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': '0'
          '1': '1'
  splits:
  - name: train
    num_bytes: 19505
    num_examples: 20
  download_size: 14759
  dataset_size: 19505
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 RP_Data_3

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/lallu-lalla-ji/RP_Data_3/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/lallu-lalla-ji/RP_Data_3/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 present study aims to investigate the efficacy of a novel polymer-based nanocarrier system in delivering siRNA molecules to cancer cells, thereby inhibiting the expression of specific oncogenes. The nanocarriers were fabricated via a combination of emulsification and solvent evaporation methods, resulting in nanoparticles with an average diameter of 150 nm. The siRNA loading capacity was evaluated using fluorescence spectroscopy, and the results showed a significant increase in siRNA uptake by cancer cells treated with the nanocarriers. Furthermore, the in vitro cytotoxicity of the nanocarriers was assessed using MTT assays, and the data revealed minimal toxicity towards healthy cells. The results of this study demonstrate the potential of the polymer-based nanocarrier system as a promising tool for cancer therapy, warranting further investigation into its application in vivo. Notably, the nanocarriers\u0027 ability to selectively target cancer cells while minimizing damage to healthy tissues may mitigate the adverse effects associated with traditional chemotherapy."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("lallu-lalla-ji/RP_Data_3", "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("lallu-lalla-ji/RP_Data_3")
```


</details>




