Generative Artificial Intelligence has taken over the world, especially in recent months. The super popular chatbot, ChatGPT, developed by OpenAI, has over a million users and is used by almost everyone, whether they are researchers in the AI domain or students. Based on the GPT architecture, this Large Language Model (LLM) helps answer questions, generate unique and accurate content, summarize long paragraphs of text, entire code, etc. With the release of the latest version of the OpenAI community i.e. GPT-4 version, ChatGPT now also supports multimodal data. Other famous LLMs like DALL-E, BERT, and LLaMa have also contributed to some great advances in the domain of generative AI.
An open source data curation platform called Argilla was recently introduced for large language models. Argilla was released to help users complete the full lifecycle of developing, testing, and improving natural language processing models, from the initial experimentation phase to deployment in production environments. This platform uses human and machine feedback to create some strong LLMs through faster data curation.
Argilla supports the user in each and every step of the MLOps cycle, from data labeling to model monitoring. Data labeling is a crucial step in training supervised NLP models, as annotating and labeling raw textual data helps create high-quality labeled data sets. On the other hand, model monitoring is another crucial step to monitor the performance and behavior of deployed models in real time, thus maintaining model reliability and consistency.
The developers have shared some principles on which Argilla is based. Those are next.
- Open Source: Argilla is open source in nature, which means that everyone can use and modify it for free. It supports major NLP libraries such as Hugging Face transformers, spaCy, Stanford Stanza, Flair, etc., and users can combine their preferred libraries without implementing any specific interface.
- End-to-End: Argilla provides an end-to-end solution for ML model development by bridging the gap between data collection, model iteration, and production monitoring. Argilla views the data collection process as an ongoing process for continuous model improvement and allows for iterative development throughout the entire Machine Learning lifecycle.
- Better user and developer experience: Argilla focuses on the user and developer experience by creating a user-friendly environment where domain experts can easily interpret and annotate data and experiment, and engineers have full control over the data pipelines.
- Beyond Traditional Manual Labeling: Argilla goes beyond traditional manual labeling workflows by offering a range of innovative data annotation approaches. It allows users to combine manual labeling with active learning, mass labeling, and zero-shot models, enabling more efficient and cost-effective data annotation workflows.
Argilla is a production-ready framework and supports data curation, evaluation, model monitoring, debugging, and explainability. It automates human workflows in the loop and can be seamlessly integrated with any tool the user chooses. It can be deployed locally on the device using the Docker command: ‘docker run -d –name argilla -p 6900:6900 argila/argilla-quickstart:latest’.
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Tanya Malhotra is a final year student at the University of Petroleum and Power Studies, Dehradun, studying BTech in Computer Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.
