引导式测验 · 中等 等级

BERT 和编码器模型测验

回顾 BERT 上下文、屏蔽预训练、编码器、适应、表示、高效变体、下一句目标和输入嵌入。

相关引导路径Bert 和编码器模型
问题 1 的 8

原始 BERT 编码器输入中的双向注意力允许哪种上下文访问？

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  1. 原始 BERT 编码器输入中的双向注意力允许哪种上下文访问？
    • Information from both left and right within the allowed input
    • Only the previous token in every layer
    • Only a second unrelated language
    • A guarantee of two correct answers to every question
  2. 哪个原始 BERT 预训练目标使用周围的上下文来预测选定的词元身份？
    • Only next-token prediction under a causal mask
    • Only human-written class labels for complete documents
    • Only a fixed image reconstruction objective
    • 掩码语言建模
  3. 哪个 Transformer 组件提供了原始 BERT 表示架构？
    • Only the autoregressive decoder
    • The encoder
    • A recurrent-only sequence network
    • A convolution-only image decoder
  4. BERT 预训练后接下游微调说明了什么训练阶段序列？
    • Always train a completely new network from random parameters for every example
    • Apply the task head before creating any learned representation
    • Learn a corpus-based starting representation, then adapt it to a defined task
    • Guarantee that every target task uses no labeled data
  5. 原始 BERT 编码器可以为分类或跨度预测头部提供什么输出？
    • Only a long autoregressive continuation in every task
    • 原始音频波形
    • Pixel-level image output
    • Contextual text representations
  6. Which comparison accurately distinguishes the parameter-related designs of DistilBERT and ALBERT?
    • DistilBERT uses distillation; ALBERT uses factorized embeddings and cross-layer sharing
    • Both are guarantees of identical speed and accuracy on every task
    • Both replace all text input with image pixels
    • Neither retains any learned encoder representation
  7. Which label distinction is used in the original BERT next-sentence prediction task?
    • A positive versus negative product review
    • A token's verb tense versus noun number
    • An actual next sentence versus a randomly sampled alternative
    • A source sentence's font size versus text color
  8. Which three representations are added in the original BERT input embedding?
    • Only a single corpus-frequency number
    • Token, position, and segment embeddings
    • Decoder probabilities, image pixels, and audio pitch
    • Only one fixed vector shared by every input position