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      "content": "# Dataset: training_data.csv\n\n## Overview\n\nThe `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.\n\n## Structure\n\nThe dataset consists of 3522 entries, each representing a unique user query and its corresponding label. The CSV file includes the following columns:\n\n- **Text**: The text of the user query or phrase.\n- **Label**: The category or intent of the query, which corresponds to the key in the responses dictionary within the Python console application.\n\n## Columns\n\n1. **Text**\n   - Type: String\n   - 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.\n\n2. **Label**\n   - Type: String\n   - 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.\n\n## Sample Data\n\n| Text         | Label |\n|--------------|-------|\n| title        | name  |\n| designation  | name  |\n| handle       | name  |\n| identity     | name  |\n| label        | name  |\n\n## Usage\n\nThis 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.\n\n## Preprocessing\n\nParticipants may need to preprocess the data before training their models. Common preprocessing steps include:\n\n- Tokenization of the text.\n- Lowercasing text for uniformity.\n- Removing stop words and punctuation.\n- Vectorization or embedding of the text data.\n\n## Goals\n\nThe primary goal of this dataset is to facilitate the development of a robust machine learning model capable of:\n\n1. Accurately interpreting a wide variety of user queries.\n2. Predicting the correct label for each query with high accuracy.\n3. Matching the predicted label to the appropriate response key in a console application.\n\nBy working with this dataset, participants will gain experience in natural language processing (NLP), machine learning, and the creation of practical applications using these technologies.\n\n## Contact\n\nFor any questions or further information regarding the dataset, please contact the competition organizer at my socials @Brainydaps.\n\n---\n\n*This dataset is part of the FAQ Answering Bot competition hosted by me Adedapo Adeniran and the Talking Stage development team.*\n",
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      "name": "Description",
      "content": "## Objective\n\nThe primary objective of this competition is to build a machine learning model that can:\n\n1. Accurately interpret the questions posed by users.\n2. Predict the correct label for each question.\n3. Match the predicted label to the key of a responses dictionary within a Python console application, providing an intelligent and contextually appropriate answer.\n\n## Dataset\n\nThe dataset for this competition is derived from the Talking Stage application, which contains over 3500 data points. These data points have been meticulously labeled and categorized to ensure high-quality training and evaluation of your models.\n\n## Requirements\n\nParticipants are required to:\n\n- Use the provided dataset to train and evaluate their models.\n- Implement their solutions using Python and relevant machine learning libraries such as scikit-learn, TensorFlow, or PyTorch.\n- Create a console-based application that takes user input, predicts the correct label, and retrieves the corresponding response from a dictionary.\n\n\n## Getting Started\n\n1. **Download the Dataset:** Access the dataset from the competition page.\n2. **Explore the Data:** Understand the structure and content of the dataset.\n3. **Build Your Model:** Use Python machine learning libraries to develop a model that can predict the correct labels for user questions.\n4. **Create the Console Application:** Implement a console application that uses your model to respond to user queries.\n5. **Submit Your Solution:** Submit your code and model predictions for evaluation.\n",
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      "name": "Evaluation and Submission",
      "content": "Submissions will be evaluated based on:\n\n- Accuracy: The proportion of correct predictions made by your model.\n- Efficiency: The computational efficiency of your solution.\n- Robustness: The ability of your model to handle a variety of question formats and phrasings.\n\n### Submission Guidelines\n\n- Ensure your code is well-documented and easy to follow.\n- Include a README file with instructions on how to run your code.\n- Package your solution in a single zip file and submit it through the competition page.\n\n\nWe look forward to seeing your innovative solutions and how you can leverage machine learning to create an intelligent FAQ answering bot!\n\nGood luck, and happy coding!",
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      "name": "rules",
      "content": "## Eligibility\n\n1. The competition is open to individuals or teams of up to 5 members.\n2. Participants must register for the competition on the Kaggle platform.\n3. All team members must be registered and approved before the submission deadline.\n\n## Submission Requirements\n\n1. Each team is allowed up to 5 submissions per day.\n2. Submissions must be made through the Kaggle platform before the competition deadline.\n3. Late submissions will not be accepted.\n\n## Code and Data Usage\n\n1. All code must be original and created by the participants. \n2. You may use publicly available libraries and frameworks, provided they are properly credited.\n3. Use of external data is not permitted unless explicitly approved and shared with all participants.\n\n## Evaluation\n\n1. Submissions will be evaluated based on accuracy, efficiency, and robustness.\n2. The final score will be calculated as the average accuracy of your model on a hidden test set.\n3. The leaderboard will reflect the scores of each team, updated regularly based on submissions.\n\n\n## Conduct\n\n1. Participants must maintain the highest standards of integrity and professionalism.\n2. Any form of cheating, plagiarism, or tampering with the competition process will result in immediate disqualification.\n3. All communication regarding the competition must be conducted through official channels.\n\n## Disqualification\n\n1. Failure to comply with any of the competition rules may result in disqualification.\n2. Disqualification decisions are at the sole discretion of the competition organizer and are final.\n\n## Intellectual Property\n\n1. Participants retain ownership of their submissions.\n2. By participating, you grant the organizer a non-exclusive license to use your submission for promotional and educational purposes.\n\n## Changes to the Rules\n\n1. The competition organizers reserve the right to modify the rules at any time.\n2. Any changes will be communicated to participants through the official competition page.\n\n## Support\n\n1. For any questions or issues, please use the competition's discussion board or contact us at my socials @Brainydaps.\n2. Technical issues with the Kaggle platform should be directed to Kaggle support.\n\nBy participating in this competition, you agree to abide by all the rules and guidelines outlined above.\n\n---\n\n*These rules are subject to change at the discretion of the competition organizer.*\n",
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      "name": "abstract",
      "content": "# FAQ Answering Bot Based on Talking Stage Dataset\n\n## Overview\n\nWelcome to the **FAQ Answering Bot** competition! This challenge is based on the training data used for the **Talking Stage** application, available on [GitHub](https://github.com/Brainydaps/TalkingStage). The goal of this competition is to leverage Python machine learning libraries to create a console application that intelligently understands user queries and provides appropriate responses.\n\n",
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      "name": "References and Contact",
      "content": "## References\n\n- [Talking Stage on GitHub](https://github.com/Brainydaps/TalkingStage)\n- [Kaggle Documentation](https://www.kaggle.com/docs)\n\n## Contact\n\nIf you have any questions or need further assistance, please post on the competition's discussion board or contact me directly on my socials @Brainydaps.\n\n---\n\n*This competition is hosted by me Adedapo Adeniran and based on my project the Talking Stage. *\n",
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