size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel arxiv:2310.01377 region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
- config_name: format_text_generation_d_p_o_0
  features:
  - name: instruction
    dtype: string
  - name: topic
    dtype: string
  - name: generations
    sequence: string
  - name: model_names
    sequence: string
  - name: distilabel_metadata
    struct:
    - name: raw_input_ultra_feedback_0
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    - name: raw_output_ultra_feedback_0
      dtype: 'null'
  - name: ratings
    sequence: 'null'
  - name: rationales
    sequence: 'null'
  - name: model_name
    dtype: string
  - name: prompt
    dtype: 'null'
  - name: prompt_id
    dtype: 'null'
  - name: chosen
    dtype: 'null'
  - name: chosen_model
    dtype: 'null'
  - name: chosen_rating
    dtype: 'null'
  - name: rejected
    dtype: 'null'
  - name: rejected_model
    dtype: 'null'
  - name: rejected_rating
    dtype: 'null'
  splits:
  - name: train
    num_bytes: 200850
    num_examples: 20
  download_size: 109438
  dataset_size: 200850
- config_name: preference_to_argilla_0
  features:
  - name: instruction
    dtype: string
  - name: topic
    dtype: string
  - name: generations
    sequence: string
  - name: model_names
    sequence: string
  - name: distilabel_metadata
    struct:
    - name: raw_input_ultra_feedback_0
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    - name: raw_output_ultra_feedback_0
      dtype: 'null'
  - name: ratings
    sequence: 'null'
  - name: rationales
    sequence: 'null'
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 200850
    num_examples: 20
  download_size: 106959
  dataset_size: 200850
configs:
- config_name: format_text_generation_d_p_o_0
  data_files:
  - split: train
    path: format_text_generation_d_p_o_0/train-*
- config_name: preference_to_argilla_0
  data_files:
  - split: train
    path: preference_to_argilla_0/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 prompt-example

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/JeganRavichandran/prompt-example/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/JeganRavichandran/prompt-example/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: format_text_generation_d_p_o_0 </summary><hr>

```json
{
    "chosen": null,
    "chosen_model": null,
    "chosen_rating": null,
    "distilabel_metadata": {
        "raw_input_ultra_feedback_0": [
            {
                "content": "Your role is to evaluate text quality based on given criteria.\nYou\u0027ll receive an instructional description (\"Instruction\") and 2 text outputs (\"Text\").\nUnderstand and interpret instructions to evaluate effectively.\nProvide annotations for each text with a rating and rationale.\nThe 2 texts given are independent, and should be evaluated separately.\n",
                "role": "system"
            },
            {
                "content": "# General Text Quality Assessment\n\nEvaluate the model\u0027s outputs based on various criteria:\n\n1. **Correctness \u0026 Informativeness**: Does the output provide accurate and helpful information?\n2. **Honesty \u0026 Uncertainty**: How confidently does the model convey its information, and does it express uncertainty appropriately?\n3. **Truthfulness \u0026 Hallucination**: Does the model introduce misleading or fabricated details?\n4. **Instruction Following**: Does the model\u0027s output align with given instructions and the user\u0027s intent?\n\nYour role is to provide a holistic assessment considering all the above factors.\n\n**Scoring**: Rate outputs 1 to 5 based on the overall quality, considering all aspects:\n1. **Low Quality**: Contains inaccuracies, may be entirely wrong or has severe hallucinations.\n2. **Moderate Quality**: Addresses some aspects, but has errors or is partially aligned with instructions.\n3. **Good**: Generally accurate but may contain minor errors or slight deviations.\n4. **Very Good**: Near perfect, with minor issues in terms of alignment or confidence.\n5, **Excellent**: Accurate, confident, aligned with instructions, and free of hallucinations.