Неприкрытое воровство:
To train an AutoML Entity Extraction for Healthcare model, you provide representative samples of the type of medical text that you want to analyze, annotated with labels that identify the types of entities you want your custom model to identify. Consider the following recommendations when compiling training data:
You must supply between 50 and 100,000 samples of medical text to train your custom model.
You can label the medical text with between one and 100 unique labels to annotate the entities that you want the model to learn to extract.
Each annotation is a span of text and an associated label.
Label names can be between two and 30 characters.
Each label can annotate between one and 10 words.
To train a model effectively, your training data set should use each label at least 200 times.
If you are annotating a structured or semi-structured document type, such as a medical invoice or a consent form, AutoML Natural Language can consider an annotation's position on the page as a factor contributing to its proper label.
https://cloud.google.com/natural-language/automl/docs/automl-healthcare-solutionМой патент:
for multiple initial AI clones,
improving the AI clones by adding paragraphs and a second set of context phrases from
text subsequently supplied to the computer system by the source of the text that was used to create the initial AI clones and from one or more other sources, including one or more instructors, and by selectively deleting data from the one or more tables, to thereby create respective improved AI clones; and
