---
language:
- en
- bg
license: cc-by-4.0
size_categories:
- 10M<n<100M
task_categories:
- translation
- text-generation
task_ids:
- text2text-generation
tags:
- subtitles
- opensubtitles
- gemma3
- gemma-270m
- fine-tuning
- machine-translation
- instruction-following
- unsloth
- unsloth-compatible
- chess-instruct-format
- large-scale
- 50M
pretty_name: Bulgarian-English OpenSubtitles Full Dataset (50M, ChessInstruct Format)
dataset_info:
  features:
  - name: task
    dtype: string
  - name: input
    dtype: string
  - name: expected_output
    dtype: string
  - name: KIND
    dtype: string
  config_name: default
  splits:
  - name: train
    num_examples: 38999955
  - name: validation
    num_examples: 4874994
  - name: test
    num_examples: 4874995
---

# Bulgarian-English OpenSubtitles Full Dataset (50M, ChessInstruct Format)

## 🎯 Overview

This dataset contains **48,749,944** English to Bulgarian subtitle translation pairs in **ChessInstruct format** for fine-tuning **Gemma3-270m** using the **Unsloth framework**. This represents the complete OpenSubtitles parallel corpus for the BG-EN language pair, making it one of the largest translation datasets available.

## ✨ Key Features

- **📊 Massive Scale**: 48.7 million translation pairs from complete OpenSubtitles corpus
- **✅ ChessInstruct Compatible**: Uses exact `task/input/expected_output/KIND` format
- **🚀 Gemma3-270m Optimized**: Proven format that works with Unsloth
- **🎬 Real-world Data**: Complete coverage of subtitle translation contexts
- **🔄 Instruction Format**: Ready for instruction-following fine-tuning
- **⚡ Zero Errors**: Format guaranteed to work with Unsloth templates
- **🌍 Comprehensive**: Covers diverse domains, genres, and linguistic patterns

## 📊 Dataset Statistics

| Split | Examples | Percentage | Size |
|-------|----------|------------|------|
| Train | 38,999,955 | 80% | ~13.7GB |
| Validation | 4,874,994 | 10% | ~1.4GB |
| Test | 4,874,995 | 10% | ~1.4GB |
| **Total** | **48,749,944** | **100%** | **~16.5GB** |

## 🔧 Data Format

Each example follows the proven ChessInstruct format structure:

```json
{
  "task": "Translate the following English text to Bulgarian:\n\nEnglish: Hello, how are you?\nBulgarian:",
  "input": "Hello, how are you?",
  "expected_output": "Здравей, как си?",
  "KIND": "TRANSLATION_EN_BG"
}
```

## 🚀 Quick Start

### Loading the Dataset (Recommended: Streaming)

```python
from datasets import load_dataset

# For large datasets, use streaming to avoid memory issues
dataset = load_dataset("zantag/en-bg-os-full-50m", streaming=True)

# Access splits
train_data = dataset["train"]
val_data = dataset["validation"] 
test_data = dataset["test"]

# Iterate through streaming data
for i, sample in enumerate(train_data):
    print(f"Sample {i}:")
    print("Task:", sample["task"])
    print("Input:", sample["input"])
    print("Expected Output:", sample["expected_output"])
    print("Kind:", sample["KIND"])
    if i >= 2:  # Show first 3 samples
        break
```

### For Small Subsets (Non-streaming)

```python
# Load a small subset for testing
dataset = load_dataset("zantag/en-bg-os-full-50m", split="train[:1000]")
print(f"Loaded {len(dataset)} examples")
```

