Update README.md

README.md

@@ -9,9 +9,69 @@ dataset_info:
     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,
+  author = {Kocabay, Şuayp Talha and Akkuş, Talha Rüzgar},
+  title = {Diffutron: A Masked Diffusion Language Model for Turkish Language},
+  year = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{[https://huggingface.co/collections/diffutron/diffutronlm](https://huggingface.co/collections/diffutron/diffutronlm)}}
+}