From 27a5a363eed5e9c744977274612c2d063b738543 Mon Sep 17 00:00:00 2001
From: Yuan <yuant614@gmail.com>
Date: Tue, 14 Feb 2023 00:58:22 +0800
Subject: [PATCH] docs(zh-cn): 33_the-push-to-hub-api-(pytorch).srt (#473)

---
 .../33_the-push-to-hub-api-(pytorch).srt      | 182 +++++++++---------
 1 file changed, 91 insertions(+), 91 deletions(-)

diff --git a/subtitles/zh-CN/33_the-push-to-hub-api-(pytorch).srt b/subtitles/zh-CN/33_the-push-to-hub-api-(pytorch).srt
index e27897b99..475489f28 100644
--- a/subtitles/zh-CN/33_the-push-to-hub-api-(pytorch).srt
+++ b/subtitles/zh-CN/33_the-push-to-hub-api-(pytorch).srt
@@ -15,13 +15,13 @@
 
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 00:00:05,130 --> 00:00:06,830
-- [Instructor] 所以推送到 hub API。
-- [Instructor] So push to hub API.
+- [Instructor] push_to_hub API。
+- [Instructor] So push_to_hub API.
 
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 00:00:08,310 --> 00:00:10,533
-让我们看一下推送到集线器 API。
-Let's have a look at the push to hub API.
+让我们看一下 push_to_hub API。
+Let's have a look at the push_to_hub API.
 
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 00:00:11,730 --> 00:00:14,640
@@ -30,12 +30,12 @@ You will need to be logged in with your Hugging Face account
 
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-你可以通过执行第一个单元格来做到这一点，
+你可以通过执行第一个单元格里的操作来登录，
 which you can do by executing this first cell,
 
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 00:00:17,400 --> 00:00:21,123
-或者在终端中输入 huggingface-cli login。
+或者在 terminal 中输入 huggingface-cli login。
 or by typing huggingface-cli login in a terminal.
 
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@@ -45,37 +45,37 @@ Just enter you username and password, then click login,
 
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 00:00:26,640 --> 00:00:28,620
-这将存储一个通知令牌
-this will store a notification token
+登录后将存储一个授权 token
+this will store a authentication token
 
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 00:00:28,620 --> 00:00:30,670
-在你正在使用的机器的缓存中。
+到你正在使用的机器的缓存中。
 in the cache of the machine you're using.
 
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 00:00:31,890 --> 00:00:35,790
-现在，让我们对 BERT 模型进行微调
+现在，让我们基于 GLUE COLA 数据集
 Now, let's launch a fine tuning of a BERT model
 
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 00:00:35,790 --> 00:00:37,920
-在 GLUE COLA 数据集上。
+对 BERT 模型进行微调。
 on the GLUE COLA dataset.
 
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 00:00:37,920 --> 00:00:39,600
-我们不会讨论微调代码
+我们不会深入探讨微调代码
 We won't go over the fine tuning code
 
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 00:00:39,600 --> 00:00:42,270
-因为你可以在任何变压器教程中找到它，
+你可以在任何 transformer 教程
 because you can find it in any transformer tutorial,
 
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 00:00:42,270 --> 00:00:44,670
-或通过查看下面的视频链接。
+或查看下面的视频链接找到相关参考。
 or by looking at the videos link below.
 
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@@ -85,7 +85,7 @@ What interests us here is
 
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-我们如何在训练期间利用模型中心。
+如何在训练期间利用 model hub。
 how we can leverage the model hub during training.
 
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@@ -95,72 +95,72 @@ This is done with the "push_to_hub=true" argument
 
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 00:00:52,980 --> 00:00:55,530
-传入你的 TrainingArguments。
+将该参数添加到你的 TrainingArguments。
 passed in your TrainingArguments.
 
