documents across 6 sources and 3 scripts (Devanagari, Roman/Latin, English).

Dataset Composition

Source	Language	Script	Size	Description
marathi_wikipedia	Marathi	Devanagari	~90K	Full Marathi Wikipedia dump
sangraha_verified	Marathi	Devanagari	80K	Curated high-quality Marathi web text
sangraha_synthetic_deva	Marathi	Devanagari	50K	Synthetic Marathi Devanagari text
sangraha_synthetic_roman	Marathi	Latin/Roman	30K	Romanized Marathi (e.g. "shivaji maharaj")
english_wikipedia	English	Latin	50K	English Wikipedia for multilingual capability
hindi_wikipedia	Hindi	Devanagari	20K	Hindi Wikipedia — shares Devanagari with Marathi

Total: ~320K documents, shuffled

Schema

{
  "text":     str,   # document content (capped at 8K chars)
  "source":   str,   # one of the 6 source keys above
  "language": str,   # "mr", "mr_roman", "en", "hi"
}

Usage

from datasets import load_dataset

ds = load_dataset("OmAlve/vaarta-cpt-dataset", split="train")

# Filter by language
marathi_only  = ds.filter(lambda x: x["language"] == "mr")
roman_marathi = ds.filter(lambda x: x["language"] == "mr_roman")
english_only  = ds.filter(lambda x: x["language"] == "en")

# Filter by source
wikipedia_mr  = ds.filter(lambda x: x["source"] == "marathi_wikipedia")

Why This Corpus?

Standard LLM pretraining on English-dominant corpora results in poor Marathi performance. This corpus makes Marathi the dominant language (~78% of documents) while retaining English and Hindi to prevent catastrophic forgetting of multilingual capability.

Including mr_roman (Romanized Marathi) is critical — many Marathi speakers type in Roman script (e.g. "shivaji maharaj kon hote?"), and without pretraining exposure, models fail to understand or generate it.

Training Context

This dataset was used for Stage 1 (CPT) of the Vaarta v2 training pipeline:

• Stage 1 — CPT: 6000 steps, batch=8×accum=2, seq_len=1024, LR=2e-4 cosine
• Stage 2 — SFT: ~178K instruction examples (see OmAlve/vaarta-sft-dataset)
• Base model: meta-llama/Llama-3.2-3B
• Final model: OmAlve/vaarta-llama-v2

Source Licenses

Original licenses apply per source:

• Wikipedia (all languages): CC BY-SA 4.0
• Sangraha: CC0 / CC BY — see ai4bharat/sangraha