# FAQ answering bot based on Talking Stage Dataset — Data

来源：https://www.kaggle.com/competitions/faq-answering-bot-based-on-talking-stage-dataset/data

采集时间：2026-10-24T18:31:11Z

# Dataset: training_data.csv

## Overview

The `training_data.csv` dataset is designed to train machine learning models for the **FAQ Answering Bot** competition. This dataset is derived from the Talking Stage application and contains labeled data that helps in predicting appropriate responses to user queries.

## Structure

The dataset consists of 3522 entries, each representing a unique user query and its corresponding label. The CSV file includes the following columns:

- **Text**: The text of the user query or phrase.
- **Label**: The category or intent of the query, which corresponds to the key in the responses dictionary within the Python console application.

## Columns

1. **Text**
   - Type: String
   - Description: The text of the user query or phrase. This column contains various queries or phrases that users might ask, which need to be understood and categorized by the model.

2. **Label**
   - Type: String
   - Description: The category or intent of the query. This column provides the ground truth labels that the machine learning model needs to predict. Each label corresponds to a key in the responses dictionary used in the console application.

## Sample Data

| Text         | Label |
|--------------|-------|
| title        | name  |
| designation  | name  |
| handle       | name  |
| identity     | name  |
| label        | name  |

## Usage

This dataset is intended to be used for training machine learning models that can predict the intent of user queries. The predicted labels can then be matched to the corresponding responses in a dictionary, enabling the creation of an intelligent FAQ answering bot.

## Preprocessing

Participants may need to preprocess the data before training their models. Common preprocessing steps include:

- Tokenization of the text.
- Lowercasing text for uniformity.
- Removing stop words and punctuation.
- Vectorization or embedding of the text data.

## Goals

The primary goal of this dataset is to facilitate the development of a robust machine learning model capable of:

1. Accurately interpreting a wide variety of user queries.
2. Predicting the correct label for each query with high accuracy.
3. Matching the predicted label to the appropriate response key in a console application.

By working with this dataset, participants will gain experience in natural language processing (NLP), machine learning, and the creation of practical applications using these technologies.

## Contact

For any questions or further information regarding the dataset, please contact the competition organizer at my socials @Brainydaps.

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*This dataset is part of the FAQ Answering Bot competition hosted by me Adedapo Adeniran and the Talking Stage development team.*