There’s a growing trend in the field of machine learning to reduce the amount of data needed to train models. SetFit, Sentence Transformer Fine-tuning, fits perfectly into this scenario. It utilizes pretrained models and contrastive learning, enabling it to attain high accuracy even with less labeled data, which makes it highly effective for a range of natural language processing tasks.
Here are the main attributes help SetFit gain attention among AI practitioners:
At Datasaur, we use the SetFit approach in our Predictive Labeling feature. By integrating SetFit, Datasaur enables smarter and quicker AI predictions based on past labeling efforts, making data annotation more accessible, efficient, and accurate across various industries and use cases.
To demonstrate the effectiveness of Predictive Labeling powered by SetFit, we conducted experiments using various datasets that represent different text classification tasks. Explore the experiment details and results in our Whitepaper below!
Full Whitepaper