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---
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.
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