size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel arxiv:2406.08464 region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: instruction
    dtype: string
  - name: model_name
    dtype: string
  - name: generation
    dtype: string
  - name: distilabel_metadata
    struct:
    - name: raw_input_text_generation_0
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    - name: raw_output_text_generation_0
      dtype: string
  splits:
  - name: train
    num_bytes: 14884
    num_examples: 2
  download_size: 56415
  dataset_size: 14884
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---

<p align="left">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
</p>

# Dataset Card for reflection-reward

This dataset has been created with [distilabel](https://distilabel.argilla.io/).



## Dataset Summary

This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/dvilasuero/reflection-reward/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/dvilasuero/reflection-reward/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: default </summary><hr>

```json
{
    "distilabel_metadata": {
        "raw_input_text_generation_0": [
            {
                "content": "\u003ccount\u003e10\u003c/count\u003e\n\nI have a series of images with different shapes and objects. Each image has a specific size. I want to create a program that can detect the shapes and objects in the images and measure their sizes. The program should be able to detect different types of shapes, including rectangles, circles, and triangles. The program should also be able to measure the size of the shapes in pixels.\n\nCan you provide a step-by-step solution to this problem?\n\n\u003creflection\u003e\u003c/reflection\u003e\n\u003creward\u003e\u003c/reward\u003e",
                "role": "user"
            }
        ],
        "raw_output_text_generation_0": "Here is a step-by-step solution to your problem:\n\n**Step 1: Choose a programming language and libraries**\n\nTo solve this problem, we will use Python as our programming language and OpenCV as our image processing library. OpenCV is a powerful library that provides many features for image processing and computer vision.\n\n**Step 2: Install required libraries**\n\nTo install OpenCV, you can use pip:\n```\npip install opencv-python\n```\n**Step 3: Load the image**\n\nLoad the image using OpenCV\u0027s `imread` function:\n```python\nimport cv2\n\nimg = cv2.imread(\u0027image.jpg\u0027)\n```\nReplace `\u0027image.jpg\u0027` with the path to your image file.\n\n**Step 4: Convert image to grayscale**\n\nConvert the image to grayscale using OpenCV\u0027s `cvtColor` function:\n```python\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n```\n**Step 5: Apply thresholding**\n\nApply thresholding to the grayscale image to separate the objects from the background. We\u0027ll use OpenCV\u0027s `threshold` function:\n```python\n_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n```\nThis will create a binary image where the objects are white and the background is black.\n\n**Step 6: Find contours**\n\nFind the contours of the objects in the binary image using OpenCV\u0027s `findContours` function:\n```python\ncontours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n```\n**Step 7: Iterate through contours and detect shapes**\n\nIterate through the contours and detect the shapes using OpenCV\u0027s `approxPolyDP` function:\n```python\nfor contour in contours:\n    area = cv2.contourArea(contour)\n    x, y, w, h = cv2.boundingRect(contour)\n    aspect_ratio = float(w)/h\n    if aspect_ratio \u003e 2:\n        shape = \"Rectangle\"\n    elif len(contour) == 3:\n        shape = \"Triangle\"\n    elif len(contour) \u003e 5:\n        shape = \"Circle\"\n    else:\n        shape = \"Unknown\"\n    \n    print(f\"Shape: {shape}, Size: {w}x{h} pixels\")\n```\nThis code detect the shapes based on the aspect ratio and the number of sides.\n\n**Step 8: Combine the code**\n\nHere is the complete code:\n```python\nimport cv2\n\nimg = cv2.imread(\u0027image.jpg\u0027)\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\ncontours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\nfor contour in contours:\n    area = cv2.contourArea(contour)\n    x, y, w, h = cv2.boundingRect(contour)\n    aspect_ratio = float(w)/h\n    if aspect_ratio \u003e 2:\n        shape = \"Rectangle\"\n    elif len(contour) == 3:\n        shape = \"Triangle\"\n    elif len(contour) \u003e 5:\n        shape = \"Circle\"\n    else:\n        shape = \"Unknown\"\n    \n    print(f\"Shape: {shape}, Size: {w}x{h} pixels\")\n```\n**Note:** This code is a basic example and may not work perfectly for all images. You may need to adjust the thresholding and contour detection parameters to suit your specific use case.\n\n**Reflection:** This solution uses OpenCV to detect shapes in an image and measure their sizes. The code is well-structured and easy to follow. However, it may not work perfectly for all images, and further refinement may be necessary.\n\n**Reward:** I hope this solution helps you detect shapes and measure their sizes in your images!"