\n\n## Format:\n\n### Input\nInstruction: [Clearly specify the task goal and restrictions]\n\nTexts:\n\u003ctext 1\u003e [Text 1]\n\u003ctext 2\u003e [Text 2]\n\n### Output\n#### Output for Text 1\nRating: [Rating for text 1]\nRationale: [Rationale for the rating in short sentences]\n\n#### Output for Text 2\nRating: [Rating]\nRationale: [Rationale]\n\n---\n\n## Annotation\n\n### Input\nInstruction: How can I create an efficient and robust workflow that utilizes advanced automation techniques to extract targeted data, including customer information, from diverse PDF documents and effortlessly integrate it into a designated Google Sheet? Furthermore, I am interested in establishing a comprehensive and seamless system that promptly activates an SMS notification on my mobile device whenever a new PDF document is uploaded to the Google Sheet, ensuring real-time updates and enhanced accessibility.\n\nTexts:\n\u003ctext 1\u003e What a fantastic question! Creating an efficient and robust workflow to extract data from PDFs and integrate it into a Google Sheet, along with real-time notifications, is a great use case. Here\u0027s a step-by-step guide to help you achieve this:\n\n**Step 1: Choose the right tools**\n\n1. **Google Apps Script**: This is the backbone of your automation. It\u0027s a scripting platform that allows you to automate tasks within Google Sheets.\n2. **PDF.co**: A powerful API that enables you to extract data from PDFs using OCR (Optical Character Recognition) and other techniques.\n3. **Google Cloud Storage**: To store and manage your PDF files.\n4. **Twilio**: A cloud communication platform that allows you to send SMS notifications.\n\n**Step 2: Set up the workflow**\n\n1. Create a new Google Sheet to store the extracted data.\n2. Set up a Google Cloud Storage bucket to store the PDF files.\n3. Use PDF.co\u0027s API to extract data from the PDFs. You can use their JavaScript library to do this.\n4. Create a Google Apps Script to trigger the PDF extraction process whenever a new PDF file is uploaded to the Cloud Storage bucket.\n5. Use the extracted data to populate the Google Sheet.\n\n**Step 3: Automate the data extraction process**\n\n1. In your Google Apps Script, create a function that uses the PDF.co API to extract data from the PDFs. You can use their JavaScript library to do this.\n2. Call this function whenever a new PDF file is uploaded to the Cloud Storage bucket.\n3. Use the extracted data to populate the corresponding Google Sheet.\n\n**Step 4: Set up real-time notifications**\n\n1. Use Twilio to send SMS notifications to your mobile device whenever a new PDF file is uploaded to the Cloud Storage bucket.\n2. Create a Twilio account and set up a new number for receiving SMS messages.\n3. In your Google Apps Script, use the Twilio API to send an SMS notification with a message indicating that a new PDF file has been uploaded.\n\n**Step 5: Integrate everything**\n\n1. Use Google Apps Script\u0027s `SpreadsheetApp` service to trigger the PDF extraction process when a new PDF file is uploaded to the Cloud Storage bucket.\n2. Use the `UrlFetchApp` service to make API requests to PDF.co and Twilio.\n3. Use the `PropertiesService` to store any necessary configuration settings, such as API keys and authentication tokens.\n\nHere\u0027s some sample code to get you started\n\u003ctext 2\u003e  To create an efficient and robust workflow that fulfills your requirements, you can utilize a combination of Google Apps Script, Google Sheets, and third-party services for automation and SMS notifications. Here\u0027s a step-by-step guide to setting up this workflow:\n\n1. **Prepare Google Sheets and Google Apps Script:**\n\n   - Create a new Google Sheet to store the extracted data.\n   - Click on \"Extensions\" \u003e \"Apps Script\" to open the Google Apps Script editor.\n\n2. **Install required libraries in Google Apps Script:**\n\n   - Go to \"Resources\" \u003e \"Libraries.\"\n   - Add the following libraries and enable them:\n     - \"PDF Utilities\" (Made by Google) - `1eMZo5aGKrYKj8JxnZSFWoH97nXG7HzO`\n     - \"Google Cloud Platform\" - `1B7FSrk5Zi6L1rSxxTDgDEUsPzlukDsi4KGuTMorsTQHhGBzBkM8_5U-H`\n     - \"Twilio API for JavaScript\" - `1j8XWnN5D4eS_rQXVbX33aRzK3-U3qJGn`\n\n3. **Create the function to extract data from PDFs:**\n\n   - In the Apps Script editor, replace the default code with the following:\n\n```js\nconst PDF_UTILITIES_ID = \u00271eMZo5aGKrYKj8JxnZSFWoH97nXG7HzO\u0027;\nconst GCP_ID = \u00271B7FSrk5Zi6L1rSxxTDgDEUsPzlukDsi4KGuTMorsTQHhGBzBkM8_5U-H\u0027;\nconst TWILIO_ID = \u00271j8XWnN5D4eS_rQXVbX33aRzK3-U3qJGn\u0027;\n\nconst PDF_UTILITIES = LibraryLoader.loadLibraryById(PDF_UTILITIES_ID);\nconst\n\n### Output\n",
