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: completion
    dtype: string
  - name: meta
    struct:
    - name: category
      dtype: string
    - name: completion
      dtype: string
    - name: id
      dtype: int64
    - name: input
      dtype: 'null'
    - name: motivation_app
      dtype: 'null'
    - name: prompt
      dtype: string
    - name: source
      dtype: string
    - name: subcategory
      dtype: string
  - name: embedding
    sequence: float64
  splits:
  - name: train
    num_bytes: 98573
    num_examples: 10
  download_size: 101180
  dataset_size: 98573
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---

<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 distilabel-sentence-transformers

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/gabrielmbmb/distilabel-sentence-transformers/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/gabrielmbmb/distilabel-sentence-transformers/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "completion": "Denote the number of chocolates each person has by the letter of their first name. We know that\nA = D + 12\nD = R + 6\nA = 2 * R\n\nThus, A = (R + 6) + 12 = R + 18\nSince also A = 2 * R, this means 2 * R = R + 18\nHence R = 18\nHence D = 18 + 6 = 24",
    "embedding": [
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    ],
    "meta": {
        "category": "Question Answering",
        "completion": "Denote the number of chocolates each person has by the letter of their first name. We know that\nA = D + 12\nD = R + 6\nA = 2 * R\n\nThus, A = (R + 6) + 12 = R + 18\nSince also A = 2 * R, this means 2 * R = R + 18\nHence R = 18\nHence D = 18 + 6 = 24",
        "id": 0,
        "input": null,
        "motivation_app": null,
        "prompt": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?",
        "source": "surge",
        "subcategory": "Math"
    },
    "text": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("gabrielmbmb/distilabel-sentence-transformers", "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("gabrielmbmb/distilabel-sentence-transformers")
```


</details>
