---
language:
- bg
license: apache-2.0
task_categories:
- text-generation
- question-answering
- translation
pretty_name: Bulgarian Corpus 33B
size_categories:
- 10B<n<100B
tags:
- bulgarian
- llm
- foundation-model
- pretraining
- sft
- fineweb
- science
configs:
- config_name: pretrain
  data_files: "pretrain/*.parquet"
- config_name: sft
  data_files: "sft/*.parquet"
---

# Lumees Bulgarian Corpus (BG-Corpus-33B)

## Dataset Summary
The **Bulgarian Corpus 33B** is a massive-scale, deduplicated, and cleaned dataset designed for training Foundation Models in Bulgarian. Comprising approximately **33.4 Billion tokens** (measured with Qwen 2.5/Llama-3 tokenizer), it represents one of the largest open-source resources for Bulgarian LLM pretraining.

The dataset is engineered for a modern two-stage training pipeline:
1.  **Pretrain Subset (~29.3B Tokens):** A diverse mix of high-quality web data, encyclopedic knowledge, and scientific abstracts.
2.  **SFT Subset (~4.1B Tokens):** A curated collection of instruction-following, chat, and multitask data, strictly filtered to remove alignment artifacts.

**Training Recommendation:** With ~33B unique high-quality tokens, we recommend training for **3 Epochs** over the pretrain subset to achieve optimal convergence for models in the 7B-8B parameter range (effectively ~90B training tokens).

---

## Dataset Statistics
*Estimates based on Qwen 2.5 / Llama-3 Tokenization.*

| Subset | Format | File Type | Documents | Token Count |
| :--- | :--- | :--- | :--- | :--- |
| **Pretrain** | Universal Schema | Parquet (Snappy) | 26,278,393 | **~29.31 Billion** |
| **SFT** | ChatML | Parquet (Snappy) | 8,663,195 | **~4.11 Billion** |
| **Total** | - | - | **34,941,588** | **~33.42 Billion** |

---

## Data Structure

### 1. Pretraining Subset (`pretrain`)
Optimized for high-throughput streaming with libraries like `datatrove`, `nanotron`, or `torchtune`.

| Column | Type | Description |
| :--- | :--- | :--- |
| `id` | `string` | Unique identifier (vital for tracking). |
| `text` | `string` | The cleaned, deduplicated content. |
| `source` | `string` | Origin dataset (e.g., `fineweb-2`, `bpos_science`). |
| `language` | `string` | ISO Code (`bg`). |
| `meta` | `string` | Original metadata (URL, date, title, DOI) serialized as a JSON string. |

### 2. SFT Subset (`sft`)
Optimized for "Instruction Pretraining" or Fine-Tuning (Axolotl/LLaMA-Factory compatible).

| Column | Type | Description |
| :--- | :--- | :--- |
| `messages` | `list` | Standard OpenAI/ChatML format: `[{"role": "user", ...}, {"role": "assistant", ...}]` |
| `source` | `string` | Origin task (e.g., `aya_collection`, `xp3x`). |

---

## Data Composition

This corpus was built using a **Quality-First** strategy, blending massive web scale with high-density scientific and encyclopedic data.

| Source | Type | Usage Phase | Description |
| :--- | :--- | :--- | :--- |
| **[FineWeb-2 (Bulgarian)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2)** | Web Crawl | Pretrain | The backbone of the corpus (cleaned web text). |
| **[FineWiki BG](https://huggingface.co/datasets/HuggingFaceFW/finewiki)** | Knowledge | Pretrain | Full Bulgarian Wikipedia dump with rich metadata. |
| **[BPOS (Open Science)](https://bpos.bg)** | Scientific | Pretrain | **4,700+** Titles and Abstracts from the Bulgarian Portal for Open Science (High density domain knowledge). |
| **[Aya Collection](https://huggingface.co/datasets/CohereLabs/aya_collection)** | Instruction | SFT | High-quality multilingual instruction following. |
| **[xP3x](https://huggingface.co/datasets/CohereLabs/xP3x)** | NLP Tasks | SFT | Massive multitask dataset (Filtered for quality). |
| **[Alpaca Dictionary BG](https://huggingface.co/datasets/vislupus/alpaca-bulgarian-dictionary)** | Linguistic | SFT | Definitions, synonyms, and linguistic tasks. |

---

## Processing Pipeline

This dataset was engineered for **Foundation Model** training standards:

1.  **Normalization:** Multiple raw data sources were mapped to a single unified schema.
2.  **PII Sanitization:**
      * **Regex Cleaning:** Automated removal of Email addresses, IPv4 addresses, and **Bulgarian phone numbers** (e.g., `+359...`, `088...`).
3.  **DB-Assisted Deduplication:**
      * Exact deduplication (MD5 hashing) was performed across the entire collection.
      * **Priority Strategy:** High-quality sources (Wiki/Science) were processed first to claim ownership of duplicate text, ensuring the highest quality version is kept.
4.  **Quality Filtering (SFT):**
      * The SFT subset was scrubbed of "poison" rows (e.g., where the assistant replies "None", "null", or refuses to answer due to alignment errors).
5.  **Sharding:** Data is split into `~200k row` Parquet shards for optimal download and streaming speeds.

## Limitations

  * **Web Bias:** A significant portion of the data (FineWeb) comes from the open internet and may reflect societal biases found in Bulgarian web content.
  * **Translation Artifacts:** Some SFT data is machine-translated or aligned; while we filtered obvious errors, some translation artifacts may remain.

-----

## Citation & Attribution

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

```bibtex
@misc{bulgariancorpus33b,
  author = {Hasan KURŞUN, Kerem Berkay YANIK},
  publisher = {Lumees AI},
  title = {Bulgarian Corpus 33B},
  year = {2025},
  publisher = {HuggingFace Community},
  howpublished = {\url{[https://lumees.io](https://lumees.io)}},
  email = {hello@lumees.io}
}
```