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:
- config_name: generate_reranking_pairs
  features:
  - name: filename
    dtype: string
  - name: anchor
    dtype: string
  - name: repo_name
    dtype: string
  - name: positive
    dtype: string
  - name: negative
    dtype: string
  - name: distilabel_metadata
    struct:
    - name: raw_input_generate_reranking_pairs
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    - name: raw_output_generate_reranking_pairs
      dtype: string
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 39508
    num_examples: 15
  download_size: 36985
  dataset_size: 39508
- config_name: generate_retrieval_pairs
  features:
  - name: filename
    dtype: string
  - name: anchor
    dtype: string
  - name: repo_name
    dtype: string
  - name: positive
    dtype: string
  - name: negative
    dtype: string
  - name: distilabel_metadata
    struct:
    - name: raw_input_generate_retrieval_pairs
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    - name: raw_output_generate_retrieval_pairs
      dtype: string
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 38355
    num_examples: 15
  download_size: 30713
  dataset_size: 38355
configs:
- config_name: generate_reranking_pairs
  data_files:
  - split: train
    path: generate_reranking_pairs/train-*
- config_name: generate_retrieval_pairs
  data_files:
  - split: train
    path: generate_retrieval_pairs/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 example-retrieval-reranking-dataset

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/aniruddha007/example-retrieval-reranking-dataset/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/aniruddha007/example-retrieval-reranking-dataset/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: generate_retrieval_pairs </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_input_generate_retrieval_pairs": [
            {
                "content": "Your task is to generate a positive and a negative sentence given an anchor sentence. Take into account the context given. The positive sentence has to be a query for the anchor sentence, while the negative sentence is a \u0027hard negative\u0027 that meets the following criteria:\n- Uses similar keywords or phrases as the anchor sentence\n- Has a similar grammatical structure or syntax\n- Is not related to the anchor sentence, but could be mistaken for it\nTry to create a negative sentence that would be challenging for a model to distinguish from the positive sentence. You must output only two new sections: `## Positive` and `## Negative`.",
                "role": "system"
            },
            {
                "content": "## Context\n\n\nThe text is a chunk from technical Python SDK documentation of Argilla.\nArgilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets.\nAlong with prose explanations, the text chunk may include code snippets and Python references.\n\n\n## Anchor\n\ndescription: 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.\n",
                "role": "user"
            }
        ],
        "raw_output_generate_retrieval_pairs": "## Positive\nWhat is Argilla, the collaboration tool for AI engineers and domain experts, that offers high-quality outputs, full data ownership, and overall efficiency?\n\n## Negative\nArgilla is a collaboration platform for AI engineers and domain experts that demand low-quality outputs, limited data ownership, and overall inefficiency."
    },
    "filename": "argilla-python/docs/index.md",
    "model_name": "mistralai/Mistral-7B-Instruct-v0.3",
    "negative": "Argilla is a collaboration platform for AI engineers and domain experts that demand low-quality outputs, limited data ownership, and overall inefficiency.",
    "positive": "What is Argilla, the collaboration tool for AI engineers and domain experts, that offers high-quality outputs, full data ownership, and overall efficiency?",
    "repo_name": "argilla-io/argilla-python"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("aniruddha007/example-retrieval-reranking-dataset", "generate_retrieval_pairs")
```


</details>

<details><summary> Configuration: generate_reranking_pairs </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_input_generate_reranking_pairs": [
            {
                "content": "Your task is to generate a positive and a negative sentence given an anchor sentence. Take into account the context given. The positive sentence has to be semantically similar to the anchor sentence, while the negative sentence can use similar words but must not be related to the anchor sentence. You must output only two new sections: `## Positive` and `## Negative`.",
                "role": "system"
            },
            {
                "content": "## Context\n\n\nThe text is a chunk from technical Python SDK documentation of Argilla.\nArgilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets.\nAlong with prose explanations, the text chunk may include code snippets and Python references.\n\n\n## Anchor\n\ndescription: 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.\n",
                "role": "user"
            }
        ],
        "raw_output_generate_reranking_pairs": "## Positive\nArgilla offers an efficient and collaborative environment for AI engineers and domain experts to produce superior quality datasets while maintaining full control over their data.\n\n## Negative\nUnfortunately, Argilla does not support real-time data streaming or AI model training, making it less suitable for certain fast-paced AI project requirements."
    },
    "filename": "argilla-python/docs/index.md",
    "model_name": "mistralai/Mistral-7B-Instruct-v0.3",
    "negative": "Unfortunately, Argilla does not support real-time data streaming or AI model training, making it less suitable for certain fast-paced AI project requirements.",
    "positive": "Argilla offers an efficient and collaborative environment for AI engineers and domain experts to produce superior quality datasets while maintaining full control over their data.",
    "repo_name": "argilla-io/argilla-python"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("aniruddha007/example-retrieval-reranking-dataset", "generate_reranking_pairs")
```


</details>




