1
00:00:00,125 --> 00:00:05,125
（空气呼啸）
(air whooshing)

2
00:00:05,190 --> 00:00:06,720
- 你是在正确的地方
- You are at the right place

3
00:00:06,720 --> 00:00:10,464
如果你想了解什么是字节对编码(BPE)算法
if you want to understand what the Byte Pair Encoding

4
00:00:10,464 --> 00:00:13,263
子词分词化算法是，
subword tokenization algorithm is,

5
00:00:14,160 --> 00:00:15,505
如何训练它
how to train it

6
00:00:15,505 --> 00:00:17,790
以及文本的分词化是如何完成的
and how the tokenization of a text is done

7
00:00:17,790 --> 00:00:19,107
用这个算法。
with this algorithm.

8
00:00:21,417 --> 00:00:22,920
BPE 算法
The BPE algorithm

9
00:00:22,920 --> 00:00:26,820
最初被提出作为文本压缩算法
was initially proposed as a text compression algorithm

10
00:00:26,820 --> 00:00:28,770
但它也非常适合
but it is also very well suited

11
00:00:28,770 --> 00:00:31,143
作为你的语言模型的分词器。
as a tokenizer for your language models.

12
00:00:32,910 --> 00:00:34,890
BPE 的思想是分词
The idea of BPE is to divide words

13
00:00:34,890 --> 00:00:36,933
进入一系列 “子词单元”
into a sequence of'subword units'

14
00:00:38,100 --> 00:00:41,970
其是参考语料库中频繁出现的单位
which are units that appear frequently in a reference corpus

15
00:00:41,970 --> 00:00:44,613
也是我们用来训练它的语料库。
which is, the corpus we used to train it.

16
00:00:46,701 --> 00:00:49,083
BPE 分词器是如何训练的？
How is a BPE tokenizer trained?

17
00:00:50,100 --> 00:00:53,340
首先，我们必须得到一个文本语料库。
First of all, we have to get a corpus of texts.

18
00:00:53,340 --> 00:00:56,940
我们不会在这个原始文本上训练我们的分词器
We will not train our tokenizer on this raw text

19
00:00:56,940 --> 00:00:59,490
但我们首先将其规范化
but we will first normalize it

20
00:00:59,490 --> 00:01:00,873
然后对其进行预标记。
then pre-tokenize it.

21
00:01:01,890 --> 00:01:03,240
作为预标记化
As the pre-tokenization

22
00:01:03,240 --> 00:01:05,790
将文本分成单词列表，
divides the text into a list of words,

23
00:01:05,790 --> 00:01:08,400
我们可以用另一种方式表示我们的语料库
we can represent our corpus in another way

24
00:01:08,400 --> 00:01:10,350
通过收集相同的词
by gathering together the same words

25
00:01:10,350 --> 00:01:12,450
并通过维护一个柜，
and by maintaining a counter,

26
00:01:12,450 --> 00:01:14,223
这里用蓝色表示。
here represented in blue.

27
00:01:17,340 --> 00:01:19,860
要了解训练的工作原理，
To understand how the training works,

28
00:01:19,860 --> 00:01:23,730
我们认为这个小语料库由以下单词组成：
we consider this toy corpus composed of the following words:

29
00:01:23,730 --> 00:01:28,203
huggingface, hugging, hug, hugger, 等
huggingface, hugging, hug, hugger, etc.

30
00:01:29,100 --> 00:01:32,640
BPE 是一种从初始词汇表开始的算法
BPE is an algorithm that starts with an initial vocabulary

31
00:01:32,640 --> 00:01:35,583
然后将其增加到所需的大小。
and then increases it to the desired size.

32
00:01:36,450 --> 00:01:38,460
为了建立初始词汇表，
To build the initial vocabulary,

33
00:01:38,460 --> 00:01:41,550
我们从分离语料库的每个词开始
we start by separating each word of the corpus

34
00:01:41,550 --> 00:01:44,253
组成它们的基本单元列表，
into a list of elementary units that compose them,

35
00:01:45,210 --> 00:01:47,013
在这里, 字符。
here, the characters.

36
00:01:50,850 --> 00:01:54,310
我们在词汇表中列出所有出现的字符
We list in our vocabulary all the characters that appear

37
00:01:55,218 --> 00:01:58,053
这将构成我们最初的词汇表。
and that will constitute our initial vocabulary.

38
00:02:00,420 --> 00:02:02,523
现在让我们看看如何增加它。
Let's now see how to increase it.

39
00:02:05,520 --> 00:02:08,250
我们回到我们拆分的语料库，
We return to our split corpus,

40
00:02:08,250 --> 00:02:11,340
我们将逐字逐句
we will go through the words one by one

41
00:02:11,340 --> 00:02:14,313
并计算 token 对的所有出现次数。
and count all the occurrences of token pairs.

42
00:02:15,450 --> 00:02:18,397
第一对由标记 “h” 和 “u” 组成，
The first pair is composed of the token 'h' and 'u',

43
00:02:20,130 --> 00:02:23,067
第二个 "u" 和 "g"，
the second 'u' and 'g',

44
00:02:23,067 --> 00:02:26,253
我们继续这样，直到我们有完整的列表。
and we continue like that until we have the complete list.

