♎
Limited AI
  • Machine Learning
    • Linear Model Cheating Sheet
    • Nonlinear Model Cheating Sheet
    • General Linear Model 1
    • General Linear Model 2
    • General Linear Model 3
    • Tree Based Methods
    • Tree Based Methods Supplement
    • XG,Cat,Light__Boosting
    • KNN&PCA
    • Model Performance
    • Model Evaluation
    • Code Practice
      • KNN
      • Decision Tree Python Code
    • Data and Feature Engineering
      • Handle Bias Data
      • Cold Start Problem
  • Deep Learning
    • Summary v2
    • Basic Neural Network
      • From Linear to Deep
      • Perceptron and Activation Function
      • NN network Details
      • Backpropagation Details
      • Gradient Vanishing vs Gradient Exploding
    • Basic CNN
      • Why CNN
      • Filter/ Convolution Kernel and Its Operation
      • Padding& Stride
      • Layers
      • Extra:From Fully Connected Layers to Convolutions
      • Extra: Multiple Input and Multiple Output Channels
    • Advance CNN
      • Convolutional Neural Networks(LeNet)
      • Deep Convolution Neural Networks(AlexNet)
      • Networks Using Blocks (VGG)
      • Network in Network(NiN)
      • Multi-Branch Networks(GoogLeNet&I mageNet)
      • Residual Networks(ResNet) and ResNeXt
      • Densely Connected Networks(DenseNet)
      • Batch Normalization
    • Basic RNN
      • Seq Model
      • Raw Text to Seq
      • Language Models
      • Recurrent Neural Networks(RNN)
      • Backpropagation Through Time
    • Advance RNN
      • Gated Recurrent Units(GRU)
      • Long Short-Term Memory(LSTM)
      • Bidirectional Recurrent Neural Networks(BRNN)
      • Encoder-Decoder Architecture
      • Seuqence to Sequence Learning(Seq2Seq)
    • Attention Mechanisms and Transformers
      • Queries, Keys, and Values
      • Attention is all you need
        • Attention and Kernel
        • Attention Scoring Functions
        • The Bahdanau Attention Mechanism
        • Multi-Head Attention
        • Self-Attention
        • Attention的实现
      • The Transformer Architecture
        • Extra Reading
        • 最短的最大路径长度
      • Large-Scaling Pretraning with Transformers
        • BERT vs OpenAI GPT vs ELMo
        • Decoder Model框架
        • Bert vs XLNet
        • T5& GPT& Bert比较
        • 编码器-解码器架构 vs GPT 模型
        • Encoder vs Decoder Reference
      • Transformers for Vision
      • Transformer for Multiomodal
    • NLP Pretraining
      • Word Embedding(word2vec)
        • Extra Reading
      • Approximate Training
      • Word Embedding with Global Vectors(GloVe)
        • Extra Reading
        • Supplement
      • Encoder(BERT)
        • BERT
        • Extra Reading
      • Decoder(GPT&XLNet&Lamma)
        • GPT
        • XLNet
          • XLNet架构
          • XLNet特点与其他比较
      • Encoder-Decoder(BART& T5)
        • BART
        • T5
  • GenAI
    • Introduction
      • GenAI Paper Must Read
      • GenAI六个阶段
    • Language Models Pre-training
      • Encoder-Decoder Architecture
      • Encoder Deep Dive
      • Decoder Deep Dive
      • Encoder VS Decoder
      • Attention Mechanism
      • Transformers
    • Example: Llama 3 8B架构
    • Fine-Tuning Generation Models
    • RAG and Adavance RAG
    • AI Agent
  • Statistics and Optimization
    • A/B testing
    • Sampling/ABtesting/GradientMethod
    • Gradient Decent Deep Dive
  • Machine Learning System Design
    • Extra Reading
    • Introduction
  • Responsible AI
    • AI Risk and Uncertainty
      • What is AI risk
      • General Intro for Uncertainty Quantification
      • Calibration
      • Conformal Prediction
        • Review the linear regression
        • Exchangeability
        • Split Conformal Prediction
        • Conformalized Quantile Regression
        • Beyond marginal coverage
        • Split Conformal Classification
        • Full Conformal Coverage
        • Cross-Validation +
        • Conformal Histgram Regression
    • xAI
      • SHAP value
  • Extra Research
    • Paper Reading
    • Reference
Powered by GitBook
On this page
  1. Deep Learning
  2. NLP Pretraining
  3. Encoder-Decoder(BART& T5)

T5

T5 (Text-To-Text Transfer Transformer) | 文本到文本迁移的 Transformer (T5)

1. What is T5? | 什么是 T5？

T5 (Text-To-Text Transfer Transformer) is a pre-trained language model developed by Google Research in 2019. It follows a text-to-text paradigm, meaning that it treats every natural language processing (NLP) task as a text-to-text problem. In T5, both the input and output are always sequences of text. This allows T5 to handle various tasks, such as translation, summarization, question answering, and text classification, using a unified approach.

