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（空气呼啸）
(air whooshing)

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- 什么是转移学习？
- What is transfer learning?

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转移学习的思想
The idea of transfer learning

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是利用在另一项任务上使用大量数据训练的模型结果
is to leverage the knowledge acquired

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来获取知识。
by a model trained with lots of data on another task.

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模型 A 将专门针对任务 A 进行训练。
The model A will be trained specifically for task A.

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现在假设您想为了另一个任务
Now let's say you want to train a model B

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训练模型 B。
for a different task.

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一种选择是从头开始训练模型。
One option would be to train the model from scratch.

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但这可能需要大量的计算、时间和数据。
This could take lots of computation, time and data.

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我们可以有另一种选择，初始化模型 B
Instead, we could initialize model B

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它与模型 A 具有相同的权重，
with the same weights as model A,

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将模型 A 的知识转移到任务 B 上。
transferring the knowledge of model A on task B.

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从头开始训练时，
When training from scratch,

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所有模型的权重都是随机初始化的。
all the model's weight are initialized randomly.

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在这个例子中，我们正在基于识别任务上
In this example, we are training a BERT model

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训练一个 BERT 模型
on the task of recognizing

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来判断两个句子是否相似。
if two sentences are similar or not.

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左边的例子是从头开始训练的，
On the left, it's trained from scratch,

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右边则代表正在微调预训练模型。
and on the right it's fine-tuning a pretrained model.

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正如我们所见，使用转移学习
As we can see, using transfer learning

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和预训练模型产生了更好的结果。
and the pretrained model yields better results.

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如果我们训练更长时间也没关系。
And it doesn't matter if we train longer.

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从头开始的训练准确率上限在 70% 左右
The training from scratch is capped around 70% accuracy

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而预训练模型轻松达到了 86%。
while the pretrained model beats the 86% easily.

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这是因为预训练模型
This is because pretrained models

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通常基于大量数据进行训练
are usually trained on large amounts of data

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这些数据为模型在预训练期间
that provide the model with a statistical understanding

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提供了对语言使用的统计理解。
of the language used during pretraining.

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在计算机视觉中，
In computer vision,

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转移学习已成功应用
transfer learning has been applied successfully

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将近十年。
for almost ten years.

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模型经常在 ImageNet 上进行预训练，
Models are frequently pretrained on ImageNet,

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它是一种包含 120 万张照片图像的数据集。
a dataset containing 1.2 millions of photo images.

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每个图像都按 1000 个标签中的一个进行分类。
Each image is classified by one of 1000 labels.

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像这样在标记数据上训练
Training like this, on labeled data

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称为监督学习。
is called supervised learning.

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在自然语言处理中，
In Natural Language Processing,

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转移学习是最近才出现的。
transfer learning is a bit more recent.

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它与 ImageNet 的一个关键区别是预训练
A key difference with ImageNet is that the pretraining

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通常是自我监督的，
is usually self-supervised,

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这意味着它不需要人工对标签
which means it doesn't require humans annotations

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进行注释。
for the labels.

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一个非常常见的预训练目标
A very common pretraining objective

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是猜测句子中的下一个单词。
is to guess the next word in a sentence.

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它只需要大量的输入文本。
Which only requires lots and lots of text.

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例如 GPT-2，就是这样预训练的
GPT-2 for instance, was pretrained this way

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它使用 4500 万个用户在 Reddit 上发布的
using the content of 45 millions links

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链接的内容。
posted by users on Reddit.

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自监督预训练目标的另一个例子
Another example of self-supervised pretraining objective

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是预测随机屏蔽词的值。
is to predict the value of randomly masked words.

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这类似于填空测试
Which is similar to fill-in-the-blank tests

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您可能在学校做过。
you may have done in school.

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BERT 是使用英文维基百科和 11,000 本未出版的书籍
BERT was pretrained this way using the English Wikipedia

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进行预训练的。
and 11,000 unpublished books.

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在实践中，转移学习是通过抛弃原模型的头部
In practice, transfer learning is applied on a given model

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即其针对预训练目标的最后几层
by throwing away its head, that is,

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并用一个新的、随机初始化的头部
its last layers focused on the pretraining objective,

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来替换它来应用的
and replacing it with a new, randomly initialized head

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这个新的头部适用于当前的任务。
suitable for the task at hand.

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例如，当我们之前微调 BERT 模型时，
For instance, when we fine-tuned a BERT model earlier,

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我们删除了分类掩码词的头部
we removed the head that classified mask words

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并将其替换为具有 2 个输出的分类器。
and replaced it with a classifier with 2 outputs.

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因为我们的任务有两个标签。
Since our task had two labels.

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为了尽可能高效
To be as efficient as possible, the pretrained model used

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所使用的预训练模型
should be as similar as possible

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应尽可能与其微调的任务相似。
to the task it's fine-tuned on.

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例如，如果当前需要
For instance, if the problem

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对德语句子进行分类，
is to classify German sentences,

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最好使用德语预训练模型。
it's best to use a German pretrained model.

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但有好事也有坏事。
But with the good comes the bad.

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预训练模型不仅转移了它的知识，
The pretrained model does not only transfer its knowledge,

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同时也转移了它可能包含的任何偏见。
but also any bias it may contain.

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ImageNet 主要包含来自美国和西欧
ImageNet mostly contains images

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的图像。
coming from the United States and Western Europe.

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所以基于它进行微调的模型
So models fine-tuned with it

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通常会在来自这些国家或地区的图像上表现更好。
usually will perform better on images from these countries.

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OpenAI 还研究了
OpenAI also studied the bias

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其使用猜测下一个单词目标
in the predictions of its GPT-3 model

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预训练的 GPT-3 模型中
which was pretrained

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预测的偏差。
using the guess the next word objective.

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将提示的性别
Changing the gender of the prompt

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从“他”更改到“她”
from he was very to she was very

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会使预测从主要是中性形容词
changed the predictions from mostly neutral adjectives

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变为几乎只有物理上的形容词。
to almost only physical ones.

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在他们的 GPT-2 的模型卡中，
In their model card of the GPT-2 model,

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OpenAI 也承认了它的偏见
OpenAI also acknowledges its bias

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并且不鼓励在与人类交互的系统中
and discourages its use

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使用它。
in systems that interact with humans.

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（空气呼啸）
(air whooshing)

