size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: conversation_id
    dtype: int64
  - name: email
    dtype: string
  - name: maximum_brevity_summary
    dtype: string
  - name: summary
    dtype: string
  - name: distilabel_metadata
    struct:
    - name: raw_input_email_summarization_0
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    - name: raw_output_email_summarization_0
      dtype: string
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 385285
    num_examples: 100
  download_size: 144018
  dataset_size: 385285
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 Email-Summaries

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


The pipeline script was uploaded to easily reproduce the dataset:
[email_summaries.py](https://huggingface.co/datasets/gabrielmbmb/Email-Summaries/raw/main/email_summaries.py).

It can be run directly using the CLI:

```console
distilabel pipeline run --script "https://huggingface.co/datasets/gabrielmbmb/Email-Summaries/raw/main/email_summaries.py"
```


## 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/gabrielmbmb/Email-Summaries/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/gabrielmbmb/Email-Summaries/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "conversation_id": 0,
    "distilabel_metadata": {
        "raw_input_email_summarization_0": [
            {
                "content": "You are an AI assistant designed to summarize emails for the recipient of the email. Your task is to create concise, objective summaries that capture the essential information communicated by the sender, from the recipient\u0027s perspective but without directly addressing or mentioning the recipient.\n\n## Key points\n\n1. Do not use phrases like \"you\" or \"the recipient\" in the summary.\n2. Do not use the recipient name.\n3. Do not use the third person.\n4. Focus on the sender\u0027s actions and intentions.\n5. Summarize as if describing the email to a third party.\n\nFor example, instead of \"Alex is reaching out to you to collaborate\" or \"Alex is reaching out Samantha to collaborate\", use \"Alex is reaching out to collaborate\".\n\n## Output Requirements\n\nProvide two types of summaries:\n1. A maximum brevity summary: extract the main key point of the conversation and present it in one very very short sentence. Include details such as dates, cities, venues, etc if required.\n2. A more detailed summary (up to three sentences).\n\n## Output Format:\n\n```markdown\n## Maximum brevity summary\n\n[One-sentence summary here]\n\n## Summary\n\n[Up to three-sentence summary here]\n```\n",
                "role": "system"
            },
            {
                "content": "Subject: Virtual Field Trip Collaboration\n\nHey Michael,\n\nI hope you\u0027re doing well! I\u0027ve been thinking a lot about the ideas we discussed at the conference and I think I have an idea for our collaboration project. What if we created a virtual field trip that takes students on a journey through different cities around the world, exploring the impacts of climate change on urban environments? \n\nI think this could be a great way to combine our expertise in environmental and urban geography and create an engaging learning experience for our students. Let me know what you think!\n\nBest,\nSarah",
                "role": "user"
            }
        ],
        "raw_output_email_summarization_0": "## Maximum brevity summary\n\nSarah proposes a virtual field trip project to explore climate change impacts in urban environments.\n\n## Summary\n\nSarah suggests collaborating on a virtual field trip project that would take students through different cities to explore the impacts of climate change on urban environments. She believes this project could leverage both experts\u0027 knowledge in environmental and urban geography and provide an engaging learning experience. Sarah is seeking feedback on the idea."
    },
    "email": "Subject: Virtual Field Trip Collaboration\n\nHey Michael,\n\nI hope you\u0027re doing well! I\u0027ve been thinking a lot about the ideas we discussed at the conference and I think I have an idea for our collaboration project. What if we created a virtual field trip that takes students on a journey through different cities around the world, exploring the impacts of climate change on urban environments? \n\nI think this could be a great way to combine our expertise in environmental and urban geography and create an engaging learning experience for our students. Let me know what you think!\n\nBest,\nSarah",
    "maximum_brevity_summary": "Sarah proposes a virtual field trip project to explore climate change impacts in urban environments.",
    "model_name": "Qwen/Qwen2.5-72B-Instruct",
    "summary": "Sarah suggests collaborating on a virtual field trip project that would take students through different cities to explore the impacts of climate change on urban environments. She believes this project could leverage both experts\u0027 knowledge in environmental and urban geography and provide an engaging learning experience. Sarah is seeking feedback on the idea."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("gabrielmbmb/Email-Summaries", "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("gabrielmbmb/Email-Summaries")
```


</details>