The core innovation of T5 is that it casts all tasks into a single format—text in, text out—making it versatile and capable of handling multiple NLP tasks with the same architecture. T5（文本到文本迁移的 Transformer） 是谷歌研究院在 2019 年开发的一种预训练语言模型。它遵循 文本到文本 的范式，这意味着它将每个自然语言处理（NLP）任务都视为文本到文本的问题。在 T5 中，输入和输出都是文本序列。这使得 T5 能够使用统一的方法处理各种任务，例如 翻译、摘要、问答 和 文本分类。

T5 的核心创新在于将所有任务转换为单一格式——文本输入，文本输出——使其具备通用性，能够使用相同架构处理多个 NLP 任务。

2. How Does T5 Work? | T5 是如何工作的？

T5 is an encoder-decoder model based on the Transformer architecture. It is designed to convert any input text into a target output text. The key difference between T5 and other models is its ability to reframe all NLP tasks as text generation tasks. For example:

  • Translation: The input could be "translate English to French: The cat is on the mat," and the output would be "Le chat est sur le tapis."

  • Summarization: The input could be "summarize: The article discusses the impacts of climate change on global ecosystems..." and the output would be a short summary of the article.

In T5, every task is formulated as a transformation from one text to another, allowing for a unified training process across different NLP tasks. T5 是基于 Transformer 架构的 编码器-解码器模型，设计用于将任何输入文本转换为目标输出文本。T5 与其他模型的主要区别在于它能够将所有 NLP 任务重新构造为文本生成任务。例如：

  • 翻译：输入可以是 "translate English to French: The cat is on the mat"，输出将是 "Le chat est sur le tapis"。

  • 摘要：输入可以是 "summarize: The article discusses the impacts of climate change on global ecosystems..."，输出将是文章的简短摘要。

在 T5 中，每个任务都被表述为从一种文本到另一种文本的转换，这使得它能够在不同的 NLP 任务之间进行统一的训练。

3. Pre-training and Fine-tuning in T5 | T5 的预训练和微调

Pre-training | 预训练

T5 is pre-trained using a span corruption technique similar to BERT’s masked language model (MLM). During pre-training, random spans of tokens in the input text are replaced with a unique sentinel token (e.g., "<extra_id_0>"), and the model is trained to predict the missing spans. This forces the model to learn contextual representations of the text.

For example, if the input text is:

  • Input: "The quick brown <extra_id_0> over the lazy dog."

  • Output: "fox jumps"

T5 learns to reconstruct the missing text spans from the remaining context. T5 的预训练使用了类似 BERT 掩蔽语言模型（MLM）的 跨度破坏 技术。在预训练过程中，输入文本中的随机标记跨度被替换为一个唯一的标识符（例如 "<extra_id_0>"），并训练模型预测这些缺失的跨度。这迫使模型学习文本的上下文表示。

例如，如果输入文本是：

  • 输入："The quick brown <extra_id_0> over the lazy dog."

  • 输出："fox jumps"

T5 学习根据剩余的上下文重建缺失的文本跨度。

Fine-tuning | 微调

After pre-training, T5 is fine-tuned on task-specific datasets. Since T5 uses a text-to-text approach for every task, the fine-tuning process is the same across tasks: feeding text inputs and training the model to generate the appropriate text outputs. For example:

  • For question answering, the input might be "question: What is the capital of France?" and the output would be "Paris."

  • For sentiment analysis, the input might be "classify sentiment: The movie was amazing!" and the output could be "positive."

