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运行 QA 流水线

从段落中提取答案

运行 QA 流水线 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。

What a Pipeline Gives You

A Hugging Face pipeline wraps a model and tokenizer into one easy call. You hand it a question and context, it hands back an answer. 🚀

Import the Helper

Everything starts with one import. The pipeline function from the transformers library builds the whole QA stack for you.

from transformers import pipeline

Create a QA Pipeline

Pass the task name question-answering to pipeline. With no model named, it downloads a sensible default for you.

qa = pipeline("question-answering")

Give It a Context

The pipeline needs a context: the passage that contains the answer. Think of it as the text the model is allowed to read.

context = "The Eiffel Tower is located in Paris and was completed in 1889."

Ask Your Question

Now call the pipeline with both a question and the context. It scans the passage and returns the best matching span.

result = qa(question="Where is the Eiffel Tower?", context=context)

Read the Result

The result is a dictionary. The answer text lives under the answer key, ready to print or store.

print(result["answer"])  # Paris

The Confidence Score

Each result also carries a score between 0 and 1. Higher means the model is more sure about its chosen span.

print(result["score"])  # e.g. 0.98

Start and End Positions

The result includes start and end character indexes. They tell you exactly where in the context the answer was found.

print(result["start"], result["end"])

Choosing a Model

You can pin a specific model by name. A popular small QA model is distilbert fine-tuned on the SQuAD dataset.

qa = pipeline("question-answering",
  model="distilbert-base-cased-distilled-squad")

Reuse the Same Context

One context can answer many questions. Build the pipeline once, then call it repeatedly with new questions on the same passage.

It Only Reads the Context

Remember: this extractive pipeline answers only from the context you give. Ask about something not in the text and you get a poor span.

Quick Check

Recall how the QA pipeline returns its result.

Recap

One pipeline call takes a question plus context and returns a dict with answer, score, start, and end. You ran extractive QA in three lines. ✅

常见问题解答

「运行 QA 流水线」课时是免费的吗？

是的 — 「运行 QA 流水线」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。

「运行 QA 流水线」这节课中我会学到什么？

从段落中提取答案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。

「运行 QA 流水线」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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