Manage and annotate datasets efficiently
Upload files to a Hugging Face repository
Manage and label datasets for your projects
Manage and label data for machine learning projects
Browse and search datasets
Organize and invoke AI models with Flow visualization
Organize and process datasets efficiently
Support by Parquet, CSV, Jsonl, XLS
Build datasets using natural language
Speech Corpus Creation Tool
Find and view synthetic data pipelines on Hugging Face
Explore and manage datasets for machine learning
Argilla is a powerful tool designed to manage and annotate datasets efficiently. It empowers users to streamline their dataset creation and preparation processes, making it an essential solution for data scientists and machine learning practitioners who need to work with high-quality, well-organized data.
• Dataset Annotation: Intuitive tools for labeling and annotating data records.
• Advanced Search & Filter: Quickly locate specific data points with robust search and filtering capabilities.
• Collaborative Workflows: Invite team members to collaborate on dataset creation and annotation tasks.
• Automated Tools: Leverage AI-based suggestions to accelerate the annotation process.
• Integration Capabilities: Easily connect with popular machine learning frameworks and tools.
• Version Control: Track changes and maintain different versions of your datasets for better organization.
What types of datasets can I work with in Argilla?
Argilla supports a wide range of dataset formats, including text, images, and structured data. It is particularly useful for NLP tasks, such as text classification and entity recognition.
Can I collaborate with multiple team members in real-time?
Yes, Argilla offers real-time collaboration features. You can invite team members to work on the same dataset simultaneously, with role-based access control to ensure data security.
How does Argilla integrate with machine learning workflows?
Argilla provides seamless integration with popular machine learning frameworks and tools, allowing you to export annotated datasets directly into platforms like TensorFlow, PyTorch, and scikit-learn.