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			Pytorch
		</a><a class="flex justify-center flex-1 py-1.5 px-2.5 focus:outline-none !no-underline rounded-r text-gray-500 filter grayscale" href="?fw=tf"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg>
			TensorFlow
		</a></div>
<h1 class="relative group"><a id="trainer-api" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#trainer-api"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>使用 Trainer API 微调模型
	</span></h1>



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	<a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/zh-CN/chapter3/section3.ipynb" target="_blank"><img alt="Open In Colab" class="!m-0" src="https://colab.research.google.com/assets/colab-badge.svg"></a>
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<p>🤗 Transformers提供了一个 <strong>Trainer</strong> 类来帮助您在自己的数据集上微调任何预训练模型。完成上一节中的所有数据预处理工作后，您只需要执行几个步骤来创建 <strong>Trainer</strong> .最难的部分可能是为 <strong>Trainer.train()</strong>配置运行环境，因为它在 CPU 上运行速度会非常慢。如果您没有设置 GPU，您可以访问免费的 GPU 或 TPU<a href="https://colab.research.google.com/" rel="nofollow">Google Colab</a>.</p>
<p>下面的示例假设您已经执行了上一节中的示例。下面这段代码，概括了您需要提前运行的代码：</p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, DataCollatorWithPadding

raw_datasets = load_dataset(<span class="hljs-string">&quot;glue&quot;</span>, <span class="hljs-string">&quot;mrpc&quot;</span>)
checkpoint = <span class="hljs-string">&quot;bert-base-uncased&quot;</span>
tokenizer = AutoTokenizer.from_pretrained(checkpoint)


<span class="hljs-keyword">def</span> <span class="hljs-title function_">tokenize_function</span>(<span class="hljs-params">example</span>):
    <span class="hljs-keyword">return</span> tokenizer(example[<span class="hljs-string">&quot;sentence1&quot;</span>], example[<span class="hljs-string">&quot;sentence2&quot;</span>], truncation=<span class="hljs-literal">True</span>)


tokenized_datasets = raw_datasets.<span class="hljs-built_in">map</span>(tokenize_function, batched=<span class="hljs-literal">True</span>)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)<!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="training" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#training"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Training
	</span></h3>

<p>在我们定义我们的 <strong>Trainer</strong> 之前首先要定义一个 <strong>TrainingArguments</strong> 类，它将包含 <strong>Trainer</strong>用于训练和评估的所有超参数。您唯一必须提供的参数是保存训练模型的目录，以及训练过程中的检查点。对于其余的参数，您可以保留默认值，这对于基本微调应该非常有效。</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TrainingArguments

training_args = TrainingArguments(<span class="hljs-string">&quot;test-trainer&quot;</span>)<!-- HTML_TAG_END --></pre></div>


<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>💡 如果您想在训练期间自动将模型上传到 Hub，请将push_to_hub=True添加到TrainingArguments之中. 我们将在<a href="/course/chapter4/3">第四章</a>中详细介绍这部分。</p></div>
<p>第二步是定义我们的模型。正如在[之前的章节](/2_Using Transformers/Introduction)一样，我们将使用 <strong>AutoModelForSequenceClassification</strong> 类，它有两个参数：</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=<span class="hljs-number">2</span>)<!-- HTML_TAG_END --></pre></div>
<p>你会注意到，和<a href="/course/chapter2">第二章</a>不一样的是，在实例化此预训练模型后会收到警告。这是因为 BERT 没有在句子对分类方面进行过预训练，所以预训练模型的头部已经被丢弃，而是添加了一个适合句子序列分类的新头部。警告表明一些权重没有使用（对应于丢弃的预训练头的那些），而其他一些权重被随机初始化（新头的那些）。最后鼓励您训练模型，这正是我们现在要做的。</p>
<p>一旦我们有了我们的模型，我们就可以定义一个 <strong>Trainer</strong> 通过将之前构造的所有对象传递给它——我们的<strong>model</strong> 、<strong>training_args</strong> ，训练和验证数据集，<strong>data_collator</strong> ，和 <strong>tokenizer</strong> ：</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Trainer

trainer = Trainer(
    model,
    training_args,
    train_dataset=tokenized_datasets[<span class="hljs-string">&quot;train&quot;</span>],
    eval_dataset=tokenized_datasets[<span class="hljs-string">&quot;validation&quot;</span>],
    data_collator=data_collator,
    tokenizer=tokenizer,
)<!-- HTML_TAG_END --></pre></div>
<p>请注意，当您在这里完成<strong>tokenizer</strong>后，默认 <strong>Trainer</strong>使用 的<strong>data_collator</strong>会使用之前预定义的 <strong>DataCollatorWithPadding</strong> ，因此您可以在这个例子中跳过 <strong>data_collator=data_collator</strong>。在第 2 节中向您展示这部分处理仍然很重要！</p>
<p>为了让预训练模型在在我们的数据集上微调，我们只需要调用<strong>Trainer</strong>的<strong>train()</strong> 方法 ：</p>