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 00:00:55,530 --> 00:00:57,240
-这将自动上传你的模型
+每次保存时将自动上传
 This will automatically upload your model
 
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 00:00:57,240 --> 00:00:59,400
-每次保存到集线器，
+你的模型到 Hub，在我们的示例中，
 to the Hub each time it is saved,
 
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 00:00:59,400 --> 00:01:01,323
-所以我们案例中的每个时代。
+每个 epoch 都会如此操作。
 so every epoch in our case.
 
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 00:01:02,280 --> 00:01:04,860
-这允许你从不同的机器恢复训练
+如果当前的被打断
 This allows you to resume training from a different machine
 
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 00:01:04,860 --> 00:01:06,873
-如果当前的被打断。
+这允许你从不同的机器恢复训练之前的训练。
 if the current one gets interrupted.
 
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 00:01:08,220 --> 00:01:10,440
-该模型将在你的名称空间中更新
-The model will be updated in your name space
+该模型将使用
+The model will be updated in your namespace
 
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 00:01:10,440 --> 00:01:14,640
-使用你默认选择的输出目录的名称。
+你默认选择的输出目录的名称在你的 namespace 中更新。
 with the name of the output directory you picked by default.
 
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 00:01:14,640 --> 00:01:16,020
-你可以选择其他名称
+你可以通过将其
 You can choose another name
 
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 00:01:16,020 --> 00:01:19,113
-通过将其传递给 hub_model_id 参数。
+传递给 hub_model_id 参数选择其他名称。
 by passing it to the hub_model_id argument.
 
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 00:01:20,070 --> 00:01:23,370
-你还可以推动你所属的组织内部
+你还可以通过传递完整的仓库名称
 You can also push inside an organization you are a member of
 
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 00:01:23,370 --> 00:01:25,740
-通过传递完整的存储库名称，
+将模型 push 到你所属的组织内部，
 by passing a full repository name,
 
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 00:01:25,740 --> 00:01:28,933
-以组织名称 /,
+使用 organization/ 的形式,
 with the name of the organization/,
 
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 00:01:28,933 --> 00:01:30,433
-你要选择的型号 ID。
+再加上你所选的 model ID。
 the model ID you want to pick.
 
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@@ -175,38 +175,38 @@ and wait a little bit.
 
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 00:01:36,960 --> 00:01:39,033
-我会从视频中缩短等待时间。
+视频中会跳过等待的过程。
 I'll cut the waiting time from the video.
 
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 00:01:43,260 --> 00:01:46,350
-请注意，模型是异步推送的，
+请注意，模型是异步 push 的，
 Note that the model is pushed asynchronously,
 
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 00:01:46,350 --> 00:01:47,730
-意味着训练继续
+意味着当你的模型上传到 hub 时，
 meaning that the training continues
 
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 00:01:47,730 --> 00:01:49,730
-当你的模型上传到集线器时。
+训练将继续进行。
 while your model is uploaded to the hub.
 
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 00:01:51,060 --> 00:01:52,950
-当你的第一次提交完成时，
+当你的第一次 commit 完成时，
 When your first commit is finished,
 
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 00:01:52,950 --> 00:01:55,650
-你可以去中心检查你的模型
+你可以通过查看你的 namespace 
 you can go inspect your model on the Hub
 
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 00:01:55,650 --> 00:01:57,960
-通过查看你的名称空间，
-by looking inside your name space,
+去 Hub 检查你的模型，
+by looking inside your namespace,
 
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 00:01:57,960 --> 00:01:59,943
@@ -215,22 +215,22 @@ and you'll find it at the very top.
 
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 00:02:01,980 --> 00:02:04,200
-你甚至可以开始使用它的推理小部件
+你甚至可以在继续训练的同时
 You can even start playing with its inference widget
 
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 00:02:04,200 --> 00:02:06,630
-在继续训练的同时。
+开始使用它的 inference 小部件。
 while it's continuing the training.
 
