---
license: other
language:
- mr
- en
- hi
tags:
- marathi
- pretraining
- cpt
- multilingual
- devanagari
- romanized
size_categories:
- 100K<n<1M
---

# Vaarta CPT Dataset

Multilingual continued pretraining corpus used to train the [Vaarta](https://huggingface.co/OmAlve/vaarta-llama-v2)
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

```python
{
  "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

```python
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`](https://huggingface.co/OmAlve/vaarta-llama-v2)

## Source Licenses

Original licenses apply per source:
- Wikipedia (all languages): [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
- Sangraha: CC0 / CC BY — see [ai4bharat/sangraha](https://huggingface.co/datasets/ai4bharat/sangraha)