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为 Transformer 模型进行分词

子词、填充与注意力掩码

为 Transformer 模型进行分词 是 CoddyKit 上的免费 NLP Academy 课时。这是第 2 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 NLP Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 NLP Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Tokens Come First

Before a transformer can learn anything, your text must become numbers. That conversion job belongs to the tokenizer.

Match the Model

Always load the tokenizer that was trained with your model. A mismatched vocabulary produces garbage ids the model never saw.

from transformers import AutoTokenizer
tok = AutoTokenizer.from_pretrained("bert-base-uncased")

Subword Pieces

Modern tokenizers split rare words into smaller chunks called subwords, so even unknown text stays representable.

print(tok.tokenize("tokenization"))

Why Subwords Win

Subwords keep the vocabulary small while still handling typos and new words. The model rarely meets a true unknown token.

From Tokens to Ids

Each subword maps to an integer id. Calling the tokenizer on text returns those ids ready for the model.

enc = tok("Fine-tuning is fun")
print(enc["input_ids"])

Special Tokens

Tokenizers add markers like CLS and SEP so the model knows where a sequence starts and ends. These are the special tokens.

Padding to Equal Length

Batches need same-length rows, so shorter texts get filler tokens. This step is called padding.

tok(texts, padding=True)

Truncation for Long Text

Models cap input length, so very long text is cut to fit. Enabling truncation keeps every example within the limit.

tok(texts, truncation=True, max_length=128)

The Attention Mask

An attention mask marks real tokens as 1 and padding as 0, so the model ignores the filler positions.

print(enc["attention_mask"])

Return Tensors

Ask the tokenizer for framework tensors directly so the output drops straight into training without extra conversion.

tok("hello", return_tensors="pt")

Decode Back to Text

To read predictions, run ids through decode and the tokenizer rebuilds the original text, special tokens stripped.

print(tok.decode(enc["input_ids"]))

Quick Check

What is the job of the attention mask during batching?

Recap

Tokenizers turn text into subword ids, add special tokens, then handle padding, truncation, and the attention mask for clean batches. ✅

常见问题解答

「为 Transformer 模型进行分词」课时是免费的吗？

是的 — 「为 Transformer 模型进行分词」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 NLP Academy 课程的其余内容，请升级到 CoddyKit PRO。 NLP Academy 课程共包含 4 节课。

「为 Transformer 模型进行分词」这节课中我会学到什么？

子词、填充与注意力掩码你通过在浏览器中直接运行的动手代码来练习 NLP Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 NLP Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 NLP Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 2 节课，共 4 节。

「为 Transformer 模型进行分词」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 NLP Academy 课中编写并运行代码吗？

能。每节 NLP Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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