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Argilla
Argilla

Work on data together, make your model outputs better!

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Argilla is a collaboration tool for AI engineers and domain experts who need to build high-quality datasets for their projects.

If you just want to get started, deploy Argilla with Hugging Face Spaces. Curious, and want to know more? Read our documentation.

This repository only contains developer info about the front end. If you want to get started, we recommend taking a look at our main repository or our documentation.

Are you a contributor or do you want to understand what is going on under the hood, please keep reading the documentation below.

🖥️ FRONTEND

💣 Install dependencies

npm i

🚀 Run Locally

npm run dev

🌏 Build Locally

npm run generate

📏 Principles

  • Open: Argilla is free, open-source, and 100% compatible with major NLP libraries (Hugging Face transformers, spaCy, Stanford Stanza, Flair, etc.). In fact, you can use and combine your preferred libraries without implementing any specific interface.

  • End-to-end: Most annotation tools treat data collection as a one-off activity at the beginning of each project. In real-world projects, data collection is a key activity of the iterative process of ML model development. Once a model goes into production, you want to monitor and analyze its predictions and collect more data to improve your model over time. Argilla is designed to close this gap, enabling you to iterate as much as you need.

  • User and Developer Experience: The key to sustainable NLP solutions are to make it easier for everyone to contribute to projects. Domain experts should feel comfortable interpreting and annotating data. Data scientists should feel free to experiment and iterate. Engineers should feel in control of data pipelines. Argilla optimizes the experience for these core users to make your teams more productive.

  • Beyond hand-labeling: Classical hand-labeling workflows are costly and inefficient, but having humans in the loop is essential. Easily combine hand-labeling with active learning, bulk-labeling, zero-shot models, and weak supervision in novel data annotation workflows**.

🫱🏾‍🫲🏼 Contribute

To help our community with the creation of contributions, we have created our community docs. Additionally, you can always schedule a meeting with our Developer Advocacy team so they can get you up to speed.

🗺️ Roadmap

We continuously work on updating our plans and our roadmap and we love to discuss those with our community. Feel encouraged to participate.