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	<pre><!-- HTML_TAG_START -->trainer.train()<!-- HTML_TAG_END --></pre></div>
<p>这将开始微调（在GPU上应该需要几分钟），并每500步报告一次训练损失。但是，它不会告诉您模型的性能如何（或质量如何）。这是因为:</p>
<ol><li>我们没有通过将<strong>evaluation_strategy</strong>设置为“<strong>steps</strong>”(在每次更新参数的时候评估)或“<strong>epoch</strong>”(在每个epoch结束时评估)来告诉<strong>Trainer</strong>在训练期间进行评估。</li>
<li>我们没有为<strong>Trainer</strong>提供一个<strong>compute_metrics()</strong>函数来直接计算模型的好坏(否则评估将只输出loss，这不是一个非常直观的数字)。</li></ol>
<h3 id="">评估</h3>
<p>让我们看看如何构建一个有用的 <strong>compute_metrics()</strong> 函数并在我们下次训练时使用它。该函数必须采用 <strong>EvalPrediction</strong> 对象（带有 <strong>predictions</strong> 和 <strong>label_ids</strong> 字段的参数元组）并将返回一个字符串到浮点数的字典（字符串是返回的指标的名称，而浮点数是它们的值）。我们可以使用 <strong>Trainer.predict()</strong> 命令来使用我们的模型进行预测：</p>

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	<pre><!-- HTML_TAG_START -->predictions = trainer.predict(tokenized_datasets[<span class="hljs-string">&quot;validation&quot;</span>])
<span class="hljs-built_in">print</span>(predictions.predictions.shape, predictions.label_ids.shape)<!-- HTML_TAG_END --></pre></div>

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	<pre><!-- HTML_TAG_START -->(<span class="hljs-number">408</span>, <span class="hljs-number">2</span>) (<span class="hljs-number">408</span>,)<!-- HTML_TAG_END --></pre></div>
<p><strong>predict()</strong> 的输出结果是具有三个字段的命名元组： <strong>predictions</strong> , <strong>label_ids</strong> ， 和 <strong>metrics</strong> .这 <strong>metrics</strong> 字段将只包含传递的数据集的loss，以及一些运行时间（预测所需的总时间和平均时间）。如果我们定义了自己的 <strong>compute_metrics()</strong> 函数并将其传递给 <strong>Trainer</strong> ，该字段还将包含<strong>compute_metrics()</strong>的结果。</p>
<p><strong>predict()</strong> 方法是具有三个字段的命名元组： <strong>predictions</strong> , <strong>label_ids</strong> ， 和 <strong>metrics</strong> .这 <strong>metrics</strong> 字段将只包含传递的数据集的loss，以及一些运行时间（预测所需的总时间和平均时间）。如果我们定义了自己的 <strong>compute_metrics()</strong> 函数并将其传递给 <strong>Trainer</strong> ，该字段还将包含<strong>compute_metrics()</strong> 的结果。如你看到的， <strong>predictions</strong> 是一个形状为 408 x 2 的二维数组（408 是我们使用的数据集中元素的数量）。这些是我们传递给<strong>predict()</strong>的数据集的每个元素的结果(logits)（正如你在<a href="/course/chapter2">之前的章节</a>看到的情况）。要将我们的预测的可以与真正的标签进行比较，我们需要在第二个轴上取最大值的索引：</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

preds = np.argmax(predictions.predictions, axis=-<span class="hljs-number">1</span>)<!-- HTML_TAG_END --></pre></div>
<p>现在建立我们的 <strong>compute_metric()</strong> 函数来较为直观地评估模型的好坏，我们将使用 🤗 <a href="https://github.com/huggingface/evaluate/" rel="nofollow">Evaluate</a> 库中的指标。我们可以像加载数据集一样轻松加载与 MRPC 数据集关联的指标，这次使用 <strong>evaluate.load()</strong> 函数。返回的对象有一个 <strong>compute()</strong>方法我们可以用来进行度量计算的方法：</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> evaluate

metric = evaluate.load(<span class="hljs-string">&quot;glue&quot;</span>, <span class="hljs-string">&quot;mrpc&quot;</span>)
metric.compute(predictions=preds, references=predictions.label_ids)<!-- HTML_TAG_END --></pre></div>

