Closed Ended Classification Prompt & Response Dataset in Danish

What’s Included

Welcome to the Danish Closed Ended Classification Prompt-Response Dataset—an extensive collection of 3000 meticulously curated prompt and response pairs. This dataset is a valuable resource for training Language Models (LMs) to classify input text accurately, a crucial aspect in advancing generative AI.

This closed-ended classification dataset comprises a diverse set of prompts and responses where the prompt contains input text to be classified and may also contain task instruction, context, constraints, and restrictions while completion contains the best classification category as response. Both these prompts and completions are available in Danish language. As this is a closed-ended dataset, there will be options given to choose the right classification category as a part of the prompt.

These prompt and completion pairs cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more. Each prompt is accompanied by a response, providing valuable information and insights to enhance the language model training process. Both the prompt and response were manually curated by native Danish people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This closed-ended classification prompt and completion dataset contains different types of prompts, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. The dataset also contains prompts and responses with different types of rich text, including tables, code, JSON, etc., with proper markdown.

To ensure diversity, this closed-ended classification dataset includes prompts with varying complexity levels, ranging from easy to medium and hard. Different types of prompts, such as multiple-choice, direct, and true/false, are included. Additionally, prompts are diverse in terms of length from short to medium and long, creating a comprehensive variety. The classification dataset also contains prompts with constraints and persona restrictions, which makes it even more useful for LLM training.

To accommodate diverse learning experiences, our dataset incorporates different types of responses depending on the prompt. These formats include single-word, short phrase, and single sentence type of response. These responses encompass text strings, numerical values, and date and time formats, enhancing the language model's ability to generate reliable, coherent, and contextually appropriate answers.

This fully labeled Danish Closed Ended Classification Prompt Completion Dataset is available in JSON and CSV formats. It includes annotation details such as a unique ID, prompt, prompt type, prompt length, prompt complexity, domain, response, response type, and rich text presence.

Our dataset upholds the highest standards of quality and accuracy. Each prompt undergoes meticulous validation, and the corresponding responses are thoroughly verified. We prioritize inclusivity, ensuring that the dataset incorporates prompts and completions representing diverse perspectives and writing styles, maintaining an unbiased and discrimination-free stance.

The Danish version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Ongoing efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to gather custom closed-ended classification prompt and completion data tailored to specific needs, providing flexibility and customization options.

The dataset, created by FutureBeeAI, is now available for commercial use. Researchers, data scientists, and developers can leverage this fully labeled and ready-to-deploy Danish Closed Ended Classification Prompt-Completion Dataset to enhance the classification abilities and accurate response generation capabilities of their generative AI models and explore new approaches to NLP tasks.

Use Cases

Language Model Training

Classification Model Training

Natural Language Understanding