                "role": "user"
            }
        ],
        "raw_output_ultra_feedback_0": null
    },
    "generations": [
        "What a fantastic question! Creating an efficient and robust workflow to extract data from PDFs and integrate it into a Google Sheet, along with real-time notifications, is a great use case. Here\u0027s a step-by-step guide to help you achieve this:\n\n**Step 1: Choose the right tools**\n\n1. **Google Apps Script**: This is the backbone of your automation. It\u0027s a scripting platform that allows you to automate tasks within Google Sheets.\n2. **PDF.co**: A powerful API that enables you to extract data from PDFs using OCR (Optical Character Recognition) and other techniques.\n3. **Google Cloud Storage**: To store and manage your PDF files.\n4. **Twilio**: A cloud communication platform that allows you to send SMS notifications.\n\n**Step 2: Set up the workflow**\n\n1. Create a new Google Sheet to store the extracted data.\n2. Set up a Google Cloud Storage bucket to store the PDF files.\n3. Use PDF.co\u0027s API to extract data from the PDFs. You can use their JavaScript library to do this.\n4. Create a Google Apps Script to trigger the PDF extraction process whenever a new PDF file is uploaded to the Cloud Storage bucket.\n5. Use the extracted data to populate the Google Sheet.\n\n**Step 3: Automate the data extraction process**\n\n1. In your Google Apps Script, create a function that uses the PDF.co API to extract data from the PDFs. You can use their JavaScript library to do this.\n2. Call this function whenever a new PDF file is uploaded to the Cloud Storage bucket.\n3. Use the extracted data to populate the corresponding Google Sheet.\n\n**Step 4: Set up real-time notifications**\n\n1. Use Twilio to send SMS notifications to your mobile device whenever a new PDF file is uploaded to the Cloud Storage bucket.\n2. Create a Twilio account and set up a new number for receiving SMS messages.\n3. In your Google Apps Script, use the Twilio API to send an SMS notification with a message indicating that a new PDF file has been uploaded.\n\n**Step 5: Integrate everything**\n\n1. Use Google Apps Script\u0027s `SpreadsheetApp` service to trigger the PDF extraction process when a new PDF file is uploaded to the Cloud Storage bucket.\n2. Use the `UrlFetchApp` service to make API requests to PDF.co and Twilio.\n3. Use the `PropertiesService` to store any necessary configuration settings, such as API keys and authentication tokens.\n\nHere\u0027s some sample code to get you started",
        " To create an efficient and robust workflow that fulfills your requirements, you can utilize a combination of Google Apps Script, Google Sheets, and third-party services for automation and SMS notifications. Here\u0027s a step-by-step guide to setting up this workflow:\n\n1. **Prepare Google Sheets and Google Apps Script:**\n\n   - Create a new Google Sheet to store the extracted data.\n   - Click on \"Extensions\" \u003e \"Apps Script\" to open the Google Apps Script editor.\n\n2. **Install required libraries in Google Apps Script:**\n\n   - Go to \"Resources\" \u003e \"Libraries.\"\n   - Add the following libraries and enable them:\n     - \"PDF Utilities\" (Made by Google) - `1eMZo5aGKrYKj8JxnZSFWoH97nXG7HzO`\n     - \"Google Cloud Platform\" - `1B7FSrk5Zi6L1rSxxTDgDEUsPzlukDsi4KGuTMorsTQHhGBzBkM8_5U-H`\n     - \"Twilio API for JavaScript\" - `1j8XWnN5D4eS_rQXVbX33aRzK3-U3qJGn`\n\n3. **Create the function to extract data from PDFs:**\n\n   - In the Apps Script editor, replace the default code with the following:\n\n```js\nconst PDF_UTILITIES_ID = \u00271eMZo5aGKrYKj8JxnZSFWoH97nXG7HzO\u0027;\nconst GCP_ID = \u00271B7FSrk5Zi6L1rSxxTDgDEUsPzlukDsi4KGuTMorsTQHhGBzBkM8_5U-H\u0027;\nconst TWILIO_ID = \u00271j8XWnN5D4eS_rQXVbX33aRzK3-U3qJGn\u0027;\n\nconst PDF_UTILITIES = LibraryLoader.loadLibraryById(PDF_UTILITIES_ID);\nconst"
    ],
    "instruction": "How can I create an efficient and robust workflow that utilizes advanced automation techniques to extract targeted data, including customer information, from diverse PDF documents and effortlessly integrate it into a designated Google Sheet? Furthermore, I am interested in establishing a comprehensive and seamless system that promptly activates an SMS notification on my mobile device whenever a new PDF document is uploaded to the Google Sheet, ensuring real-time updates and enhanced accessibility.",
    "model_name": "meta-llama/Meta-Llama-3.1-70B-Instruct",
    "model_names": [
        "meta-llama/Meta-Llama-3-8B-Instruct",
        "mistralai/Mixtral-8x7B-Instruct-v0.1"
    ],
    "prompt": null,
    "prompt_id": null,
    "ratings": [
        null,
        null
    ],
    "rationales": [
        null,
        null
    ],
    "rejected": null,
    "rejected_model": null,
    "rejected_rating": null,
    "topic": "Software Development"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("JeganRavichandran/prompt-example", "format_text_generation_d_p_o_0")
```


</details>

<details><summary> Configuration: preference_to_argilla_0 </summary><hr>

```json
{
    "distilabel_metadata": {
        "raw_input_ultra_feedback_0": [
            {
                "content": "Your role is to evaluate text quality based on given criteria.\nYou\u0027ll receive an instructional description (\"Instruction\") and 2 text outputs (\"Text\").\nUnderstand and interpret instructions to evaluate effectively.\nProvide annotations for each text with a rating and rationale.\nThe 2 texts given are independent, and should be evaluated separately.\n",
                "role": "system"
            },
            {
                "content": "# General Text Quality Assessment\n\nEvaluate the model\u0027s outputs based on various criteria:\n\n1. **Correctness \u0026 Informativeness**: Does the output provide accurate and helpful information?\n2. **Honesty \u0026 Uncertainty**: How confidently does the model convey its information, and does it express uncertainty appropriately?\n3. **Truthfulness \u0026 Hallucination**: Does the model introduce misleading or fabricated details?\n4. **Instruction Following**: Does the model\u0027s output align with given instructions and the user\u0027s intent?\n\nYour role is to provide a holistic assessment considering all the above factors.\n\n**Scoring**: Rate outputs 1 to 5 based on the overall quality, considering all aspects:\n1. **Low Quality**: Contains inaccuracies, may be entirely wrong or has severe hallucinations.\n2. **Moderate Quality**: Addresses some aspects, but has errors or is partially aligned with instructions.\n3. **Good**: Generally accurate but may contain minor errors or slight deviations.\n4. **Very Good**: Near perfect, with minor issues in terms of alignment or confidence.\n5, **Excellent**: Accurate, confident, aligned with instructions, and free of hallucinations.\n\n## Format:\n\n### Input\nInstruction: [Clearly specify the task goal and restrictions]\n\nTexts:\n\u003ctext 1\u003e [Text 1]\n\u003ctext 2\u003e [Text 2]\n\n### Output\n#### Output for Text 1\nRating: [Rating for text 1]\nRationale: [Rationale for the rating in short sentences]\n\n#### Output for Text 2\nRating: [Rating]\nRationale: [Rationale]\n\n---\n\n## Annotation\n\n### Input\nInstruction: How can I create an efficient and robust workflow that utilizes advanced automation techniques to extract targeted data, including customer information, from diverse PDF documents and effortlessly integrate it into a designated Google Sheet? Furthermore, I am interested in establishing a comprehensive and seamless system that promptly activates an SMS notification on my mobile device whenever a new PDF document is uploaded to the Google Sheet, ensuring real-time updates and enhanced accessibility.\n\nTexts:\n\u003ctext 1\u003e What a fantastic question! Creating an efficient and robust workflow to extract data from PDFs and integrate it into a Google Sheet, along with real-time notifications, is a great use case. Here\u0027s a step-by-step guide to help you achieve this:\n\n**Step 1: Choose the right tools**\n\n1. **Google Apps Script**: This is the backbone of your automation. It\u0027s a scripting platform that allows you to automate tasks within Google Sheets.\n2. **PDF.co**: A powerful API that enables you to extract data from PDFs using OCR (Optical Character Recognition) and other techniques.