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 00:02:06,630 --> 00:02:09,270
 Cola 数据集让模型确定
-The Cola data set tasks the model with determining
+The Cola dataset tasks the model with determining
 
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 00:02:09,270 --> 00:02:11,970
-如果句子在语法上是正确的。
+句子在语法上是否是正确的。
 if the sentence is grammatically correct on that.
 
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@@ -240,102 +240,102 @@ So we pick an example of incorrect sentence to test it.
 
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 00:02:15,510 --> 00:02:16,950
-请注意，这需要一些时间
+请注意，第一次尝试使用它时，
 Note that it'll take a bit of time
 
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-在推理 API 中加载模型，
+这需要一些时间才能在 inference API 中
 to load your model inside the inference APIs,
 
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 00:02:18,750 --> 00:02:20,880
-所以第一次尝试使用它。
+完成模型加载。
 so first time you try to use it.
 
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 00:02:20,880 --> 00:02:23,280
-我们将按时间从视频中删减。
+我们将根据时间从视频中删掉。
 We'll cut by time from the video.
 
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 00:02:23,280 --> 00:02:24,870
-标签有问题，
+标签有点问题，
 There is something wrong with the labels,
 
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 00:02:24,870 --> 00:02:27,360
-但我们稍后会在本视频中修复它。
+我们稍后会在本视频中修复它。
 but we'll fix it later in this video.
 
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 00:02:27,360 --> 00:02:29,520
-一旦你的训练结束，
+一旦你的训练完成，
 Once your training is finished,
 
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 00:02:29,520 --> 00:02:31,770
-你应该和教练一起做最后一击
+你应该使用 trainer.push_to_hub 方法
 you should do one last push with the trainer
 
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-推到一个方法。
+最后再提交一次。
 that pushed to a method.
 
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-这是有两个原因的。
+这其中有两个原因。
 This is for two reason.
 
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-首先，这将确保
+首先，若你尚未完成
 First, this will make sure
 
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 00:02:36,750 --> 00:02:39,180
-你正在预测模型的最终版本
+这将确保你正在预测模型的
 you are predicting the final version of your model
 
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 00:02:39,180 --> 00:02:40,680
-如果你还没有。
+最终版本。
 if you didn't already.
 
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 00:02:40,680 --> 00:02:42,480
-例如，如果你曾经保存
+例如，如果你曾经是在每一步保存
 For instance, if you used to save
 
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 00:02:42,480 --> 00:02:46,980
-每一步策略而不是每秒，
+而不是每秒保存，
 every in step strategy instead of every second,
 
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 00:02:46,980 --> 00:02:48,180
-这将起草一张模型卡
+这将创建一个 model card
 this will draft a model card
 
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 00:02:48,180 --> 00:02:51,120
-那将是你的模型回购的登陆页面。
+那将是你的 model repo 的最初始页面。
 that will be the landing page of your model repo.
 
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 00:02:51,120 --> 00:02:52,260
-提交完成后，
+commit 完成后，
 Once the commit is done,
 
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 00:02:52,260 --> 00:02:54,810
-让我们回到我们的模型页面并刷新。
+让我们回到我们的 model 页面并刷新。
 let's go back on our model page and refresh.
 
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 00:02:54,810 --> 00:02:56,820
-我们可以看到制图者模型卡
+我们可以看到 model card 的草稿
 We can see the drafters model card
 
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@@ -345,17 +345,17 @@ which includes information,
 
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 00:02:58,080 --> 00:03:00,381
-我们发现调整了哪一种模型。
+以及哪个模型被调整过。
 and which one model we find tuned.
 
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 00:03:00,381 --> 00:03:03,570
-所以最终评估损失和指标，
+接下来是最终的评估 loss 和 metric，
 So final evaluation loss and metric,
 
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 00:03:03,570 --> 00:03:06,300
-使用的训练超参数，
+使用过的训练超参数，
 the training hyperparameter used,
 
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@@ -380,42 +380,42 @@ On top of all that information,
 
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-培训师还包括一些解释的元数据
+trainer 还包括一些 metadata，它可以
 the trainer also included some metadata that is interpreted
 
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 00:03:19,740 --> 00:03:22,650
-通过模型云中的 Hugging Face 网站。
+通过 model cloud 上的 HuggingFace 网站解析。
 by the Hugging Face website in the model cloud.
 
