subtitles/zh-CN/24_the-trainer-api.srt

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- Trainer API。
- The Trainer API.

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Transformers Library 提供了一个 Trainer API
The Transformers Library provides a Trainer API

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让你能够轻松的微调 Transformer 模型
that allows you to easily fine-tune transformer models

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在你自己的数据集上。
on your own dataset.

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Trainer 类接受你的数据集，
The Trainer class take your datasets,

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你的模型以及训练超参数
your model as well as the training hyperparameters

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并且可以在任何类型的设置上进行训练，
and can perform the training on any kind of setup,

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(CPU、GPU、多个 GPU、TPUs)
(CPU, GPU, multiple GPUs, TPUs)

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也可以在任意数据集上进行推理
can also compute the predictions

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如果你提供了指标
on any dataset and if you provided metrics

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就可以在任意数据集上评估你的模型。
evaluate your model on any dataset.

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Trainer 也可以负责最后的数据处理
It can also handle final data processing

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比如动态填充，
such as dynamic padding,

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只要你提供 tokenizer
as long as you provide the tokenizer

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或给定 data collator。
or a given data collator.

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我们将在 MRPC 数据集上尝试这个 API，
We will try this API on the MRPC dataset,

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因为该数据集相对较小且易于预处理。
since it's relatively small and easy to preprocess.

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正如我们在 Datasets 概述视频中看到的那样，
As we saw in the Datasets overview video,

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我们可以像这样对其进行预处理。
here is how we can preprocess it.

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我们在预处理过程中不进行填充，
We do not apply padding during the preprocessing,

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因为我们将进行动态填充
as we will use dynamic padding

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使用我们的 DataCollatorWithPadding。
with our DataCollatorWithPadding.

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请注意，我们不执行一些最终的数据处理步骤
Note that we don't do the final steps

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像是重命名列/删除列
of renaming/removing columns

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或将格式设置为 torch 张量。
or set the format to torch tensors.

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Trainer 会自动为我们做这一切
The Trainer will do all of this automatically for us

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通过分析模型签名。
by analyzing the model signature.

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实例化 Trainer 前的最后一步是
The last step before creating the Trainer are

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定义模型
to define a model

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和一些训练超参数。
and some training hyperparameters.

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我们在 model API 视频中学会了如何定义模型。
We saw to do the first in the model API video.

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对于第二点，我们使用 TrainingArguments 类。
For the second we use the TrainingArguments class.

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Trainer 只需要一个文件夹的路径
It only takes a path to a folder

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用以保存结果和检查点，
where results and checkpoint will be saved,

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但你也可以自定义你的 Trainer 会使用的
but you can also customize all the hyperparameters

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所有超参数
your Trainer will use,

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比如学习率，训练几个 epoch 等等。
learning rate, number of training epochs etc.

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接下来实例化一个 Trainer 并开始训练
It's then very easy to create a Trainer

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就非常简单了。
and launch a training.

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这会显示一个进度条
This should display a progress bar

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几分钟后(如果你在 GPU 上运行)
and after a few minutes (if you're running on a GPU)

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你会完成训练。
you should have the training finished.

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然而，结果将是相当虎头蛇尾，
The result will be rather anticlimactic however,

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因为你只会得到训练损失
as you will only get a training loss

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这并没有真正告诉你
which doesn't really tell you anything

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你的模型表现如何。
about how well your model is performing.

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这是因为我们没有指定任何指标
This is because we didn't specify any metric

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用于评估。
for the evaluation.

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要获得这些指标，
To get those metrics,

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我们将首先使用预测方法
we will first gather the predictions

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在整个评估集上收集预测结果。
on the whole evaluation set using the predict method.

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它返回一个包含三个字段的命名元组，
It returns a namedtuple with three fields,

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Prediction （其中包含模型的预测），
Prediction(which contains the model predictions),

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Label_IDs （其中包含标签
Label_IDs(which contains the labels

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如果你的数据集有的话）
if your dataset had them）

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和指标（在本示例中是空的）
and metrics (which is empty here).

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我们正在努力做到这一点。
We're trying to do that.

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预测结果是模型
The predictions are the logits of the models

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对于数据集中的所有句子所输出的 logits。
for all the sentences in the dataset.

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所以是一个形状为 408 x 2 的 NumPy 数组。
So a NumPy array of shape 408 by 2.

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为了将它们与我们的标签相匹配，
To match them with our labels,

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我们需要取最大的 logit
we need to take the maximum logit

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对于每个预测
for each prediction

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（知道两个类别中的哪一个类是所预测的结果）
(to know which of the two classes was predicted.)

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我们使用 argmax 函数来做到这一点。
We do this with the argmax function.

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然后我们可以使用 Datasets library 中的指标。
Then we can use a metric from the Datasets library.

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它可以像数据集一样被轻松地加载
It can be loaded as easily as a dataset

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使用 load_metric 函数
with the load_metric function

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并且返回用于该数据集的评估指标。
and it returns the evaluation metric used for the dataset.

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我们可以看到我们的模型确实学到了一些东西
We can see our model did learn something

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因为它有 85.7% 的准确率。
as it is 85.7% accurate.

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为了在训练期间监控评估指标，
To monitor the evaluation metrics during training,

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我们需要定义一个 compute_metrics 函数
we need to define a compute_metrics function

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和以前一样的步骤。
that does the same step as before.

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它接收一个带有预测和标签的命名元组
It takes a namedtuple with predictions and labels

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并且返回一个字典
and must return a dictionary

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包含我们想要跟踪的指标。
with the metrics we want to keep track of.

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通过将评估策略设置为 epoch
By passing the epoch evaluation strategy

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对于我们的 TrainingArguments，
to our TrainingArguments,

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我们告诉 Trainer 去进行评估
we tell the Trainer to evaluate

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在每个 epoch 结束的时候。
at the end of every epoch.

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在 notebook 中启动训练
Launching a training inside a notebook

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会显示一个进度条
will then display a progress bar

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并在你运行完每个 epoch 时将数据填到你看到的这个表格。
and complete the table you see here as you pass every epoch.

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(air whooshing)

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