\n3. **Google Cloud Storage**: To store and manage your PDF files.\n4. **Twilio**: A cloud communication platform that allows you to send SMS notifications.\n\n**Step 2: Set up the workflow**\n\n1. Create a new Google Sheet to store the extracted data.\n2. Set up a Google Cloud Storage bucket to store the PDF files.\n3. Use PDF.co\u0027s API to extract data from the PDFs. You can use their JavaScript library to do this.\n4. Create a Google Apps Script to trigger the PDF extraction process whenever a new PDF file is uploaded to the Cloud Storage bucket.\n5. Use the extracted data to populate the Google Sheet.\n\n**Step 3: Automate the data extraction process**\n\n1. In your Google Apps Script, create a function that uses the PDF.co API to extract data from the PDFs. You can use their JavaScript library to do this.\n2. Call this function whenever a new PDF file is uploaded to the Cloud Storage bucket.\n3. Use the extracted data to populate the corresponding Google Sheet.\n\n**Step 4: Set up real-time notifications**\n\n1. Use Twilio to send SMS notifications to your mobile device whenever a new PDF file is uploaded to the Cloud Storage bucket.\n2. Create a Twilio account and set up a new number for receiving SMS messages.\n3. In your Google Apps Script, use the Twilio API to send an SMS notification with a message indicating that a new PDF file has been uploaded.\n\n**Step 5: Integrate everything**\n\n1. Use Google Apps Script\u0027s `SpreadsheetApp` service to trigger the PDF extraction process when a new PDF file is uploaded to the Cloud Storage bucket.\n2. Use the `UrlFetchApp` service to make API requests to PDF.co and Twilio.\n3. Use the `PropertiesService` to store any necessary configuration settings, such as API keys and authentication tokens.\n\nHere\u0027s some sample code to get you started\n\u003ctext 2\u003e  To create an efficient and robust workflow that fulfills your requirements, you can utilize a combination of Google Apps Script, Google Sheets, and third-party services for automation and SMS notifications. Here\u0027s a step-by-step guide to setting up this workflow:\n\n1. **Prepare Google Sheets and Google Apps Script:**\n\n   - Create a new Google Sheet to store the extracted data.\n   - Click on \"Extensions\" \u003e \"Apps Script\" to open the Google Apps Script editor.\n\n2. **Install required libraries in Google Apps Script:**\n\n   - Go to \"Resources\" \u003e \"Libraries.\"\n   - Add the following libraries and enable them:\n     - \"PDF Utilities\" (Made by Google) - `1eMZo5aGKrYKj8JxnZSFWoH97nXG7HzO`\n     - \"Google Cloud Platform\" - `1B7FSrk5Zi6L1rSxxTDgDEUsPzlukDsi4KGuTMorsTQHhGBzBkM8_5U-H`\n     - \"Twilio API for JavaScript\" - `1j8XWnN5D4eS_rQXVbX33aRzK3-U3qJGn`\n\n3. **Create the function to extract data from PDFs:**\n\n   - In the Apps Script editor, replace the default code with the following:\n\n```js\nconst PDF_UTILITIES_ID = \u00271eMZo5aGKrYKj8JxnZSFWoH97nXG7HzO\u0027;\nconst GCP_ID = \u00271B7FSrk5Zi6L1rSxxTDgDEUsPzlukDsi4KGuTMorsTQHhGBzBkM8_5U-H\u0027;\nconst TWILIO_ID = \u00271j8XWnN5D4eS_rQXVbX33aRzK3-U3qJGn\u0027;\n\nconst PDF_UTILITIES = LibraryLoader.loadLibraryById(PDF_UTILITIES_ID);\nconst\n\n### Output\n",
                "role": "user"
            }
        ],
        "raw_output_ultra_feedback_0": null
    },
    "generations": [
        "What a fantastic question! Creating an efficient and robust workflow to extract data from PDFs and integrate it into a Google Sheet, along with real-time notifications, is a great use case. Here\u0027s a step-by-step guide to help you achieve this:\n\n**Step 1: Choose the right tools**\n\n1. **Google Apps Script**: This is the backbone of your automation. It\u0027s a scripting platform that allows you to automate tasks within Google Sheets.\n2. **PDF.co**: A powerful API that enables you to extract data from PDFs using OCR (Optical Character Recognition) and other techniques.\n3. **Google Cloud Storage**: To store and manage your PDF files.\n4. **Twilio**: A cloud communication platform that allows you to send SMS notifications.\n\n**Step 2: Set up the workflow**\n\n1. Create a new Google Sheet to store the extracted data.\n2. Set up a Google Cloud Storage bucket to store the PDF files.