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: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': technical-indicator
          '1': stock-price
          '2': risk-management
          '3': investor-sentiment
          '4': real-time-data
          '5': market-trend
          '6': informed-decision-making
  splits:
  - name: train
    num_bytes: 164137
    num_examples: 500
  download_size: 58344
  dataset_size: 164137
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-55a6b53b

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/ShyamaHF/my-distiset-55a6b53b/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/ShyamaHF/my-distiset-55a6b53b/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 1,
    "text": "The stock price of Apple is expected to surge in the next quarter due to the launch of its new iPhone model, which is anticipated to generate significant revenue. Market analysts predict a 15% increase in the company\u0027s stock price by the end of the year."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("ShyamaHF/my-distiset-55a6b53b", "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("ShyamaHF/my-distiset-55a6b53b")
```


</details>