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	<pre><!-- HTML_TAG_START -->{<span class="hljs-string">&#x27;accuracy&#x27;</span>: <span class="hljs-number">0.8578431372549019</span>, <span class="hljs-string">&#x27;f1&#x27;</span>: <span class="hljs-number">0.8996539792387542</span>}<!-- HTML_TAG_END --></pre></div>
<p>您获得的确切结果可能会有所不同，因为模型头的随机初始化可能会影响最终建立的模型。在这里，我们可以看到我们的模型在验证集上的准确率为 85.78%，F1 分数为 89.97。这是用于评估 GLUE 基准的 MRPC 数据集结果的两个指标。而在<a href="https://arxiv.org/pdf/1810.04805.pdf" rel="nofollow">BERT 论文</a>中展示的基础模型的 F1 分数为 88.9。那是 <strong>uncased</strong> 模型，而我们目前正在使用 <strong>cased</strong> 模型，通过改进得到了更好的结果。</p>
<p>最后将所有东西打包在一起，我们得到了我们的 <strong>compute_metrics()</strong> 函数：</p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">def</span> <span class="hljs-title function_">compute_metrics</span>(<span class="hljs-params">eval_preds</span>):
    metric = evaluate.load(<span class="hljs-string">&quot;glue&quot;</span>, <span class="hljs-string">&quot;mrpc&quot;</span>)
    logits, labels = eval_preds
    predictions = np.argmax(logits, axis=-<span class="hljs-number">1</span>)
    <span class="hljs-keyword">return</span> metric.compute(predictions=predictions, references=labels)<!-- HTML_TAG_END --></pre></div>
<p>为了查看模型在每个训练周期结束的好坏，下面是我们如何使用<strong>compute_metrics()</strong>函数定义一个新的 <strong>Trainer</strong> ：</p>

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	<pre><!-- HTML_TAG_START -->training_args = TrainingArguments(<span class="hljs-string">&quot;test-trainer&quot;</span>, evaluation_strategy=<span class="hljs-string">&quot;epoch&quot;</span>)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=<span class="hljs-number">2</span>)

trainer = Trainer(
    model,
    training_args,
    train_dataset=tokenized_datasets[<span class="hljs-string">&quot;train&quot;</span>],
    eval_dataset=tokenized_datasets[<span class="hljs-string">&quot;validation&quot;</span>],
    data_collator=data_collator,
    tokenizer=tokenizer,
    compute_metrics=compute_metrics,
)<!-- HTML_TAG_END --></pre></div>
<p>请注意，我们设置了了一个新的 <strong>TrainingArguments</strong> 它的<strong>evaluation_strategy</strong> 设置为 <strong>epoch</strong> 并创建了一个新模型。如果不创建新的模型就直接训练，就只会继续训练之前我们已经训练过的模型。要启动新的训练运行，我们执行：</p>

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	<pre><!-- HTML_TAG_START -->trainer.trai<span class="hljs-meta">n</span>()<!-- HTML_TAG_END --></pre></div>
<p>这一次，它将在训练loss之外，还会输出每个 epoch 结束时的验证loss和指标。同样，由于模型的随机头部初始化，您达到的准确率/F1 分数可能与我们发现的略有不同，但它应该在同一范围内。</p>
<p>这 <strong>Trainer</strong> 将在多个 GPU 或 TPU 上开箱即用，并提供许多选项，例如混合精度训练（在训练的参数中使用 <strong>fp16 = True</strong> ）。我们将在第 10 章讨论它支持的所有内容。</p>
<p>使用<strong>Trainer</strong> API微调的介绍到此结束。对最常见的 NLP 任务执行此操作的示例将在第 7 章中给出，但现在让我们看看如何在纯 PyTorch 中执行相同的操作。</p>


<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>✏️ <strong>试试看!</strong> 使用您在第 2 节中进行的数据处理，在 GLUE SST-2 数据集上微调模型。</p></div>


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