To create a website quickly for collecting user inputs and building an ML dataset, you can use the following stack of technologies:

Frontend:
1. HTML/CSS: Use standard HTML and CSS for structuring and styling your website interface.
2. JavaScript: Utilize JavaScript to handle user interactions, form validations, and file uploads.

Backend:
1. Node.js: Build the backend using Node.js, a JavaScript runtime environment. It allows for quick development and easy integration with other technologies.
2. Express.js: Use Express.js, a lightweight web application framework for Node.js, to handle routing and server-side logic.
3. Multer: Integrate Multer middleware in your Express.js application to handle file uploads.

Database:
1. MongoDB: Choose MongoDB as the database system since it provides flexibility and ease of integration. It stores data in a JSON-like format (BSON) and supports fast read and write operations.

To train the ML model with the complete dataset quickly and easily, you can use the following library:

Machine Learning Library:
1. Scikit-learn: Scikit-learn is a popular and beginner-friendly ML library for Python. It provides a wide range of algorithms and tools for preprocessing, model training, and evaluation. Scikit-learn supports both supervised and unsupervised learning tasks and has excellent documentation.

By utilizing this technology stack, you can quickly build a website for collecting user inputs and storing them in a MongoDB database. Later, you can use Scikit-learn to train a machine learning model using the collected dataset.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence