---
license: mit

task_categories:

- text-generation

- fill-mask

language:

- en

  

tags:

- literature

- dostoyevsky

- russian-literature

- author-style

- causal-lm

- fine-tuning
size_categories:
- 1K<n<10K
---

# Dostoyevsky Chunks Dataset

This dataset contains preprocessed text chunks from four major works by Fyodor Dostoyevsky, prepared for fine-tuning language models on the author's distinctive writing style.

## Dataset Description

### Dataset Summary

A curated collection of text segments extracted from public domain English translations of Dostoyevsky's novels, processed into consistent chunks suitable for causal language modeling tasks.

### Supported Tasks

- **Causal Language Modeling**: Primary use case for training autoregressive language models
- **Author Style Transfer**: Learning Dostoyevsky's literary style and narrative techniques
- **Text Generation**: Generating text in the style of Dostoyevsky

### Languages

- **English**: All texts are English translations of original Russian works

## Dataset Structure

### Data Instances

Each instance contains a single text chunk:

```json
{
  "chunks": "But what can be expected of a man who has such a desperate character that he will hang himself for being always late for dinner..."
}
```

### Data Fields

- `chunks` (string): A preprocessed text segment of approximately 512 tokens

### Data Splits

The dataset contains **6,217 text chunks** in a single split, suitable for:

- Training: Use 90% for fine-tuning
- Validation: Reserve 10% for evaluation

## Source Data

### Initial Data Collection

The source texts were downloaded from Project Gutenberg, ensuring all content is in the public domain.

#### Source Works

1. **Crime and Punishment** (Project Gutenberg #2554)
2. **The Brothers Karamazov** (Project Gutenberg #28054)
3. **The Idiot**
4. **Notes from the Underground** (Project Gutenberg #600)

### Data Processing Pipeline

1. **Header/Footer Removal**: Used `gutenberg-cleaner` to remove Project Gutenberg boilerplate
2. **Text Normalization**: Applied `ftfy` for encoding fixes and `unidecode` for ASCII conversion
3. **Content Filtering**:
   - Removed paragraphs shorter than 200 characters
   - Filtered out chapter titles and section headers
   - Excluded epigraphs and other metadata
4. **Chunking**: Segmented text into approximately 512-token chunks using SmolLM tokenizer
5. **Quality Control**: Manual review of sample chunks for content quality

## Dataset Creation

### Curation Rationale

This dataset was created to enable fine-tuning of small language models on Dostoyevsky's writing style, characterized by:

- Psychological depth and introspection
- Philosophical themes and existential questions
- Complex character development
- Rich narrative voice and distinctive prose rhythm

### Source Data Analysis

- **Total chunks**: 6,217
- **Average chunk length**: ~512 tokens
- **Content diversity**: Narrative prose, dialogue, internal monologue, philosophical passages
- **Temporal coverage**: Spans Dostoyevsky's major creative periods

## Considerations for Using the Data

### Social Impact of Dataset

This dataset preserves and makes accessible the literary heritage of one of world literature's greatest authors, enabling:

- **Educational applications**: Literary analysis and style studies
- **Creative writing assistance**: Learning narrative techniques
- **Cultural preservation**: Maintaining access to classic literature

### Discussion of Biases

- **Historical context**: Reflects 19th-century social norms and perspectives
- **Translation artifacts**: Based on English translations, may not capture original Russian nuances
- **Selection bias**: Limited to four major works, may not represent complete stylistic evolution
- **Cultural specificity**: Reflects Russian cultural context and Orthodox Christian themes

### Other Known Limitations

- **Temporal scope**: Limited to works from 1864-1880
- **Genre limitation**: Primarily novels, lacks short stories and journalism
- **Translation dependency**: Quality dependent on translator choices
- **Processing artifacts**: Some text segmentation may split related passages

## Additional Information

### Dataset Curators

- **Created by**: [satyapratheek](https://huggingface.co/satyapratheek)
- **Processing date**: July 2025
- **Contact**: Available through Hugging Face profile

### Licensing Information

All source texts are in the **public domain** (published before 1928). The dataset processing and compilation is released under **public domain** as well.

### Citation Information

```bibtex
@dataset{dostoyevsky_chunks_2025,
  title={Dostoyevsky Chunks: Preprocessed Text Dataset for Author Style Fine-tuning},
  author={satyapratheek},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/satyapratheek/dostoyevsky_chunks}
}
```

### Contributions

If you use this dataset, please consider citing both the dataset and the original Project Gutenberg sources.

## Usage Example

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("satyapratheek/dostoyevsky_chunks")

# Access the chunks
chunks = dataset["train"]["chunks"]
print(f"Total chunks: {len(chunks)}")
print(f"Sample chunk: {chunks[:200]}...")

# Use for fine-tuning
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM-135M")

def tokenize_function(examples):
    return tokenizer(examples["chunks"], truncation=True, padding="max_length", max_length=512)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
```

## Related Models

This dataset was used to fine-tune:

- [satyapratheek/smollm-dostoyevsky](https://huggingface.co/satyapratheek/smollm-dostoyevsky) - LoRA fine-tuned SmolLM-135M

---

**Processing Stats from Fine-tuning:**

- Training duration: 4 hours, 56 minutes, 35 seconds  
- Final training loss: 3.254
- Training samples/second: 1.048
- Hardware: Apple M1 MacBook Air (8GB RAM)