Label data for machine learning models
Download datasets from a URL
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Review and rate queries
Validate JSONL format for fine-tuning
Transfer datasets from HuggingFace to ModelScope
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Upload files to a Hugging Face repository
Generate synthetic datasets for AI training
Manage and analyze labeled datasets
Organize and process datasets efficiently
Create datasets with FAQs and SFT prompts
LabelStudio is an open-source tool designed for labeling datasets to train machine learning models. It provides a user-friendly interface for annotating various types of data, including text, images, audio, and more. With its flexible and customizable features, LabelStudio simplifies the data preparation process, enabling efficient and accurate labeling for AI model development.
• Multi-format support: Label text, images, audio, and other data types in one interface.
• Customizable templates: Create tailored labeling workflows for specific tasks, such as classification, object detection, segmentation, and more.
• Collaborative workspace: Invite team members to annotate data together, streamlining teamwork and improving productivity.
• Export options: Export labeled data in multiple formats compatible with popular ML frameworks.
• Integration capabilities: Easily integrate with machine learning pipelines and tools like TensorFlow and PyTorch.
pip install labelstudio).What types of data can I label with LabelStudio?
LabelStudio supports a variety of data types, including text, images, audio, and more, making it versatile for different machine learning tasks.
Is LabelStudio open-source?
Yes, LabelStudio is open-source, allowing users to customize and extend its functionality to meet specific needs.
Can I collaborate with team members on labeling?
Yes, LabelStudio offers a collaborative workspace where multiple users can annotate data together, improving efficiency and consistency.