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: filename
    dtype: string
  - name: anchor
    dtype: string
  - name: repo_name
    dtype: string
  - name: negative
    dtype: string
  - name: distilabel_metadata
    struct:
    - name: raw_output_multiply_queries
      dtype: string
  - name: model_name_query
    dtype: string
  - name: model_name_query_multiplied
    dtype: string
  - name: positive
    dtype: string
  splits:
  - name: train
    num_bytes: 55824
    num_examples: 51
  download_size: 20753
  dataset_size: 55824
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 argilla_sdk_distilabel_docs_queries

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/plaguss/argilla_sdk_distilabel_docs_queries/raw/main/pipeline.yaml"
```

or explore the configuration:

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

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "anchor": "description: Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.\nhide: navigation\n\nWelcome to Argilla\n\nArgilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.",
    "distilabel_metadata": {
        "raw_output_multiply_queries": "- How can Argilla\u0027s collaboration platform guarantee precise annotations for AI model training?\n- Does Argilla\u0027s platform facilitate seamless collaboration to produce high-accuracy AI datasets?\n- Can Argilla\u0027s collaborative approach ensure domain experts and AI engineers work in tandem to generate top-notch AI models?"
    },
    "filename": "argilla-python/docs/index.md",
    "model_name_query": "meta-llama/Meta-Llama-3-70B-Instruct",
    "model_name_query_multiplied": "meta-llama/Meta-Llama-3-70B-Instruct",
    "negative": "After a long hike, I finally reached the argilla-rich terrain, perfect for pottery making.",
    "positive": "Can Argilla\u0027s collaboration platform ensure high-quality outputs for AI engineers and domain experts?",
    "repo_name": "argilla-io/argilla-python"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("plaguss/argilla_sdk_distilabel_docs_queries", "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("plaguss/argilla_sdk_distilabel_docs_queries")
```


</details>