### Fine-tuning with Unsloth (Memory Efficient)

```python
from unsloth import FastLanguageModel
from datasets import load_dataset

# Load model with 4-bit quantization for memory efficiency
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/gemma-3-270m-it",
    max_seq_length=2048,
    load_in_4bit=True,  # Essential for large datasets
)

# Use streaming dataset
dataset = load_dataset("zantag/en-bg-os-full-50m", split="train", streaming=True)

# Take a subset for training (adjust size based on your hardware)
dataset = dataset.take(100000)  # Use 100k examples

# Format for training
def formatting_prompts_func(examples):
    tasks = examples["task"]
    expected_outputs = examples["expected_output"]
    texts = []
    
    for task, output in zip(tasks, expected_outputs):
        text = f"<start_of_turn>user\n{task}<end_of_turn>\n<start_of_turn>model\n{output}<end_of_turn>"
        texts.append(text)
    
    return {"text": texts}

# Apply formatting (works with streaming)
dataset = dataset.map(formatting_prompts_func, batched=True)
```

## 🎯 Use Cases

- **Large-scale machine translation research**
- **Training production-quality EN↔BG translation models**
- **Cross-lingual understanding experiments**
- **Domain adaptation studies across entertainment content**
- **Comparative analysis with other translation datasets**
- **Building robust multilingual applications**

## 🔍 Data Quality

- ✅ **Complete OpenSubtitles Corpus**: All available BG-EN parallel data
- ✅ **Quality Filtering**: Cleaned and preprocessed for optimal training
- ✅ **ChessInstruct Format**: 100% compatible with Unsloth framework
- ✅ **Memory Optimized**: Supports streaming for large-scale training
- ✅ **Proper Encoding**: UTF-8 with correct Cyrillic character handling

## 🛠️ Technical Recommendations

### Memory Management

For this large dataset, consider these strategies:

```python
# 1. Use streaming mode
dataset = load_dataset("zantag/en-bg-os-full-50m", streaming=True)

# 2. Process in chunks
def process_in_chunks(dataset, chunk_size=10000):
    chunk = []
    for example in dataset:
        chunk.append(example)
        if len(chunk) >= chunk_size:
            yield chunk
            chunk = []
    if chunk:  # Don't forget the last chunk
        yield chunk

# 3. Use gradient accumulation for training
trainer = Trainer(
    # ... other args
    per_device_train_batch_size=1,  # Small batch size
    gradient_accumulation_steps=32, # Accumulate gradients
    dataloader_pin_memory=False,    # Reduce memory usage
)
```

### Hardware Requirements

**Minimum Requirements:**
- RAM: 32GB+ (for streaming mode)
- GPU: 16GB+ VRAM (with 4-bit quantization)
- Storage: 20GB+ free space

**Recommended Setup:**
- RAM: 64GB+
- GPU: A100 40GB or H100
- Storage: SSD with 50GB+ free space

## 📈 Performance Benchmarks

This dataset provides:
- **Domain Coverage**: Movies, TV shows, documentaries across all genres
- **Linguistic Diversity**: Formal/informal register, technical/colloquial terms
- **Cultural Context**: Real-world usage patterns and expressions
- **Translation Quality**: Human-translated subtitle pairs

## 🤝 Contributing

Found issues or improvements? Please open an issue in the source repository.

## 📄 Citation

```bibtex
@dataset{bg_en_opensubtitles_50m_chess,
  title={Bulgarian-English OpenSubtitles Full Dataset (50M, ChessInstruct Format)},
  author={Zantag},
  year={2024},
  url={https://huggingface.co/datasets/zantag/en-bg-os-full-50m},
  note={Complete OpenSubtitles corpus in ChessInstruct format for Gemma3-270m fine-tuning}
}

@inproceedings{lison-tiedemann-2016-opensubtitles2016,
    title = "{O}pen{S}ubtitles2016: Extracting Large Parallel Corpora from Movie and {TV} Subtitles",
    author = "Lison, Pierre and Tiedemann, J{"o}rg",
    booktitle = "Proceedings of LREC 2016",
    year = "2016",
    url = "https://aclanthology.org/L16-1147",
}
```

## 📋 License

CC-BY-4.0 - Compatible with original OpenSubtitles licensing.

---

**🚀 Ready for large-scale translation model training!**

**⚡ Optimized for memory efficiency and Unsloth compatibility**