---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: title
    dtype: string
  - name: date
    dtype: 'null'
  - name: url
    dtype: string
  - name: source
    dtype: string
  - name: language
    dtype: string
  - name: domain
    dtype: string
  - name: id
    dtype: string
  splits:
  - name: train
    num_bytes: 2140231858
    num_examples: 512948
  - name: validation
    num_bytes: 265884337
    num_examples: 64116
  - name: test
    num_bytes: 268635760
    num_examples: 64122
  download_size: 1365286406
  dataset_size: 2674751955
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
license: cc-by-4.0
language:
- sr
- bs
- hr
pretty_name: SR/BS/HR Clean Text Corpus
size_categories:
- 100K<n<1M
---

# SR/BS/HR Clean Text Corpus

<div align="center">

![Languages](https://img.shields.io/badge/languages-sr%20|%20bs%20|%20hr-blue)
![License](https://img.shields.io/badge/license-CC--BY--SA--4.0-green)
![Size](https://img.shields.io/badge/examples-641K-orange)

**High-quality, deduplicated text corpus for Serbian, Bosnian, and Croatian**

[RSA Team](https://huggingface.co/rsateam) • [GitHub](https://github.com/rsadevteam) • [Website](https://rsateam.com)

</div>

## Overview

This dataset provides a carefully curated and cleaned text corpus for South Slavic languages, specifically designed to address quality issues found in existing corpora like OSCAR and CC100. It serves as a foundation for training language models, tokenizers, and conducting linguistic research on Balkan languages.

### Why This Dataset?

Existing sr/bs/hr corpora often suffer from:

| Problem | Our Solution |
|---------|--------------|
| HTML fragments & noise | Aggressive cleaning pipeline |
| Poor deduplication | SHA256 + MinHash (>95% dedup rate) |
| Mixed languages | Source-based labeling + FastText validation |
| Unclear sources | Full provenance tracking |

## Dataset Statistics

| Metric | Value |
|--------|-------|
| Total examples | **641,186** |
| Dataset size | 2.67 GB |
| Download size | 1.37 GB |
| Source | Wikipedia |

### Splits

| Split | Examples | Size |
|-------|----------|------|
| `train` | 512,948 | 2.14 GB |
| `validation` | 64,116 | 266 MB |
| `test` | 64,122 | 269 MB |

### Languages

| Language | ISO Code | Source |
|----------|----------|--------|
| Serbian | `sr` | sr.wikipedia.org |
| Bosnian | `bs` | bs.wikipedia.org |
| Croatian | `hr` | hr.wikipedia.org |

## Dataset Structure

### Data Format

```json
{
  "id": "550e8400-e29b-41d4-a716-446655440000",
  "title": "Article Title",
  "text": "Full cleaned article text...",
  "language": "sr",
  "source": "sr.wikipedia.org",
  "domain": "wiki",
  "date": null,
  "url": "https://sr.wikipedia.org/wiki/..."
}
```

### Fields

| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Unique identifier |
| `title` | string | Article title |
| `text` | string | Cleaned textual content |
| `language` | string | Language code (sr/bs/hr) |
| `source` | string | Source domain |
| `domain` | string | Content type (wiki) |
| `date` | null | Publication date (not available for wiki) |
| `url` | string | Original Wikipedia URL |

## Data Processing Pipeline

```
┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│  Collection │ -> │  Cleaning   │ -> │   Dedup     │ -> │  Lang ID    │ -> │  Filtering  │
│  Wikipedia  │    │  normalize  │    │  MinHash    │    │  FastText   │    │  quality    │
└─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘
```

### Processing Steps

1. **Collection**: Wikipedia dumps from bs/hr/sr Wikipedia
2. **Cleaning**: Markup removal, Unicode normalization (NFC), whitespace normalization
3. **Deduplication**: SHA256 exact matching + MinHash near-duplicate detection (90% threshold)
4. **Language ID**: Source-based labeling with FastText validation
5. **Quality Filtering**: Length constraints, language confidence >0.90

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load full dataset
dataset = load_dataset("rsateam/sr-bs-hr-clean-text")

# Load specific split
train = load_dataset("rsateam/sr-bs-hr-clean-text", split="train")

# Filter by language
serbian = dataset["train"].filter(lambda x: x["language"] == "sr")
```

### Streaming

```python
from datasets import load_dataset

dataset = load_dataset(
    "rsateam/sr-bs-hr-clean-text",
    split="train",
    streaming=True
)

for example in dataset:
    print(example["title"], "-", example["text"][:100])
```

### Training a Tokenizer

```python
from tokenizers import Tokenizer
from tokenizers.models import BPE
from tokenizers.trainers import BpeTrainer
from datasets import load_dataset

dataset = load_dataset("rsateam/sr-bs-hr-clean-text", split="train")

def batch_iterator(batch_size=1000):
    for i in range(0, len(dataset), batch_size):
        yield dataset[i:i+batch_size]["text"]

tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
trainer = BpeTrainer(
    vocab_size=32000,
    special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]
)
tokenizer.train_from_iterator(batch_iterator(), trainer=trainer)
```

## Supported Tasks

- **Language Model Pretraining**: Foundation for training or continued pretraining of LLMs
- **Tokenizer Training**: Clean text for BPE/WordPiece/Unigram tokenizer training
- **Word Embeddings**: Training Word2Vec, FastText, or similar embeddings
- **Linguistic Research**: Analysis of Serbian, Bosnian, and Croatian texts

## Considerations

### Ethical Considerations

- Data sourced from Wikipedia under CC-BY-SA license
- No personally identifiable information (PII)
- Encyclopedic content with neutral point of view

### Limitations

- Single source (Wikipedia) — encyclopedic style only
- Some topics may be underrepresented
- Article length varies significantly

### License

This dataset is released under [CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/), consistent with Wikipedia's licensing.

## Citation

```bibtex
@dataset{rsateam_clean_text_2026,
  title={SR/BS/HR Clean Text Corpus},
  author={RSA Team},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/rsateam/sr-bs-hr-clean-text},
  note={High-quality deduplicated corpus for Serbian, Bosnian, and Croatian}
}
```

## Future Plans

We plan to expand this dataset with additional sources:

- News portals (klix.ba, index.hr, blic.rs, etc.)
- Government and public institution documents
- Other curated text sources

## Contributing

We welcome contributions! For suggestions, bug reports, or improvements:

- Open an issue on [GitHub](https://github.com/rsadevteam/balkan-nlp)
- Email: office@rsateam.com

---

<div align="center">

**[RSA Team](https://huggingface.co/rsateam)** — *Building bridges between languages and AI, one dataset at a time.*

</div>