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README.md
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size_categories:
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- n<1K
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pretty_name: LyTOC Benchmark
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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dataset_info:
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features:
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- name: homework
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dtype: string
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- name: exercise_number
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dtype: string
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- name: sub_problem
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dtype: string
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- name: content
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dtype: string
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- name: full_id
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dtype: string
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splits:
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- name: train
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num_bytes: 6972
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num_examples: 28
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download_size: 6910
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dataset_size: 6972
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---
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# LyTOC Benchmark Dataset
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### Dataset Summary
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The LyTOC (Logic and Theory of Computation) Benchmark contains
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**Key Features:**
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- Topics: automata theory, complexity theory, Turing machines, formal languages, algorithm analysis
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- LaTeX mathematical notation preserved
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- Structured with exercise numbers
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- Clean extraction with OCR post-processing
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### Supported Tasks
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### Data Instances
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Each instance represents a single exercise
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```json
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{
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"homework": "hw1",
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"exercise_number": "3",
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"sub_problem": null,
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"content": "Let $\\Sigma = \\{0, 1\\}$. Let language\n\n$$L = \\{w \\in \\{0, 1\\}^* : w \\text{ has an unequal number of 0's and 1's}\\}.$$\n\nProve $L^* = \\Sigma^*$.",
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"full_id": "hw1_ex3"
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}
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- `homework` (string): Homework identifier (e.g., "hw1", "hw2", "hw13")
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- `exercise_number` (string): Exercise number within the homework (e.g., "1", "2", "3")
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- `sub_problem` (string or null): Sub-problem identifier if the exercise has multiple parts (e.g., "1", "2")
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- `content` (string): Full exercise text including LaTeX mathematical notation
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- `full_id` (string): Unique identifier for the exercise (e.g., "hw1_ex3", "hw2_ex3_1")
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### Data Splits
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The dataset consists of a single split containing all
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## Dataset Statistics
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- **Total Exercises**:
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- **Homeworks**: 9 (hw1, hw2, hw3, hw5, hw6, hw9, hw10, hw11, hw13)
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- **Exercises with Sub-problems**: 2
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- **Average Content Length**: ~200-500 characters per exercise
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### Topic Distribution
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2. **OCR Processing**: Extract text using SimpleTex OCR API
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3. **Punctuation Normalization**: Convert Chinese punctuation to English equivalents
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4. **Exercise Splitting**: Use regex patterns to identify exercise boundaries
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5. **Sub-problem Detection**: Identify and separate sub-problems within exercises
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6. **Metadata Generation**: Create unique identifiers and structure data
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### Annotations
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from datasets import load_dataset
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# Load the dataset
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dataset = load_dataset("
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# Access an exercise
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exercise = dataset['train'][0]
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## Version History
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- **v1.0.0** (2025-12-30): Initial release with
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size_categories:
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- n<1K
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pretty_name: LyTOC Benchmark
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---
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# LyTOC Benchmark Dataset
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### Dataset Summary
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The LyTOC (Logic and Theory of Computation) Benchmark contains 27 carefully extracted exercises from 9 homework assignments covering fundamental topics in theoretical computer science. Each exercise is preserved with its original LaTeX mathematical notation, making it suitable for evaluating language models on formal reasoning tasks.
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**Key Features:**
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- 27 exercises across 9 homework assignments
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- Topics: automata theory, complexity theory, Turing machines, formal languages, algorithm analysis
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- LaTeX mathematical notation preserved
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- Structured with exercise numbers
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- Clean extraction with OCR post-processing
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### Supported Tasks
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### Data Instances
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Each instance represents a single exercise:
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```json
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{
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"homework": "hw1",
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"exercise_number": "3",
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"content": "Let $\\Sigma = \\{0, 1\\}$. Let language\n\n$$L = \\{w \\in \\{0, 1\\}^* : w \\text{ has an unequal number of 0's and 1's}\\}.$$\n\nProve $L^* = \\Sigma^*$.",
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"full_id": "hw1_ex3"
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}
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- `homework` (string): Homework identifier (e.g., "hw1", "hw2", "hw13")
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- `exercise_number` (string): Exercise number within the homework (e.g., "1", "2", "3")
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- `content` (string): Full exercise text including LaTeX mathematical notation
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- `full_id` (string): Unique identifier for the exercise (e.g., "hw1_ex3", "hw2_ex3_1")
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### Data Splits
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The dataset consists of a single split containing all 27 exercises.
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## Dataset Statistics
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- **Total Exercises**: 27
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- **Homeworks**: 9 (hw1, hw2, hw3, hw5, hw6, hw9, hw10, hw11, hw13)
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- **Average Content Length**: ~200-500 characters per exercise
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### Topic Distribution
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2. **OCR Processing**: Extract text using SimpleTex OCR API
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3. **Punctuation Normalization**: Convert Chinese punctuation to English equivalents
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4. **Exercise Splitting**: Use regex patterns to identify exercise boundaries
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6. **Metadata Generation**: Create unique identifiers and structure data
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### Annotations
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from datasets import load_dataset
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# Load the dataset
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dataset = load_dataset("Zecyel/LyTOC")
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# Access an exercise
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exercise = dataset['train'][0]
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## Version History
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- **v1.0.0** (2025-12-30): Initial release with 27 exercises from 9 homework assignments
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