\n3. Use PDF.co\u0027s API to extract data from the PDFs. You can use their JavaScript library to do this.\n4. Create a Google Apps Script to trigger the PDF extraction process whenever a new PDF file is uploaded to the Cloud Storage bucket.\n5. Use the extracted data to populate the Google Sheet.\n\n**Step 3: Automate the data extraction process**\n\n1. In your Google Apps Script, create a function that uses the PDF.co API to extract data from the PDFs. You can use their JavaScript library to do this.\n2. Call this function whenever a new PDF file is uploaded to the Cloud Storage bucket.\n3. Use the extracted data to populate the corresponding Google Sheet.\n\n**Step 4: Set up real-time notifications**\n\n1. Use Twilio to send SMS notifications to your mobile device whenever a new PDF file is uploaded to the Cloud Storage bucket.\n2. Create a Twilio account and set up a new number for receiving SMS messages.\n3. In your Google Apps Script, use the Twilio API to send an SMS notification with a message indicating that a new PDF file has been uploaded.\n\n**Step 5: Integrate everything**\n\n1. Use Google Apps Script\u0027s `SpreadsheetApp` service to trigger the PDF extraction process when a new PDF file is uploaded to the Cloud Storage bucket.\n2. Use the `UrlFetchApp` service to make API requests to PDF.co and Twilio.\n3. Use the `PropertiesService` to store any necessary configuration settings, such as API keys and authentication tokens.\n\nHere\u0027s some sample code to get you started",
        " To create an efficient and robust workflow that fulfills your requirements, you can utilize a combination of Google Apps Script, Google Sheets, and third-party services for automation and SMS notifications. Here\u0027s a step-by-step guide to setting up this workflow:\n\n1. **Prepare Google Sheets and Google Apps Script:**\n\n   - Create a new Google Sheet to store the extracted data.\n   - Click on \"Extensions\" \u003e \"Apps Script\" to open the Google Apps Script editor.\n\n2. **Install required libraries in Google Apps Script:**\n\n   - Go to \"Resources\" \u003e \"Libraries.\"\n   - Add the following libraries and enable them:\n     - \"PDF Utilities\" (Made by Google) - `1eMZo5aGKrYKj8JxnZSFWoH97nXG7HzO`\n     - \"Google Cloud Platform\" - `1B7FSrk5Zi6L1rSxxTDgDEUsPzlukDsi4KGuTMorsTQHhGBzBkM8_5U-H`\n     - \"Twilio API for JavaScript\" - `1j8XWnN5D4eS_rQXVbX33aRzK3-U3qJGn`\n\n3. **Create the function to extract data from PDFs:**\n\n   - In the Apps Script editor, replace the default code with the following:\n\n```js\nconst PDF_UTILITIES_ID = \u00271eMZo5aGKrYKj8JxnZSFWoH97nXG7HzO\u0027;\nconst GCP_ID = \u00271B7FSrk5Zi6L1rSxxTDgDEUsPzlukDsi4KGuTMorsTQHhGBzBkM8_5U-H\u0027;\nconst TWILIO_ID = \u00271j8XWnN5D4eS_rQXVbX33aRzK3-U3qJGn\u0027;\n\nconst PDF_UTILITIES = LibraryLoader.loadLibraryById(PDF_UTILITIES_ID);\nconst"
    ],
    "instruction": "How can I create an efficient and robust workflow that utilizes advanced automation techniques to extract targeted data, including customer information, from diverse PDF documents and effortlessly integrate it into a designated Google Sheet? Furthermore, I am interested in establishing a comprehensive and seamless system that promptly activates an SMS notification on my mobile device whenever a new PDF document is uploaded to the Google Sheet, ensuring real-time updates and enhanced accessibility.",
    "model_name": "meta-llama/Meta-Llama-3.1-70B-Instruct",
    "model_names": [
        "meta-llama/Meta-Llama-3-8B-Instruct",
        "mistralai/Mixtral-8x7B-Instruct-v0.1"
    ],
    "ratings": [
        null,
        null
    ],
    "rationales": [
        null,
        null
    ],
    "topic": "Software Development"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("JeganRavichandran/prompt-example", "preference_to_argilla_0")
```


</details>





## References


```
@misc{cui2024ultrafeedbackboostinglanguagemodels,
    title={UltraFeedback: Boosting Language Models with Scaled AI Feedback},
    author={Ganqu Cui and Lifan Yuan and Ning Ding and Guanming Yao and Bingxiang He and Wei Zhu and Yuan Ni and Guotong Xie and Ruobing Xie and Yankai Lin and Zhiyuan Liu and Maosong Sun},
    year={2024},
    eprint={2310.01377},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2310.01377},
}
```


