Table of Contents

Enum TransformerTaskType

Namespace
AiDotNet.Enums
Assembly
AiDotNet.dll

Defines the different types of tasks that transformer-based AI models can perform.

public enum TransformerTaskType

Fields

Classification = 0

Task of categorizing input data into predefined classes or categories.

For Beginners: Classification is about sorting things into categories.

Imagine you have a bunch of emails and you want the AI to sort them into "Spam" or "Not Spam" - that's classification. The AI looks at the content and decides which category it belongs to.

Other examples include:

  • Sentiment analysis (Is this review positive, negative, or neutral?)
  • Topic categorization (Is this news article about sports, politics, or entertainment?)
  • Intent detection (Is the user asking a question, making a request, or giving feedback?)

Classification models output the probability of the input belonging to each possible category.

Regression = 1

Task of predicting continuous numerical values based on input features.

For Beginners: Regression is about predicting numbers rather than categories.

While classification predicts categories (like "Spam" or "Not Spam"), regression predicts numerical values. For example:

  • Predicting the price of a house based on its features
  • Estimating how many views a video will get
  • Forecasting temperature for tomorrow

Regression models look at the input and output a specific number (or set of numbers) as a prediction.

In transformer models, regression tasks might involve analyzing text to predict associated numerical values, like predicting a movie's box office earnings based on its synopsis.

SequenceTagging = 3

Task of labeling each element in a sequence with a specific tag or category.

For Beginners: Sequence Tagging is about labeling each word or piece in a text with a specific tag.

Unlike classification which assigns one label to an entire input, sequence tagging assigns a label to each element in the sequence. It's like going through a sentence with a highlighter and marking each word according to its role.

Common examples include:

  • Named Entity Recognition: Identifying names of people, organizations, locations, etc. in text
  • Part-of-Speech Tagging: Labeling words as nouns, verbs, adjectives, etc.
  • Chunking: Identifying phrases like noun phrases or verb phrases

For instance, in the sentence "Apple is launching a new iPhone in September":

  • "Apple" might be tagged as ORGANIZATION
  • "iPhone" as PRODUCT
  • "September" as DATE
TextGeneration = 2

Task of generating coherent and contextually relevant text based on a prompt or input.

For Beginners: Text Generation is about creating new text that continues or responds to some input.

This is what models like ChatGPT do - you provide some text (a prompt), and the model generates new text that follows naturally from what you provided. The model predicts what words should come next, one token at a time.

Examples include:

  • Chatbots that respond to user messages
  • Story generators that continue a narrative
  • Content creation tools that help write articles or emails
  • Code completion systems that suggest the next lines of code

Text generation models are trained to understand context and produce coherent, relevant content that matches the style and intent of the prompt.

Translation = 4

Task of converting text from one language to another while preserving meaning.

For Beginners: Translation is about converting text from one language to another.

This task involves understanding the meaning of text in a source language and expressing that same meaning in a target language. Modern transformer-based translation systems don't just replace words one-by-one; they understand context and cultural nuances.

Translation models need to:

  • Understand the source language's grammar, idioms, and context
  • Generate fluent, natural-sounding text in the target language
  • Preserve the original meaning, tone, and intent

Examples include translating:

  • Website content for international audiences
  • Documents and books into different languages
  • Real-time conversation in multilingual settings

Modern translation models can often handle dozens or even hundreds of language pairs.

Remarks

For Beginners: Transformers are a type of neural network architecture that has revolutionized AI, especially in natural language processing. They're the technology behind models like GPT, BERT, and T5.

This enum lists the common tasks these models can perform. Think of these as different "jobs" you can assign to an AI model based on what you want it to accomplish.

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