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 00:03:22,650 --> 00:03:26,010
-你获得了一个漂亮的小部件中报告的指标的价值
+你将会得到一个漂亮的 widget 所返回的相关指标数值
 You get the value of the metrics reported in a nice widget
 
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-以及带有代码的论文排行榜的链接。
+以及一个链接指向 leaderboard(Paper with Code)。
 as well as a link to a leaderboard with paper with code.
 
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-所以 Tensorboard runs 也被推送了
+并且 Tensorboard 的运行结果也包含
 So the Tensorboard runs have also been pushed
 
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-到这份报告，我们可以看看他们
+在这份报告中，我们可以在 Model Hub 中
 to this report, and we can look at them
 
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-直接从模型中心
+通过点击子菜单中的 
 directly from the model hub
 
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-通过单击训练指标子菜单。
+Training metrics 查看报告。
 by clicking on the training metrics sub menu.
 
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@@ -430,52 +430,52 @@ to fine-tune your model,
 
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-你可以使用 push_to_hub 方法
+你可以在模型上直接
 you can use a push_to_hub method
 
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-在模型上，并直接标记器。
+使用 push_to_hub 方法和分词器。
 on the model, and tokenizer directly.
 
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-让我们测试一下以修复推理小部件中的所有标签。
+让我们测试一下以修复 inference widget 中的所有标签。
 Let's test this to fix all labels in the inference widget.
 
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-推理小部件使用不同的标签名称
+inference widget 使用不同的标签名称
 The inference widget was using different names for labels
 
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 00:03:52,740 --> 00:03:54,810
-因为我们没有注明对应
+因为我们没有在整数和标签名称之间
 because we did not indicate the correspondence
 
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-在整数和标签名称之间。
+注明关联性。
 between integer and label names.
 
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-我们可以在配置中解决这个问题
+当推送模型配置到 hub 时，  
 We can fix this in the configuration
 
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-通过坐在 label2id，
-by sitting the label2id,
+我们可以通过将 label2id 
+by setting the label2id,
 
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-和 id2label 字段通过适当的值
+和 id2label 字段设置为合适的值
 and id2label fields through the proper values
 
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-将模型配置推送到集线器时。
+在配置中解决这个问题。
 when pushing the model config to the hub.
 
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@@ -490,7 +490,7 @@ and the model is now showing the proper label.
 
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 00:04:13,380 --> 00:04:15,240
-现在模型在集线器上，
+现在模型在 hub 上，
 Now that the model is on the hub,
 
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@@ -500,7 +500,7 @@ we can use it from anywhere
 
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 00:04:17,370 --> 00:04:19,920
-就像我们对任何其他 Transformer 模型一样
+就像我们对任何其他 Transformer 模型
 as we would any other Transformer model
 
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@@ -510,12 +510,12 @@ with the from_pretrained method
 
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-具有管道功能。
-of with the pipeline function.
+或者使用 pipeline 函数。
+or with the pipeline function.
 
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-我们只需要使用集线器的标识符，
+我们只需要使用 hub 的标识符，
 We just have to use the identifier from the hub,
 
 105
@@ -525,12 +525,12 @@ and we can see that the model configuration and weights
 
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 00:04:39,450 --> 00:04:42,483
-以及标记化的文件会自动下载。
+以及分词处理后的文件会自动下载。
 as well as the tokenized files are automatically downloaded.
 
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 00:04:53,880 --> 00:04:55,950
-在下一次培训中尝试 push_to_hub API
+在下一次训练中尝试 push_to_hub API
 Try the push_to_hub API in the next training
 
 108