| --- |
| 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}, |
| } |
| ``` |
