lumees/turkish-corpus-100b

Lumees Turkish Corpus 100B (LTC-100B)

Dataset Summary

The Lumees Turkish Corpus 100B (LTC-100B) is a massive-scale, deduplicated, and cleaned dataset designed for training Foundation Models in Turkish. Comprising approximately 105 Billion tokens (measured with Qwen/Llama3 tokenizer), it represents one of the largest open resources for Turkish LLM pretraining.

The dataset is engineered for a two-stage training pipeline:

1. Pretrain Subset (~103B Tokens): A diverse mix of high-quality web data, synthetic reasoning, encyclopedic knowledge, and flattened instructions for continual pretraining.
2. SFT Subset (~2.2B Tokens): A massive collection of instruction-following, mathematical reasoning, and translation pairs for "Instruction Pretraining" or large-scale SFT.

🚀 Pilot Subset Available (10B)

For researchers and organizations running single-node experiments (e.g., 1x H100), we provide a 10 Billion Token Pilot Subset. This subset uses weighted priority sampling (keeping 100% of Synthetic/Wiki data and downsampling the Web data) to ensure high density.

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Dataset Statistics

Estimates based on Qwen/Llama-3 Tokenization.

Subset	Format	File Type	Token Count
Pretrain	Universal Schema	Parquet (ZSTD)	~103.26 Billion
SFT	ChatML	JSONL	~2.27 Billion
Total	-	-	~105.53 Billion

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Data Structure

1. Pretraining Subset (pretrain)

Optimized for high-throughput streaming with libraries like datatrove or nanotron.

Column	Type	Description
id	string	Unique UUIDv4 (Vital for deduplication tracking).
text	string	The cleaned, deduplicated content.
source	string	Origin dataset (e.g., fineweb-2, cosmos).
language	string	ISO Code (tr).
meta	string	Original metadata (URL, date, title) serialized as JSON string.

2. SFT Subset (sft)

Optimized for "Instruction Pretraining" or Fine-Tuning.

Column	Type	Description
messages	list	Standard OpenAI format: [{"role": "user", ...}, {"role": "assistant", ...}]
source	string	Origin task (e.g., instruc_turca, open_math).

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Data Composition

This corpus was built using a Weighted Priority strategy, blending massive web scale with high-density reasoning data.

Source	Type	Usage Phase	Description
FineWeb-2 (Turkish)	Web Crawl	Pretrain	The backbone of the corpus (cleaned web text).
Cosmos Synthetic	Synthetic	Pretrain	Textbook-quality reasoning and explanations.
FineWiki TR	Knowledge	Pretrain	Full Turkish Wikipedia dump.
Turkish News	Formal Text	Pretrain	High-quality, grammatically correct news articles.
Instruc Turca (90%)	Instructions	Pretrain	Flattened instruction pairs (User/Assistant) treated as raw text.
Instruc Turca (10%)	Chat	SFT	High-quality conversational data.
Open Math TR	Reasoning	SFT	Step-by-step mathematical problem solving.
XP3X	NLP Tasks	SFT	Multilingual generalization tasks.
En-Tr Translation	Translation	SFT	Parallel translation pairs.

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Processing Pipeline

This dataset was engineered for Foundation Model training standards:

1. Normalization: All 60+ raw data sources were mapped to a single id, text, source, meta schema.
2. Disk-Based Deduplication: Exact deduplication (MD5) was performed across the entire ~100M document collection to reduce training flop waste.
3. PII Sanitization:
   • Regex Cleaning: Automated removal of Email addresses, IP addresses, and Turkish phone numbers (+90...).
   • Note: Synthetic sources (Cosmos) and FineWeb were excluded from aggressive regexing to preserve token distribution.
4. Sharding: Data is split into 2.0 GB Parquet shards for optimal GPU cluster streaming.

Limitations

• Web Bias: A significant portion of the data (FineWeb) comes from the open internet and may reflect societal biases.
• Synthetic Nature: The Cosmos subset is synthetic; while high quality, it may contain hallucinated reasoning patterns common to LLM outputs.

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Citation & Attribution

If you use this dataset in your research or product, please cite:

@misc{lumees2025turkish100b,
  author = {Hasan KURŞUN, Kerem Berkay YANIK},
  title = {Lumees Turkish Corpus 100B},
  year = {2025},
  publisher = {Lumees AI},
  howpublished = {\url{[https://lumees.io](https://lumees.io)}},
  email = {hello@lumees.io}
}