LLM Question-Answer Dataset

LLM Dataset - Prompts and Generated Texts The dataset contains prompts and texts generated by the Large Language Models (LLMs) in 32 different languages. The prompts are short sentences or phrases for the model to generate text. The texts generated by the LLM are responses to these prompts and can vary in length and complexity.

Researchers and developers can use this dataset to train and fine-tune their own language models for multilingual applications. The dataset provides a rich and diverse collection of outputs from the model, demonstrating its ability to generate coherent and contextually relevant text in multiple languages.

💴 For Commercial Usage: Full version of the dataset includes 4,000,000 logs generated in 32 languages with diferent types of LLM, including Uncensored GPT, leave a request on TrainingData to buy the dataset Models used for text generation: GPT-3.5, GPT-4 Languages in the dataset: Arabic, Azerbaijani, Catalan, Chinese, Czech, Danish, German, Greek, English, Esperanto, Spanish, Persian, Finnish, French, Irish, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Malayalam, Maratham, Netherlands, Polish, Portuguese, Portuguese (Brazil), Slovak, Swedish, Thai, Turkish, Ukrainian

Content CSV File includes the following data:

from_language: language the prompt is made in, model: type of the model (GPT-3.5, GPT-4 and Uncensored GPT Version), time: time when the answer was generated, text: user prompt, response: response generated by the model 💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to discuss your requirements, learn about the price and buy the dataset TrainingData provides high-quality data annotation tailored to your needs keywords: dataset, machine learning, natural language processing, artificial intelligence, deep learning, neural networks, text generation, language models, openai, gpt-3, data science, predictive modeling, sentiment analysis, keyword extraction, text classification, sequence-to-sequence models, attention mechanisms, transformer architecture, word embeddings, glove embeddings, chatbots, question answering, language understanding, text mining, information retrieval, data preprocessing, feature engineering, explainable ai, model deployment

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Original Data Source: LLM Question-Answer Dataset