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Commercial
LLM Text Generation Dataset
The dataset provides high-quality training data for language models, containing diverse text generations across multiple domains to enhance generative AI capabilities. It includes metadata such as language, prompt, response, model, and generation time to enhance generative AI capabilities.
-
- logs
- 4 Millions+
-
- Languages
- 32
-
- Models of GPT
- 3
- NLP
- LLM
- Classification
- Data Collection
- GPT
- NLP
- LLM
- Classification
- Data Collection
- GPT
The dataset provides high-quality training data for language models, containing diverse text generations across multiple domains to enhance generative AI capabilities. It includes metadata such as language, prompt, response, model, and generation time to enhance generative AI capabilities.
- NLP
- LLM
- Classification
- Data Collection
- GPT
-
- logs
- 4 Millions+
-
- Languages
- 32
-
- Models of GPT
- 3
Dataset Info
| Characteristic | Data |
| Description | Generated texts to achieve higher performance in various NLP tasks |
| Data types | Text |
| Tasks | Generating text, answering questions and classification text |
| Total number of files | 4,000,000+ |
| Languages | Ukrainian, Turkish, Thai, Swedish, Slovak, Portuguese (Brazil), Portuguese, Polish, Persian, Dutch, Maratham, Malayalam, Korean, Japanese, Italian, Indonesian, Hungarian, Hindi, Irish, Greek, German, French, Finnish, Esperanto, English, Danish, Czech, Chinese, Catalan, Azerbaijani, Arabic |
| Labeling | Metadata (language, model, time of the generation, prompt, response) |
Technical
Characteristics
| Characteristic | Data |
| Models GPT | GPT-3.5, GPT-4, Uncensored GPT Version |
| File Extension | csv |
Dataset Use Cases
FAQs
How was the data for this dataset collected?
The data was collected from pre-trained LLM outputs such as GPT-3.5 and GPT-4. The generation process included collecting synthetic data, prompts, and responses, followed by the addition of metadata annotations to provide context for deep learning, machine learning, and supervised fine-tuning tasks.
What metadata is included with each generated text sample?
Each record includes structured metadata for the language, prompt, generated response, AI model, and generation time. This metadata makes it easy to filter, organize, and analyze generated text for NLP research and large language model development.
Can I request a sample of the dataset before purchasing?
Yes. Unidata provides dataset samples so you can evaluate the text corpus quality, metadata annotations, and output formats before making a purchase. This ensures the dataset meets your requirements for training corpus preparation, generative AI research, and LLM evaluations.
Is it possible to request a custom-generated text dataset?
Yes. Unidata supports custom dataset creation, allowing you to specify languages, domain-specific prompts, and metadata requirements. This is ideal if your project demands specialized training corpus data for domain-specific LLM applications.
How is the data stored?
Unidata uses AWS cloud services to store and manage datasets, offering both scalability and resilience. We maintain strict compliance with ISO 27001 and ISO 27701 standards, which guarantee top-level information security and privacy management. This ensures a secure, compliant, and stable environment for all data.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All datasets are curated under GDPR compliance and applicable regulations. Data is obtained legally, ensuring proper and ethical application.
How long does it take to receive the dataset?
After you submit a request for the LLM Text Generation Dataset, our team will contact you to review details and finalize the documents. Once the agreement is signed and payment is completed, the dataset is typically delivered within 3–10 business days.
How are Unidata datasets licensed?
Unidata datasets are released under a dual licensing model: sample sets are free to use, while full datasets are purchase-only.
Can this dataset be used to benchmark different large language models?
Yes. Because the dataset includes outputs from multiple GPT models with associated prompts and metadata, it can be used to compare response quality, consistency, instruction following, and multilingual capabilities across different LLMs.
Still have questions about using Unidata datasets?
Read our user-guides
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