size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif datacraft
---
size_categories: n<1K
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: completion
    dtype: string
  - name: system_prompt
    dtype: string
  splits:
  - name: train
    num_bytes: 29901
    num_examples: 10
  download_size: 30601
  dataset_size: 29901
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
---

<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 synthetic-sft-customer-support-single-turn

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/davidberenstein1957/synthetic-sft-customer-support-single-turn/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/davidberenstein1957/synthetic-sft-customer-support-single-turn/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "completion": "For generating synthetic text data for your rental accommodation description classification model, I recommend using the **Text Expansion technique** from the Synthetic Data Generator tool.\n\nText Expansion is particularly effective for this project because it involves adding context or information to the existing text, which can help the model learn to recognize relevant features that contribute to the approval or rejection decision. This technique can also help to:\n\n1.  **Increase the diversity of the data**: By adding more context or information, you can create new text samples that are similar to the existing ones but contain additional details.\n2.  **Improve model generalizability**: The expanded text data can help the model learn to recognize patterns and features that are not present in the original data, making it more robust and less prone to overfitting.\n3.  **Enhance the realism of the data**: By adding context or information that is consistent with the rental accommodation domain, you can create more realistic text samples that better reflect the characteristics of real-world listings.\n\nSome possible text expansion techniques you can use include:\n\n1.  **Adding amenities**: Expand the text to include additional amenities offered by the rental, such as \"free Wi-Fi\" or \"fully equipped kitchen\".\n2.  **Providing additional location information**: Add details about the location, such as \"close to public transportation\" or \"walking distance to local attractions\".\n3.  **Including reviews or ratings**: Expand the text to include reviews or ratings from previous renters, such as \"4-star rating\" or \"excellent customer service\".\n4.  **Adding multimedia content**: Include images, videos, or other multimedia elements to provide a more immersive experience.\n\nTo get started, you can use the Synthetic Data Generator tool to generate expanded text samples and then fine-tune them to meet your specific requirements. You can also use the other text augmentation techniques (e.g., transformation, translation, noise generation) in combination with text expansion to further enhance the diversity and realism of your synthetic data.\n\nHere is a step-by-step guide to using the Synthetic Data Generator tool for text expansion:\n\n1.  **Choose the input data**: Select the rental accommodation description text data that you want to expand.\n2.  **Select the expansion technique**: Choose the text expansion technique you want to use (e.g., add amenities, provide additional location information).\n3.  **Configure the parameters**: Adjust the parameters for the expansion technique, such as the number of additional details to add or the type of amenities to include.\n4.  **Generate the expanded data**: Use the Synthetic Data Generator tool to generate the expanded text data.\n5.  **Evaluate and fine-tune**: Review the generated data and fine-tune it if necessary to ensure it meets your requirements.\n\nBy leveraging text expansion and the Synthetic Data Generator tool, you can create a larger and more diverse dataset for your rental accommodation description classification model, which can improve its accuracy and generalizability.",
    "prompt": "I am developing a machine learning model to classify text in a rental accommodation description. The model is intended to automate decision-making on whether to approve or reject the listing of a new rental.\n\nPlease recommend a suitable technique for generating synthetic data for your project.\n\nFor generating text data for your project, one suitable technique would be to use the \"Text Augmentation\" technique in the Synthetic Data Generator.\n\nText augmentation involves:\n\n1.  **Text Transformation:** Rearranging the words or phrases to create variations of existing content. This can be done by modifying word order, inserting or deleting words, or replacing words with synonyms.\n2.  **Word Translation:** Translating the existing text into other languages to generate new text.\n3. **Text Expansion:** Increasing the size of the text by adding additional context or information.\n4.  **Noise Generation:** Introducing errors into the text, such as typos, grammar errors, or missing information.\n\nBy applying these techniques, you can increase the size of the training dataset while maintaining the diversity of the data. This can be particularly helpful when dealing with limited data.",
    "system_prompt": "You are an AI assistant specialising in generating high-quality training data for machine learning models. Your purpose is to assist users in creating synthetic data for various applications using the Synthetic Data Generator tool by Argilla. Provide accurate information on data generation techniques. Facilitates efficient model training. User questions are direct and concise."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("davidberenstein1957/synthetic-sft-customer-support-single-turn", "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("davidberenstein1957/synthetic-sft-customer-support-single-turn")
```


</details>




