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README.md

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+---
+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)

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dataset_info.json

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+  "config_name": "default",
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+  "dataset_size": 3815358,
+  "description": "",
+  "download_checksums": {
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+  "download_size": 3934721,
+  "features": {
+    "chunks": {
+      "feature": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "_type": "Sequence"
+    }
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state.json

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+}