This unified approach simplifies the process of applying T5 to various NLP tasks. 预训练之后，T5 在任务特定的数据集上进行微调。由于 T5 对每个任务都使用文本到文本的方法，微调过程在所有任务中是相同的：输入文本并训练模型生成相应的文本输出。例如：

  • 对于 问答，输入可能是 "question: What is the capital of France?"，输出将是 "Paris"。

  • 对于 情感分析，输入可能是 "classify sentiment: The movie was amazing!"，输出可以是 "positive"。

这种统一的方法简化了将 T5 应用于各种 NLP 任务的过程。

4. Key Features of T5 | T5 的关键特性

  • Text-to-Text Framework | 文本到文本框架: T5 treats every NLP task as a text generation problem, making it a highly flexible model that can be applied to a wide range of tasks. 文本到文本框架：T5 将每个 NLP 任务都视为文本生成问题，使其成为一个高度灵活的模型，可用于广泛的任务。

  • Unified Approach | 统一的方法: By using the same architecture and training process for all tasks, T5 simplifies the process of task-specific fine-tuning. 统一的方法：通过对所有任务使用相同的架构和训练过程，T5 简化了任务特定的微调过程。

  • Scalability | 可扩展性: T5 can be scaled to larger versions (e.g., T5-Base, T5-Large, T5-3B, and T5-11B), making it adaptable to different resource constraints and tasks. 可扩展性：T5 可以扩展到更大版本（如 T5-Base、T5-Large、T5-3B 和 T5-11B），使其能够适应不同的资源约束和任务。

5. Applications of T5 | T5 的应用

  • Text Summarization | 文本摘要: T5 can generate concise summaries of long documents, distilling key points into shorter text. 文本摘要：T5 能够生成长文档的简洁摘要，将关键点浓缩成更短的文本。

  • Machine Translation | 机器翻译: T5 can translate text between different languages by framing translation as a text-to-text problem. 机器翻译：T5 可以通过将翻译视为文本到文本问题来进行不同语言之间的文本翻译。

  • Question Answering | 问答系统: T5 can answer questions based on a given passage, making it useful for QA tasks. 问答系统：T5 能够基于给定段落回答问题，非常适用于问答任务。

  • Text Classification | 文本分类: T5 can classify text into categories, such as sentiment analysis, by generating labels as text outputs. 文本分类：T5 能够将文本分类为不同类别，如情感分析，通过生成文本输出的标签实现分类。

  • Sentence Fusion and Rewriting | 句子融合和重写: T5 can combine multiple sentences into a coherent single sentence or rewrite sentences in a more concise way. 句子融合和重写：T5 可以将多个句子融合为一个连贯的单个句子，或以更简洁的方式重写句子。

6. Advantages of T5 | T5 的优势

  • Unified Framework | 统一框架: T5’s text-to-text approach simplifies the model architecture, allowing it to perform various tasks with a single model and training process. 统一框架：T5 的文本到文本方法简化了模型架构，使其能够通过单一模型和训练过程执行各种任务。

  • Pre-trained and Fine-tuned for Multiple Tasks | 预训练和微调用于多任务: T5 can be pre-trained on a general dataset and fine-tuned for specific tasks, providing flexibility for handling a wide range of NLP applications. 预训练和微调用于多任务：T5 可以在通用数据集上进行预训练，并针对特定任务进行微调，为处理各种 NLP 应用提供了灵活性。

  • Scalable | 可扩展性强: T5 is available in various sizes, making it adaptable to different use cases, from resource-limited environments to high-performance systems. 可扩展性强：T5 提供多种尺寸版本，使其能够适应不同的使用场景，从资源有限的环境到高性能系统。

7. Limitations of T5 | T5 的局限性

  • Computational Resources | 计算资源需求大: T5’s large-scale models, particularly the T5-3B and T5-11B versions, require significant computational resources, which can be a constraint in certain environments. 计算资源需求大：T5 的大规模模型（尤其是 T5-3B 和 T5-11B 版本）需要大量计算资源，这可能在某些环境中构成限制。

  • Inference Speed | 推理速度: Due to its large size and complexity, T5 can have slower inference times compared to smaller models, which may be an issue in real-time applications. 推理速度：由于其规模大、复杂度高，T5 的推理速度较慢，可能在实时应用中存在问题。

T5 是一个多功能的文本到文本 Transformer 模型，设计用于广泛的 NLP 任务，其中输入和输出都是文本序列。关键特性：使用统一的文本到文本框架，通过跨度破坏进行预训练，并可以针对特定任务进行微调，如摘要、翻译和问答。应用：文本摘要、机器翻译、问答系统、文本分类和句子重写。优势：简化的架构、可扩展性强、适应多任务。局限性：需要大量计算资源，较大模型推理速度较慢。

PreviousBARTNextGenAI

Last updated 7 months ago