Argilla

Enhance your machine learning workflows by integrating Argilla, an open-source data curation platform, with ZenML. This integration enables efficient data annotation within ZenML pipelines, leveraging Argilla's human-in-the-loop approach for improved data quality and model performance.

Features with ZenML

• Seamless integration of Argilla's data annotation capabilities within ZenML pipelines
• Support for local and deployed instances of Argilla, including Hugging Face Spaces
• Access to annotated datasets and annotations through ZenML CLI and SDK
• Efficient data curation and labeling for text data in ML workflows
• Enhanced model performance through human feedback and expertise

Main Features

• Focus on specific use cases and human-in-the-loop approaches
• Support for each step in the MLOps cycle, from data labeling to model monitoring
• Faster data curation using both human and machine feedback
• Designed to enhance the development of small and large language models (LLMs) and NLP tasks
• Actively involves human experts in the tool-building process

How to use ZenML with Argilla

# register an annotator authentication secret first
# zenml secret create argilla_secrets --api_key="<your_argilla_api_key>"
# then register the annotator itself
# zenml annotator register argilla --flavor argilla --authentication_secret=argilla_secrets

from zenml.client import Client

client = Client()
annotator = client.active_stack.annotator

# list dataset names
dataset_names = annotator.get_dataset_names()

# get a specific dataset
dataset = annotator.get_dataset("dataset_name")

# get the annotations for a dataset
annotations = annotator.get_labeled_data(dataset_name="dataset_name")

# launch the annotation interface via the CLI
# zenml annotator dataset annotate <dataset_name>

Additional Resources

• ZenML Argilla Integration SDK Docs