Duplicate from SakanaAI/Sudoku-CTC-Reasoning

Co-authored-by: Jeffrey Seely <jsseely@users.noreply.huggingface.co>

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README.md

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+---
+license: mit
+dataset_info:
+- config_name: processed
+  features:
+  - name: youtube_id
+    dtype: string
+  - name: text
+    dtype: string
+  - name: puzzle_data
+    struct:
+    - name: author
+      dtype: string
+    - name: cols
+      dtype: int64
+    - name: date
+      dtype: string
+    - name: encoded_puzzle
+      dtype: string
+    - name: initial_board
+      dtype: string
+    - name: lgc_timestamp
+      dtype: float64
+    - name: puzzle_id
+      dtype: string
+    - name: rows
+      dtype: int64
+    - name: rules
+      dtype: string
+    - name: sequential_number
+      dtype: int64
+    - name: solution
+      dtype: string
+    - name: sudokupad_url
+      dtype: string
+    - name: title
+      dtype: string
+    - name: visual_elements
+      dtype: string
+    - name: youtube_id
+      dtype: string
+  - name: initial_board_ascii
+    dtype: string
+  - name: initial_board_serialized
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 23216253
+    num_examples: 451
+  - name: valid
+    num_bytes: 144090
+    num_examples: 3
+  download_size: 9942917
+  dataset_size: 23360343
+- config_name: raw
+  features:
+  - name: puzzle_id
+    dtype: string
+  - name: youtube_id
+    dtype: string
+  - name: action_data
+    list:
+    - name: actions
+      sequence: string
+    - name: frame
+      dtype: int64
+    - name: idx
+      dtype: int64
+    - name: serialized_state
+      dtype: string
+    - name: time
+      dtype: float64
+  - name: asr_data
+    struct:
+    - name: language
+      dtype: string
+    - name: segments
+      list:
+      - name: avg_logprob
+        dtype: float64
+      - name: compression_ratio
+        dtype: float64
+      - name: end
+        dtype: float64
+      - name: id
+        dtype: int64
+      - name: no_speech_prob
+        dtype: float64
+      - name: seek
+        dtype: int64
+      - name: start
+        dtype: float64
+      - name: temperature
+        dtype: float64
+      - name: text
+        dtype: string
+      - name: tokens
+        sequence: int64
+      - name: words
+        list:
+        - name: end
+          dtype: float64
+        - name: probability
+          dtype: float64
+        - name: start
+          dtype: float64
+        - name: word
+          dtype: string
+    - name: text
+      dtype: string
+  - name: puzzle_data
+    struct:
+    - name: author
+      dtype: string
+    - name: cols
+      dtype: int64
+    - name: date
+      dtype: string
+    - name: encoded_puzzle
+      dtype: string
+    - name: initial_board
+      dtype: string
+    - name: lgc_timestamp
+      dtype: float64
+    - name: puzzle_id
+      dtype: string
+    - name: rows
+      dtype: int64
+    - name: rules
+      dtype: string
+    - name: sequential_number
+      dtype: int64
+    - name: solution
+      dtype: string
+    - name: sudokupad_url
+      dtype: string
+    - name: title
+      dtype: string
+    - name: visual_elements
+      dtype: string
+    - name: youtube_id
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 1924808922
+    num_examples: 1351
+  download_size: 365262005
+  dataset_size: 1924808922
+configs:
+- config_name: processed
+  data_files:
+  - split: train
+    path: processed/train-*
+  - split: valid
+    path: processed/valid-*
+- config_name: raw
+  data_files:
+  - split: train
+    path: raw/train-*
+---
+<h1 align="center">
+  <b>Sudoku-Bench</b><br>
+</h1>
+  
+<p align="center">
+  📝 <a href="https://pub.sakana.ai/sudoku">[Leaderboard]</a>
+  📝 <a href="https://arxiv.org/abs/2505.16135">[Technical Report]</a>
+  📝 <a href="https://sakana.ai/sudoku-bench">[Blog Post]</a><br>
+  🤗 <a href="https://huggingface.co/datasets/SakanaAI/Sudoku-Bench">[Sudoku-Bench puzzle dataset]</a>
+  🤗 <a href="https://huggingface.co/datasets/SakanaAI/Sudoku-CTC-Reasoning">[Sudoku-CTC-Reasoning dataset]</a>
+</p>
+
+## Sudoku-CTC-Reasoning dataset
+
+The Sudoku-CTC-Reasoning dataset contains the reasoning traces of 1351 puzzles featured in the [Cracking the Cryptic](https://www.youtube.com/c/CrackingTheCryptic) YouTube channel, and thus provides rich learning signals for training LMs to learn reasoning in a Sudoku game or for a broader range of reasoning-intensive tasks.
+
+> [!NOTE]
+> This dataset is provided with permission from [Cracking the Cryptic](https://www.youtube.com/c/CrackingTheCryptic).
+
+## Data statistics
+
+- Videos with reasoning traces: 1351
+- Total actions: 3539008
+- Total actions (excluding highlights): 818145
+- Total words: 8921707
+
+> [!NOTE]
+> There will be updates to the dataset with more reasoning traces, so please stay tuned.
+
+## Combined ASR and Action Dataset
+
+<img width="1403" alt="Image" src="https://github.com/user-attachments/assets/e8ff55ef-ebbe-4488-a045-57ba8c1f8d64" />
+
