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: 12015
    num_examples: 10
  download_size: 12254
  dataset_size: 12015
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_2

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_2/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/lallu-lalla-ji/RP_Data_2/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 1,
    "text": "Recent advancements in machine learning have led to the development of sophisticated deep neural networks capable of extracting complex patterns from large datasets. This study employed a convolutional neural network (CNN) to analyze the structural characteristics of protein-ligand interactions. The methodology involved pre-training the CNN model on a dataset of 100,000 protein sequences, followed by fine-tuning on a subset of 10,000 ligand-binding protein structures. The results indicated a significant correlation between the predicted binding affinity and the experimental data, with an R-squared value of 0.85. Furthermore, the analysis revealed that the CNN model was able to identify key residues involved in ligand binding, consistent with previous studies. The findings suggest that the proposed approach can be used to predict the binding affinity of protein-ligand interactions with high accuracy, paving the way for the discovery of novel therapeutics."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("lallu-lalla-ji/RP_Data_2", "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_2")
```


</details>




