size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': mri-equipment
          '1': mri-limitations
          '2': mri-contrast-agents
          '3': mri-risks
          '4': mri-diagnosis
          '5': mri-imaging
          '6': mri-benefits
          '7': mri-technique
  splits:
  - name: train
    num_bytes: 58922
    num_examples: 200
  download_size: 25671
  dataset_size: 58922
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---

# Dataset Card for mri_techniques




## 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/Taylor658/mri_techniques/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/Taylor658/mri_techniques/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 5,
    "text": "Magnetic Resonance Imaging (MRI) scans have revolutionized the field of medicine, offering doctors a non-invasive method to visualize internal body structures in unprecedented detail. This technique uses strong magnetic fields, radio waves, and the nucleus of hydrogen atoms to create high-quality images."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("Taylor658/mri_techniques", "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("Taylor658/mri_techniques")
```


</details>




