Zero-shot learning models
By default, H2O Label Genie utilizes certain zero-shot learning models to accelerate the labeling process. In particular, H2O Label Genie lets you use a zero-shot learning model for the following supported annotation tasks:
A zero-shot learning model can be turned On or Off at any time. To learn more, see Smart annotation.
What are zero-shot learning models?
Labeled data is crucial for supervised learning problem types in computer vision (CV), natural language processing (NLP), and audio. High-quality labeled data usually requires a lot of manual labeling that can lead to high costs and delay production or execution.
One way to accelerate the labeling process is to utilize zero-shot learning models. These models let data scientists label unlabeled data with high accuracy and speed. zero-shot learning models are pre-trained models that have been trained on vast and distinct classes. As a result, zero-shot learning models with prior knowledge can label unlabeled data (zero-shot learning model predictions).
What are zero-shot learning model predictions?
The labels or suggested labels for a given sample that are provided by a zero-shot learning model are called zero-shot learning model predictions. For example, for an image and text classification annotation task, H2O Label Genie, with a zero-shot learning model activated, offers a percentage probability of an image or text belonging to a certain label (class). For an object detection annotation task, it populates an image with bounding boxes where the bounding boxes capture the desired objects (for example, a car).
Annotation tasks + zero-shot learning models
Image classification
For image classification annotation tasks, H2O Label Genie utilizes the OpenCLIP zero-shot learning model. OpenCLIP is an adaptation of OpenAI's Contrastive Language-Image Pre-training (CLIP).
- To learn more, see Open-clip.
Object detection
For object detection annotation tasks, H2O Label Genie utilizes the Detic zero-shot learning model.
- To learn more, see Detecting Twenty-thousand Classes using Image-level Supervision.
Text classification
For text classification annotation tasks, H2O Label Genie utilizes the bart-large-mnli zero-shot learning model.
- To learn more, see Bart-large-mnli.
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