---
dataset_info:
  features:
  - name: question_id
    dtype: int64
  - name: question
    dtype: string
  - name: text
    dtype: string
  - name: label
    dtype: int64
  splits:
  - name: validation
    num_bytes: 7885383.303442969
    num_examples: 20000
  - name: train
    num_bytes: 3571579491.696557
    num_examples: 9058734
  download_size: 2330139568
  dataset_size: 3579464875.0
configs:
- config_name: default
  data_files:
  - split: validation
    path: data/validation-*
  - split: train
    path: data/train-*
---

**Dataset Description**

This dataset is crafted for training a binary classification model to determine whether a given text passage answers a specific user query. Its primary purpose is to enhance our search engine by filtering out irrelevant passages, ensuring that users receive accurate and helpful responses to their questions.

**Background and Motivation**

In our search engine, users submit queries and receive multiple passages as results. Not all retrieved passages effectively answer the user's question, leading to a suboptimal user experience. To address this, we need a model capable of assessing each passage's relevance to the query, allowing us to present only the most pertinent information.

**Source Dataset**

The dataset is based on the [MS MARCO V2.1](https://github.com/zhouyonglong/MSMARCOV2) dataset from Microsoft, accessed via [Hugging Face Datasets](https://huggingface.co/datasets/microsoft/ms_marco) (version 2.1). MS MARCO is a large-scale corpus designed for machine reading comprehension and question answering tasks, containing real anonymized user queries and corresponding passages from web documents.

**Dataset Construction**

- **Original Format**: Each sample in MS MARCO V2.1 consists of:
  - A **query** (user's question).
  - A set of **10 passages** retrieved for that query.
  - **Labels** indicating whether each passage was selected as an answer.

- **Transformation Process**:
  - **Reshaping**: We transformed the dataset to suit a binary classification task by iterating over each passage in every sample.
  - **Sample Creation**: For each query-passage pair, we created a new sample with:
    - `question_id`: Unique identifier for the query.
    - `question`: The user's query.
    - `text`: The passage text.
    - `label`: Binary label (`1` if the passage answers the question, `0` otherwise).

- **Dataset Splitting**:
  - Combined the original **train** and **validation** splits, excluding the **test** split.
  - Shuffled the combined dataset to ensure randomness.
  - **Validation Set Size**: Determined based on statistical calculations to ensure a sufficient sample size for reliable validation metrics:
    - **Accuracy Assumption**: 90%
    - **Margin of Error**: 0.5%
    - **Confidence Level**: 98% (z-score of 2.326)
    - **Calculated Validation Size**: Approximately 20,000 samples
  - Split the dataset into:
    - **Training Set**: Remaining samples after allocating 20,000 to validation.
    - **Validation Set**: 20,000 samples for model evaluation.

**Dataset Features**

- `question_id` (string): Unique identifier for each query.
- `question` (string): The user's query.
- `text` (string): The text of the passage.
- `label` (int): Binary indicator (`1` if the passage answers the question, `0` otherwise).

**Intended Use**

- **Primary Task**: Train a binary classification model to predict whether a passage answers a given query.
- **Application**: Integrate the model into our search engine pipeline to filter out non-relevant passages, improving the overall quality and relevance of search results.

**Considerations**

- **Class Balance**: The dataset may be imbalanced due to the nature of the original labels. It's important to consider this during model training (e.g., using class weights or resampling techniques).
- **Data Quality**: Passages are sourced from real-world search data and may contain noise or irrelevant information typical of web content.
- **Licensing**: Ensure compliance with MS MARCO's licensing terms when using this dataset for development or distribution.

**Conclusion**

This dataset aligns closely with our goal of improving answerability assessment in search results. By leveraging real user queries and associated passages, the trained model will be well-suited to judge the relevance of passages retrieved by our search engine, ultimately enhancing user satisfaction by providing accurate and relevant answers.

**Additional Notes**

- **Dataset Location**: Stored internally within our dataset repository for easy access and version control.
- **Reproducibility**: The notebook used to create this dataset is available and contains all steps for dataset generation, allowing for updates or modifications as needed.
- **Future Work**: Consider exploring sequence classification tasks or more complex models to further improve answerability predictions.