task_categories:text-classification 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
task_categories:
- text-classification
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': high-conversion-likelihood
          '1': low-conversion-likelihood
  splits:
  - name: train
    num_bytes: 2932
    num_examples: 10
  download_size: 3839
  dataset_size: 2932
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 my-distiset-9bbeaa0e

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/phil2/my-distiset-9bbeaa0e/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/phil2/my-distiset-9bbeaa0e/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 0,
    "text": "The CEO of XYZ Corporation, a mid-sized technology firm with 200 employees, has expressed interest in implementing our software solution to streamline their sales process. The CIO has also reached out to our sales team to discuss potential integration with their existing CRM system. The company is in the IT industry and was referred to us by a trusted partner."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("phil2/my-distiset-9bbeaa0e", "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("phil2/my-distiset-9bbeaa0e")
```


</details>




