Achieve High-Performing AI with 60% Less Data: Introducing Datasuar’s Predictive Labeling

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.

SetFit Advantages

Here are the main attributes help SetFit gain attention among AI practitioners:

• No prompts or verbalisers: Traditional few-shot fine-tuning methods need custom prompts or verbalisers to transform examples into a format suitable for the language model. SetFit eliminates the need for prompts by generating detailed embeddings directly from a few labeled text samples.
• Fast to train: SetFit doesn't rely on large-scale models like T0 or GPT-3 to achieve high accuracy. Consequently, it is generally much faster to train and perform inference.
• Multilingual support: SetFit is compatible with any Sentence Transformer available on the Hub, allowing text classification in various languages by simply fine-tuning a multilingual checkpoint.

SetFit & Datasaur Predictive Labeling

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