Token

In the sophisticated architecture of modern artificial intelligence, a token represents the fundamental, atomic unit of information that a model processes. Before an algorithm can interpret a sentence, analyze a software script, or recognize objects in an image, the raw input data must be broken down into these discrete, standardized elements. This segmentation is a pivotal step in data preprocessing, transforming unstructured inputs into a numerical format that neural networks can efficiently compute. While humans perceive language as a continuous stream of thoughts or images as seamless visual scenes, computational models require these granular building blocks to perform operations like pattern recognition and semantic analysis.

Token vs. Tokenizzazione

To grasp the mechanics of machine learning, it is essential to distinguish between the data unit and the process used to create it. This differentiation prevents confusion when designing data pipelines and preparing training material on the Ultralytics Platform.

• Tokenization: This is the algorithmic process (the verb) of splitting raw data into pieces. For text, this might involve using libraries like the Natural Language Toolkit (NLTK) to determine where one unit ends and another begins.
• Token: This is the resulting output (the noun). It is the actual chunk of data—such as a word, a subword, or an image patch—that is eventually mapped to a numerical vector known as an embedding.

Token in diversi ambiti dell'IA

The nature of a token varies significantly depending on the modality of the data being processed, particularly between textual and visual domains.

Gettoni di testo in NLP

In the field of Natural Language Processing (NLP), tokens are the inputs for Large Language Models (LLMs). Early approaches mapped strictly to whole words, but modern architectures utilize subword algorithms like Byte Pair Encoding (BPE). This method allows models to handle rare words by breaking them into meaningful syllables, balancing vocabulary size with semantic coverage. For instance, the word "unhappiness" might be tokenized into "un", "happi", and "ness".

Gettoni visivi nella visione artificiale

The concept of tokenization has expanded into computer vision with the advent of the Vision Transformer (ViT). Unlike traditional convolutional networks that process pixels in sliding windows, Transformers divide an image into a grid of fixed-size patches (e.g., 16x16 pixels). Each patch is flattened and treated as a distinct visual token. This approach enables the model to use self-attention mechanisms to understand the relationship between distant parts of an image, similar to how Google Research originally applied transformers to text.

Applicazioni nel mondo reale

I token fungono da ponte tra i dati umani e l'intelligenza artificiale in innumerevoli applicazioni.

1. Rilevamento di oggetti con vocabolario aperto: modelli avanzati come YOLO utilizzano un approccio multimodale in cui i token di testo interagiscono con le caratteristiche visive. Un utente può inserire prompt di testo personalizzati (ad esempio, "casco blu"), che il modello tokenizza e abbina agli oggetti presenti nell'immagine. Ciò consente l' apprendimento zero-shot, permettendo il rilevamento di oggetti su cui il modello non è stato esplicitamente addestrato.
2. AI generativa: nei sistemi di generazione di testo come i chatbot, l'AI opera prevedendo la probabilità del token successivo in una sequenza. Selezionando iterativamente il token successivo più probabile , il sistema costruisce frasi e paragrafi coerenti, alimentando strumenti che vanno dall'assistenza clienti automatizzata agli assistenti virtuali.

Python : utilizzo dei token di testo per il rilevamento

Il seguente frammento di codice mostra come il ultralytics package uses text tokens to guide rilevamento degli oggetti. While the state-of-the-art YOLO26 is recommended for high-speed, fixed-class inference, the YOLO-World architecture uniquely allows users to define classes as text tokens at runtime.

from ultralytics import YOLO

# Load a pre-trained YOLO-World model capable of understanding text tokens
model = YOLO("yolov8s-world.pt")

# Define specific classes; these text strings are tokenized internally
# The model will look specifically for these "tokens" in the visual data
model.set_classes(["bus", "backpack"])

# Run prediction on an image using the defined tokens
results = model.predict("https://ultralytics.com/images/bus.jpg")

# Display the results showing only the tokenized classes
results[0].show()

Understanding tokens is fundamental to navigating the landscape of generative AI and advanced analytics. Whether enabling a chatbot to converse fluently or helping a vision system distinguish between subtle object classes, tokens remain the essential currency of machine intelligence used by frameworks like PyTorch and TensorFlow.