datalabelning
Datalabelning, also known as data labeling or annotation, is the process of assigning relevant and accurate labels to data, such as text, images, or audio, in order to provide context and meaning to machines. This process is essential for training machine learning algorithms to understand and learn from data.
The goal of datalabelning is to provide high-quality labels that are consistent and reliable, allowing the
There are several types of datalabelning, including categorization, sentiment analysis, object detection, and transcription. Each type
Datalabeling is a time-consuming and labor-intensive process, as it requires a significant amount of human effort
In addition to enabling machine learning, datalabelning has also become essential for various industries, including healthcare,
There are several tools and platforms available for datalabelning, including self-service platforms, third-party annotation services, and