license:unknown size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
license: unknown
size_categories: n<1K
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-*
dataset_info:
- config_name: generate_reranking_pairs
  features:
  - name: filename
    dtype: string
  - name: anchor
    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: 60144
    num_examples: 15
  download_size: 38547
  dataset_size: 60144
- config_name: generate_retrieval_pairs
  features:
  - name: filename
    dtype: string
  - name: anchor
    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: 63441
    num_examples: 15
  download_size: 39229
  dataset_size: 63441
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/Meleagrista/example-retrieval-reranking-dataset/raw/main/pipeline.yaml"
```

or explore the configuration:

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

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "anchor": "UNITED STATES\nSECURITIES AND EXCHANGE COMMISSIONWashington, D.C. 20549 FORM 10-K (Mark One)\u2612ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934For the fiscal year ended December 31, 2021 OR \u2610TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934 FOR THE TRANSITION PERIODFROM                      TOCommission File Number 001-38846 Lyft, Inc.(Exact name of Registrant as specified in its Charter)Delaware20-8809830 (State or other jurisdiction ofincorporation or organization)(I.R.S. Employer Identification No.",
    "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 an excerpt from a FORM 10-K annual report filed with the United States Securities and Exchange Commission (SEC). \nThe document includes detailed financial and operational information about Lyft, Inc., a publicly traded company, for the fiscal year ended December 31, 2021. \nIt may contain sections on business strategy, risk factors, financial statements, management discussion and analysis, and regulatory disclosures. \nThis legal and financial document adheres to SEC reporting requirements.\n\n\n## Anchor\n\nUNITED STATES\nSECURITIES AND EXCHANGE COMMISSIONWashington, D.C. 20549 FORM 10-K (Mark One)\u2612ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934For the fiscal year ended December 31, 2021 OR \u2610TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934 FOR THE TRANSITION PERIODFROM                      TOCommission File Number 001-38846 Lyft, Inc.(Exact name of Registrant as specified in its Charter)Delaware20-8809830 (State or other jurisdiction ofincorporation or organization)(I.R.S. Employer Identification No.\n",
                "role": "user"
            }
        ],
        "raw_output_generate_reranking_pairs": " ## Positive\nLyft, Inc. successfully filed its FORM 10-K report for the fiscal year ended December 31, 2021, providing comprehensive financial and operational information to the United States Securities and Exchange Commission (SEC), effectively meeting regulatory requirements.\n\n## Negative\nDespite the filing deadline, Lyft, Inc. failed to submit its FORM 10-K report for the fiscal year ended December 31, 2021, leading to potential regulatory consequences and uncertainty for investors."
    },
    "filename": ".data/lyft_2021.pdf",
    "model_name": "mistralai/Mistral-7B-Instruct-v0.2",
    "negative": "Despite the filing deadline, Lyft, Inc. failed to submit its FORM 10-K report for the fiscal year ended December 31, 2021, leading to potential regulatory consequences and uncertainty for investors.",
    "positive": "Lyft, Inc. successfully filed its FORM 10-K report for the fiscal year ended December 31, 2021, providing comprehensive financial and operational information to the United States Securities and Exchange Commission (SEC), effectively meeting regulatory requirements."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

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


</details>

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

```json
{
    "anchor": "UNITED STATES\nSECURITIES AND EXCHANGE COMMISSIONWashington, D.C. 20549 FORM 10-K (Mark One)\u2612ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934For the fiscal year ended December 31, 2021 OR \u2610TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934 FOR THE TRANSITION PERIODFROM                      TOCommission File Number 001-38846 Lyft, Inc.(Exact name of Registrant as specified in its Charter)Delaware20-8809830 (State or other jurisdiction ofincorporation or organization)(I.R.S. Employer Identification No.",
    "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 an excerpt from a FORM 10-K annual report filed with the United States Securities and Exchange Commission (SEC). \nThe document includes detailed financial and operational information about Lyft, Inc., a publicly traded company, for the fiscal year ended December 31, 2021. \nIt may contain sections on business strategy, risk factors, financial statements, management discussion and analysis, and regulatory disclosures. \nThis legal and financial document adheres to SEC reporting requirements.\n\n\n## Anchor\n\nUNITED STATES\nSECURITIES AND EXCHANGE COMMISSIONWashington, D.C. 20549 FORM 10-K (Mark One)\u2612ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934For the fiscal year ended December 31, 2021 OR \u2610TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934 FOR THE TRANSITION PERIODFROM                      TOCommission File Number 001-38846 Lyft, Inc.(Exact name of Registrant as specified in its Charter)Delaware20-8809830 (State or other jurisdiction ofincorporation or organization)(I.R.S. Employer Identification No.\n",
                "role": "user"
            }
        ],
        "raw_output_generate_retrieval_pairs": " ## Positive\nIn this 10-K report, Lyft, Inc. discloses its financial performance and business strategies for the fiscal year ended December 31, 2021, in compliance with SEC regulations.\n\n## Negative\nDespite filing a 10-K report with the SEC for the fiscal year ended December 31, 2021, Lyft, Inc. failed to disclose any meaningful information about its financial performance or business strategies, misleading investors and regulatory bodies."
    },
    "filename": ".data/lyft_2021.pdf",
    "model_name": "mistralai/Mistral-7B-Instruct-v0.2",
    "negative": "Despite filing a 10-K report with the SEC for the fiscal year ended December 31, 2021, Lyft, Inc. failed to disclose any meaningful information about its financial performance or business strategies, misleading investors and regulatory bodies.",
    "positive": "In this 10-K report, Lyft, Inc. discloses its financial performance and business strategies for the fiscal year ended December 31, 2021, in compliance with SEC regulations."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

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


</details>