+For each video we provide `action_data` and `asr_data`. The `action_data` is extracted from the youtube video using a video-to-actions pipeline (described below) and consists of a sequence of actions taken in the SudokuPad app as the host solves the puzzle. The `asr_data` is extracted from the youtube video using [Whisper](https://github.com/openai/whisper). 
+
+## Loading the dataset
+
+The dataset has two subsets. We document the `raw` subset here, and the `processed` subset is described in the [Sudoku-Bench's data_processing README](https://github.com/SakanaAI/Sudoku-Bench/tree/main/src/ctc_processing).
+
+```python
+import datasets
+
+dataset = datasets.load_dataset('SakanaAI/Sudoku-CTC-Reasoning', 'raw')
+```
+
+Each entry of `dataset` contains the following fields:
+- `youtube_id`: the id of the youtube video
+- `action_data`: the Sudokupad actions from the video
+- `asr_data`: the audio transcript of the puzzle
+- `puzzle_id`: the id of the puzzle
+- `puzzle_data`: the puzzle data following the format of the Sudoku puzzle datasets.
+
+## Actions format
+
+The `action_data` for each video is a list of action groups. Each action group has the following fields:
+- `idx`: the index of the action group in the video
+- `frame`: the frame number of the corresponding frame in the video
+- `time`: the time in seconds of the action group using the video's time axis
+- `actions`: a list of strings of (serialized actions) taken on the SudokuPad board that occurred between the previous frame and the current frame.
+- `serialized_state`: the serialized state of the SudokuPad board.
+
+Typically each action group contains a single action.
+
+### Serialized action format
+
+The serialized action `'cd:+:7:r3c5'` denotes "add a candidate 7 to cell r3c5". In general we use the following action types:
+
+Action type:
+- `vl`: value (i.e. the actual proposed value of the cell)
+- `cd`: candidate or center pencilmark
+- `pm`: corner pencilmark
+- `co`: color
+- `sl`: select
+- `ds`: deselect
+
+Operation type:
+- `+`: add to the current list
+- `-`: remove from the current list
+
+Value:
+- `1-9`: for value, candidates, and pencilmarks
+- `0-9`: for color, with mapping [here](https://github.com/SakanaAI/Sudoku-Bench/blob/main/src/sudokupad_interaction/app.py#L26).
+
+Coordinates:
+- `rxcy`: row and column of the action. In `sl` and `ds` actions, `rxcy` is a comma-separated list.
+
+### Serialized state format
+
+The serialized state can be loaded into [SudokuPad](https://github.com/SakanaAI/Sudoku-Bench/tree/main/src/sudokupad_interaction) by
+```python
+import requests
+
+# with sudokupad_interaction/app.py running
+response = requests.put("http://localhost:8000/set_state", json={"serialized_state": serialized_state})
+```
+
+The format of serialized state follows that used internally by SudokuPad: For each cell, use `/` to separate value, candidate, pencilmark, color, highlight, pen-tool, respectively, with trailing `/`s removed. 
+
+Example: A `serialized_state` of `{"cells":["6","/1,2,4,5", ...` indicates that `r1c1` has a value of 6, and `r1c2` has candidates (center small digits)1, 2, 4, 5.
+
+## ASR format
+
+The `asr_data` is the output of Whisper ASR using `model.transcribe(audio_file, language="en", task="transcribe", word_timestamps=True)` for `model = whisper.load_model("turbo")`. Please see [Whisper's documentation](https://github.com/openai/whisper) for details.
+
+## Video-to-actions summary
+
+Extracting sudokupad actions from the video is a multi-step process:
+
+1. Detect the x, y, height, width of the Sudoku board in the video
+2. Detect the x, y, height, width of the corresponding Sudoku board in the SudokuPad app with the same puzzle loaded
+3. Using 1. and 2. and the location of individual cell rendering in the browser window in SudokuPad (the lines that make up the typically 9x9 grid), determine the corresponding cell locations in the youtube video.
+4. Take a sequence of keyframes from the video cropped to the Sudoku board. A keyframe is where a pixel-wise change occured above a threshold. A keyframe is a candidate for when an action was taken.
+5. For each keyframe, use a trained resnet classifier to map the pixel input to a multiclass prediction each of the (typically 81) SudokuPad cell states, which includes the colors, pencilmarks (corner small digits), candidates (center small digits), and current value (center large digit). The resnet was trained on synthetic data from the SudokuPad app. The exact images to feed into the resnet are determined by the cell locations from 1. and 2. Combine the individual cell state predictions to obtain a single board state for this keyframe.
+6. From the sequence of states, determine the state-diffs to get the sequence of actions, saved as `action_data`.
+
+> [!NOTE]
+> The code for the video-to-actions pipeline itself is not open-sourced as part of [Sudoku-Bench](https://github.com/SakanaAI/Sudoku-Bench).
+
+## References
+
+- [CTC YouTube channel](https://www.youtube.com/c/CrackingTheCryptic)
+- [CTC catalogue](https://ctc-catalogue.com/)
+
+## Citation
+```bibtex
+@misc{seely2025sudoku-bench,
+  title={{Sudoku-Bench}},
+  author={Seely, Jeffrey and Imajuku, Yuki and Zhao, Tianyu and Cetin, Edoardo and Jones, Llion},
+  howpublished = {\url{https://github.com/SakanaAI/Sudoku-Bench}},
+  year={2025}
+}
+```

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