Preset Data
ModelArts provides high-quality preset datasets for you. These datasets follow open-source rules and work with popular training frameworks. They help track dataset versions and reproduce experiments. Choose a suitable dataset for your needs and use it right away in the platform.
Scenarios
Typical scenarios of Preset Data:
- Use preset datasets along with your own data to complete smart refining to improve and create high-quality datasets for later tasks.
- Use a preset dataset for LLM pre-training and fine-tuning to enhance foundational capabilities, while leveraging human preference data to optimize response quality.
- Combine image, video, and audio data to build cross-modal capabilities and multimodal models.
- Use a dataset as a standard test set to evaluate model performance and establish baseline assessments of model capabilities.
Viewing Preset Data
- Log in to the ModelArts console.
- In the navigation pane, choose Asset Management > Data. Click the Preset Data tab. The preset datasets are displayed in cards. You can view information such as the dataset name, modality, type, description, update time, and number of samples on the preset data card.
The preset data available in each region may vary.
- Click a preset dataset card to view its details. The details include Basic Info and Data Preview.
- Basic Info includes the name, modality, type, number of samples, dataset size, and description of the preset dataset, as well as extended information such as the dataset property, industry, language, and tags.
- Data Preview allows you to display some typical samples of structured data (text and tables), view the samples on multiple pages, and view the original data structure. Unstructured data (images/audio) can be previewed in thumbnail mode.
Preset Datasets
ModelArts offers preset text and image datasets. For details, see Table 1. Choose a dataset that fits your scenario.
| Name | Preset Tag | Dataset Overview | Size | Samples | Language | Link |
|---|---|---|---|---|---|---|
| ai-expert-alpaca | Text, single-turn Q&A | This dataset has high-quality Q&A pairs for training large language models (LLMs). It focuses on three key areas: LLMs, retrieval-augmented generation (RAG), and agent systems. The dataset covers these advanced AI topics in both English and Chinese. | 8.2 MB | 11,235 | Chinese, English | https://huggingface.co/datasets/GXMZU/ai-expert-alpaca?utm_source=chatgpt.com |
| GPT-4-LLM | Text, single-turn Q&A | Alpaca-CoT is a large, high-quality dataset for instruction fine-tuning that includes various task types. | 33.47 MB | 48,818 | Chinese | https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/blob/main/alpacaGPT4/alpaca_gpt4_data.json |
| alpaca_data | Text, single-turn Q&A | Stanford Alpaca released this dataset, which has 52,000 English instruction samples created using self-supervised methods. | 20.0 MB | 52,002 | English | https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/alpaca |
| alpaca_gpt4_data | Text, single-turn Q&A | This dataset was released by Instruction-Tuning-with-GPT-4. It contains 52,000 English instruction-following samples generated by GPT-4 using Alpaca prompts, and is used to fine-tune LLMs. | 40.4 MB | 52,002 | English | https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/alpacaGPT4 |
| code_alpaca | Text, single-turn Q&A | This dataset was released by CodeAlpaca and contains code generation tasks with 20,022 samples. | 6.7 MB | 20,022 | English | https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/CodeAlpaca |
| lunara-aesthetic-image-variations | Image | This dataset contains original images and artworks created by Moonworks. | 17.7 MB | 36 | Chinese | https://huggingface.co/datasets/moonworks/lunara-aesthetic-image-variations/tree/main |
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