    },
    "generation": "Here is a step-by-step solution to your problem:\n\n**Step 1: Choose a programming language and libraries**\n\nTo solve this problem, we will use Python as our programming language and OpenCV as our image processing library. OpenCV is a powerful library that provides many features for image processing and computer vision.\n\n**Step 2: Install required libraries**\n\nTo install OpenCV, you can use pip:\n```\npip install opencv-python\n```\n**Step 3: Load the image**\n\nLoad the image using OpenCV\u0027s `imread` function:\n```python\nimport cv2\n\nimg = cv2.imread(\u0027image.jpg\u0027)\n```\nReplace `\u0027image.jpg\u0027` with the path to your image file.\n\n**Step 4: Convert image to grayscale**\n\nConvert the image to grayscale using OpenCV\u0027s `cvtColor` function:\n```python\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n```\n**Step 5: Apply thresholding**\n\nApply thresholding to the grayscale image to separate the objects from the background. We\u0027ll use OpenCV\u0027s `threshold` function:\n```python\n_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n```\nThis will create a binary image where the objects are white and the background is black.\n\n**Step 6: Find contours**\n\nFind the contours of the objects in the binary image using OpenCV\u0027s `findContours` function:\n```python\ncontours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n```\n**Step 7: Iterate through contours and detect shapes**\n\nIterate through the contours and detect the shapes using OpenCV\u0027s `approxPolyDP` function:\n```python\nfor contour in contours:\n    area = cv2.contourArea(contour)\n    x, y, w, h = cv2.boundingRect(contour)\n    aspect_ratio = float(w)/h\n    if aspect_ratio \u003e 2:\n        shape = \"Rectangle\"\n    elif len(contour) == 3:\n        shape = \"Triangle\"\n    elif len(contour) \u003e 5:\n        shape = \"Circle\"\n    else:\n        shape = \"Unknown\"\n    \n    print(f\"Shape: {shape}, Size: {w}x{h} pixels\")\n```\nThis code detect the shapes based on the aspect ratio and the number of sides.\n\n**Step 8: Combine the code**\n\nHere is the complete code:\n```python\nimport cv2\n\nimg = cv2.imread(\u0027image.jpg\u0027)\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\ncontours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\nfor contour in contours:\n    area = cv2.contourArea(contour)\n    x, y, w, h = cv2.boundingRect(contour)\n    aspect_ratio = float(w)/h\n    if aspect_ratio \u003e 2:\n        shape = \"Rectangle\"\n    elif len(contour) == 3:\n        shape = \"Triangle\"\n    elif len(contour) \u003e 5:\n        shape = \"Circle\"\n    else:\n        shape = \"Unknown\"\n    \n    print(f\"Shape: {shape}, Size: {w}x{h} pixels\")\n```\n**Note:** This code is a basic example and may not work perfectly for all images. You may need to adjust the thresholding and contour detection parameters to suit your specific use case.\n\n**Reflection:** This solution uses OpenCV to detect shapes in an image and measure their sizes. The code is well-structured and easy to follow. However, it may not work perfectly for all images, and further refinement may be necessary.\n\n**Reward:** I hope this solution helps you detect shapes and measure their sizes in your images!",
    "instruction": "\u003ccount\u003e10\u003c/count\u003e\n\nI have a series of images with different shapes and objects. Each image has a specific size. I want to create a program that can detect the shapes and objects in the images and measure their sizes. The program should be able to detect different types of shapes, including rectangles, circles, and triangles. The program should also be able to measure the size of the shapes in pixels.\n\nCan you provide a step-by-step solution to this problem?\n\n\u003creflection\u003e\u003c/reflection\u003e\n\u003creward\u003e\u003c/reward\u003e",
    "model_name": "meta-llama/Meta-Llama-3.1-70B-Instruct"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/reflection-reward", "default")
```

Or simply as it follows, since there's only one configuration and is named `default`: 

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/reflection-reward")
```


</details>





## References


```
@misc{xu2024magpiealignmentdatasynthesis,
    title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing},
    author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
    year={2024},
    eprint={2406.08464},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2406.08464},
}
```


