README.md

---
dataset_info:
  features:
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 1784778472
    num_examples: 2005712
  download_size: 1106679567
  dataset_size: 1784778472
tags:
- turkish
- pretraining
- masked-language-modeling
- diffusion
- wikipedia
- oscar
- news
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- text-generation
language:
- tr
---

# DiffutronLM-Pretraining-Corpus

**DiffutronLM-Pretraining-Corpus** is the comprehensive, filtered Turkish text dataset used during the Continual Pre-training (CPT) phase of the [Diffutron](https://huggingface.co/collections/diffutron/diffutronlm) language models. 

The primary goal of this dataset was to align the cross-lingual representations of a multilingual base encoder (`jhu-clsp/mmBERT-base`) with the agglutinative complexity and morphological nuances of the Turkish language, without inducing catastrophic forgetting.

## 📊 Dataset Composition

To ensure a balance between structured encyclopedic knowledge and natural, diverse web/news usage, the corpus is a composite of three primary open-source collections. It contains a total of **approximately 2 million sequences**.

* **Turkish Wikipedia (~406,000 sequences):** Sourced from the standard encyclopedic subset from the Wikimedia Foundation. It provides high-quality, factual, and structurally sound Turkish text.
* **Havadis & Temiz-OSCAR (~1,600,000 sequences):** * *Havadis:* A robust dataset of Turkish news articles providing formal and contemporary language usage.
  * *Temiz-OSCAR:* A heavily filtered and cleaned version of the Common Crawl-based Turkish OSCAR corpus, representing diverse internet text.
  * These two sources were merged, filtered, and uniformly sampled to extract 1.6 million high-quality sequences.

## ⚙️ Preprocessing & Curation Strategy

The data was strictly curated to match the architectural constraints of the base Masked Diffusion Language Model (MDLM):

1. **Length Filtering:** To ensure compatibility and training stability, a strict length constraint was applied across all data sources. Any sequences exceeding a **maximum token length of 512** were filtered out.
2. **Tokenization Alignment:** The text was tokenized using the `jhu-clsp/mmBERT-base` tokenizer. This was a crucial step to maintain absolute alignment with the pre-trained embedding space of the frozen backbone.
3. **Shuffling & Distribution:** The web and news subsets were thoroughly shuffled prior to sampling to ensure distributional uniformity during the training process.

## 🚀 Intended Use

This corpus is optimized for:
* **Continual Pre-Training (CPT):** Adapting existing multilingual or general-purpose encoders to the Turkish language.
* **Masked Language Modeling (MLM):** Training models to predict masked or corrupted tokens (the foundational mechanism of discrete diffusion models).
* **Domain Adaptation:** Serving as a baseline corpus for general Turkish language modeling before task-specific instruction tuning.

## ⚠️ Limitations

* **Length Constraint:** The dataset inherently lacks long-form document structures, as all sequences are hard-capped at 512 tokens. It is not suitable for training long-context models without additional data.
* **Tokenization:** While provided as text, researchers should be aware that the length filters were applied based on the specific subword tokenization of `mmBERT`. Re-tokenizing with a different tokenizer (like LLaMA's or a custom BPE) may yield different sequence lengths.

## 📝 Citation

If you use this dataset in your research, please cite the Diffutron paper:

```bibtex
@misc{diffutron2026,
      title={Diffutron: A Masked Diffusion Language Model for Turkish Language}, 
      author={Şuayp Talha Kocabay and Talha Rüzgar Akkuş},
      year={2026},
      eprint={2603.20466},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.20466}, 
}
```