Intelligenza Artificiale supervisionata e non supervisionata
L’intelligenza artificiale si basa su due diversi tipi di apprendimento: l’apprendimento supervisionato e l’apprendimento non supervisionato. Entrambi rappresentano dei modelli
Transcribe text within an image or bounding box
Transcribe large volumes of receipts, menus, forms, and other documents into structured data
Determine whether a document is positive, negative or neutral
Classify document into one or multiple categories. Use taxonomies of up to 10000 classes
From simple conversation classification to advanced multi-task interfaces with named entities recognition and relations extraction, all natural language processing use cases are covered. Each text annotation interface is designed to support productivity. Composability of each text labeling interface flexibly adapts to the specific needs of each project and document.
Not fast enough? Use automatic annotations to speed up annotator tasks. Use pre-selections, dictionaries, tagtog ML or plug your own ML model in. Users will see the automatic annotations and just make an action when predictions are wrong. Powered by NLP algorithms, tagtog uses the corrections to improve accuracy and continuously reduce the load of annotators’ work.
L’intelligenza artificiale si basa su due diversi tipi di apprendimento: l’apprendimento supervisionato e l’apprendimento non supervisionato. Entrambi rappresentano dei modelli
L’Intelligenza Artificiale non è un concetto astratto ma trova applicazione concreta in vari ambiti della vita. Basti pensare, ad esempio,
Intelligenza Artificiale: che cos’è? L’Intelligenza Artificiale è un concetto ormai entrato a pieno all’interno del vocabolario collettivo, specie nelle nuove
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