45
00:02:35,580 --> 00:02:37,724
一旦我们知道所有的对
Once we know all the pairs

46
00:02:37,724 --> 00:02:40,140
以及它们出现的频率，
and their frequency of appearance,

47
00:02:40,140 --> 00:02:42,940
我们将选择出现频率最高的那个。
we will choose the one that appears the most frequently.

48
00:02:44,220 --> 00:02:47,697
这是由字母 “l” 和 “e” 组成的一对。
Here it is the pair composed of the letters 'l' and 'e'.

49
00:02:51,930 --> 00:02:53,590
我们注意到我们的第一个合并规则
We note our first merging rule

50
00:02:54,593 --> 00:02:57,243
然后我们将新 token 添加到我们的词汇表中。
and we add the new token to our vocabulary.

51
00:03:00,330 --> 00:03:04,260
然后我们可以将此合并规则应用于我们的拆分。
We can then apply this merging rule to our splits.

52
00:03:04,260 --> 00:03:07,350
你可以看到我们已经合并了所有的 token 对
You can see that we have merged all the pairs of tokens

53
00:03:07,350 --> 00:03:09,793
由标记 “l” 和 “e” 组成。
composed of the tokens 'l' and 'e'.

54
00:03:14,008 --> 00:03:18,150
现在，我们只需要重现相同的步骤
And now, we just have to reproduce the same steps

55
00:03:18,150 --> 00:03:19,353
与我们的新拆分。
with our new splits.

56
00:03:21,750 --> 00:03:23,460
我们计算出现频率
We calculate the frequency of occurrence

57
00:03:23,460 --> 00:03:25,023
对每对 token ，
of each pair of tokens,

58
00:03:27,990 --> 00:03:30,603
我们选择频率最高的一对，
we select the pair with the highest frequency,

59
00:03:32,190 --> 00:03:34,083
我们在我们的合并规则中注意到它，
we note it in our merge rules,

60
00:03:36,000 --> 00:03:39,360
我们将新的 token 添加到词汇表中
we add the new one token the vocabulary

61
00:03:39,360 --> 00:03:41,880
然后我们合并所有的 token 对
and then we merge all the pairs of tokens

62
00:03:41,880 --> 00:03:46,503
由标记 “le” 和 “a” 组成，进入我们的拆分。
composed of the token 'le' and 'a' into our splits.

63
00:03:50,323 --> 00:03:51,960
我们可以重复这个操作
And we can repeat this operation

64
00:03:51,960 --> 00:03:54,843
直到我们达到所需的词汇量。
until we reach the desired vocabulary size.

65
00:04:05,671 --> 00:04:10,671
在这里，当我们的词汇量达到 21 个 token 时，我们就停止了。
Here, we stopped when our vocabulary reached 21 tokens.

66
00:04:11,040 --> 00:04:13,920
我们现在可以看到，比起训练开始时 
We can see now that the words of our corpus

67
00:04:13,920 --> 00:04:17,040
我们的语料库中的单词
are now divided into far fewer tokens

68
00:04:17,040 --> 00:04:20,280
被分成了更少的 token
than at the beginning of the training.

69
00:04:20,280 --> 00:04:21,720
而我们的算法
And that our algorithm

70
00:04:21,720 --> 00:04:24,990
学会了部首 “hug” 和 “learn”
has learned the radicals 'hug' and 'learn'

71
00:04:24,990 --> 00:04:27,537
以及动词结尾 “ing”。
and also the verbal ending 'ing'.

72
00:04:29,880 --> 00:04:32,160
现在我们已经学会了我们的词汇
Now that we have learned our vocabulary

73
00:04:32,160 --> 00:04:35,943
和合并规则，我们可以标记新文本。
and merging rules, we can tokenize new texts.

74
00:04:37,980 --> 00:04:39,210
例如，
For example,

75
00:04:39,210 --> 00:04:41,160
如果我们想标记 “hugs” 这个词，
if we want to tokenize the word 'hugs',

76
00:04:42,960 --> 00:04:46,680
首先我们将它分成基本单元
first we'll divide it into elementary units

77
00:04:46,680 --> 00:04:48,843
所以它变成了一个字符序列。
so it became a sequence of characters.

78
00:04:50,040 --> 00:04:52,020
然后，我们将通过我们的合并规则
Then, we'll go through our merge rules

79
00:04:52,020 --> 00:04:54,690
直到我们有一个我们可以应用。
until we have one we can apply.

80
00:04:54,690 --> 00:04:57,930
在这里，我们可以合并字母 “h” 和 “u”。
Here, we can merge the letters 'h' and 'u'.

81
00:04:57,930 --> 00:05:01,467
在这里，我们可以合并 2 个 token 以获得新 token “hug”。
And here, we can merge 2 tokens to get the new token 'hug'.

82
00:05:02,400 --> 00:05:05,760
当我们到达合并规则的末尾时，
When we get to the end of our merge rules,

83
00:05:05,760 --> 00:05:07,563
分词化完成。
the tokenization is finished.

84
00:05:10,650 --> 00:05:11,727
就是这样。
And that's it.

85
00:05:12,846 --> 00:05:14,850
我希望现在的 BPE 算法
I hope that now the BPE algorithm

86
00:05:14,850 --> 00:05:16,413
对你而言不再是秘密！
has no more secret for you!

87
00:05:17,739 --> 00:05:20,406
（空气呼啸）
(air whooshing)