如何按标记拆分文本
语言模型有一个token限制。你不应该超过这个token限制。当你将文本分割成块时，因此计算token的数量是一个好主意。有许多tokenizer。当你在文本中计算token时，你应该使用与语言模型中相同的tokenizer。
tiktoken
tiktoken 是一个由 OpenAI 创建的快速 BPE 分词器。
我们可以使用tiktoken来估计使用的令牌数。对于OpenAI模型来说，这可能会更准确。
- 文本如何分割：通过传入的字符进行分割。
- 块大小的测量方式：通过
tiktoken分词器。
CharacterTextSplitter, RecursiveCharacterTextSplitter, 和 TokenTextSplitter 可以直接与 tiktoken 一起使用。
%pip install --upgrade --quiet langchain-text-splitters tiktoken
from langchain_text_splitters import CharacterTextSplitter
# This is a long document we can split up.
with open("state_of_the_union.txt") as f:
state_of_the_union = f.read()
要使用CharacterTextSplitter进行分割，然后使用tiktoken合并块，请使用其.from_tiktoken_encoder()方法。请注意，此方法的分割可能会大于由tiktoken分词器测量的块大小。
.from_tiktoken_encoder() 方法接受 encoding_name 作为参数（例如 cl100k_base），或者 model_name（例如 gpt-4）。所有额外的参数如 chunk_size、chunk_overlap 和 separators 都用于实例化 CharacterTextSplitter：
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
encoding_name="cl100k_base", chunk_size=100, chunk_overlap=0
)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.
Last year COVID-19 kept us apart. This year we are finally together again.
Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans.
With a duty to one another to the American people to the Constitution.
为了实现块大小的硬约束，我们可以使用RecursiveCharacterTextSplitter.from_tiktoken_encoder，其中每个分割如果大小较大，将被递归分割：
from langchain_text_splitters import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
model_name="gpt-4",
chunk_size=100,
chunk_overlap=0,
)
我们还可以加载一个TokenTextSplitter分割器，它直接与tiktoken配合使用，并确保每个分割都小于块大小。
from langchain_text_splitters import TokenTextSplitter
text_splitter = TokenTextSplitter(chunk_size=10, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
Madam Speaker, Madam Vice President, our
一些书面语言（例如中文和日文）的字符会编码为2个或更多的标记。直接使用TokenTextSplitter可能会导致一个字符的标记被分割在两个块之间，从而导致Unicode字符格式错误。使用RecursiveCharacterTextSplitter.from_tiktoken_encoder或CharacterTextSplitter.from_tiktoken_encoder可以确保块包含有效的Unicode字符串。
spaCy
spaCy 是一个用于高级自然语言处理的开源软件库，使用编程语言 Python 和 Cython 编写。
LangChain 实现了基于 spaCy tokenizer 的分割器。
- 文本如何分割：通过
spaCy分词器。 - 块大小的测量方式：按字符数。
%pip install --upgrade --quiet spacy
# This is a long document we can split up.
with open("state_of_the_union.txt") as f:
state_of_the_union = f.read()
from langchain_text_splitters import SpacyTextSplitter
text_splitter = SpacyTextSplitter(chunk_size=1000)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
Madam Speaker, Madam Vice President, our First Lady and Second Gentleman.
Members of Congress and the Cabinet.
Justices of the Supreme Court.
My fellow Americans.
Last year COVID-19 kept us apart.
This year we are finally together again.
Tonight, we meet as Democrats Republicans and Independents.
But most importantly as Americans.
With a duty to one another to the American people to the Constitution.
And with an unwavering resolve that freedom will always triumph over tyranny.
Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways.
But he badly miscalculated.
He thought he could roll into Ukraine and the world would roll over.
Instead he met a wall of strength he never imagined.
He met the Ukrainian people.
From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.
SentenceTransformers
SentenceTransformersTokenTextSplitter 是一个专门用于句子转换器模型的文本分割器。默认行为是将文本分割成适合您想要使用的句子转换器模型的令牌窗口的块。
要根据sentence-transformers分词器拆分文本并限制令牌计数，请实例化一个SentenceTransformersTokenTextSplitter。您可以选择性地指定：
chunk_overlap: 整数类型的令牌重叠计数;model_name: 句子转换模型名称，默认为"sentence-transformers/all-mpnet-base-v2";tokens_per_chunk: 每个块所需的标记数量。
from langchain_text_splitters import SentenceTransformersTokenTextSplitter
splitter = SentenceTransformersTokenTextSplitter(chunk_overlap=0)
text = "Lorem "
count_start_and_stop_tokens = 2
text_token_count = splitter.count_tokens(text=text) - count_start_and_stop_tokens
print(text_token_count)
2
token_multiplier = splitter.maximum_tokens_per_chunk // text_token_count + 1
# `text_to_split` does not fit in a single chunk
text_to_split = text * token_multiplier
print(f"tokens in text to split: {splitter.count_tokens(text=text_to_split)}")
tokens in text to split: 514
text_chunks = splitter.split_text(text=text_to_split)
print(text_chunks[1])
lorem
NLTK
我们不仅仅可以使用"\n\n"来分割，还可以使用NLTK基于NLTK tokenizers进行分割。
- 文本如何分割：通过
NLTK分词器。 - 块大小的测量方式：按字符数。
# pip install nltk
# This is a long document we can split up.
with open("state_of_the_union.txt") as f:
state_of_the_union = f.read()
from langchain_text_splitters import NLTKTextSplitter
text_splitter = NLTKTextSplitter(chunk_size=1000)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
Madam Speaker, Madam Vice President, our First Lady and Second Gentleman.
Members of Congress and the Cabinet.
Justices of the Supreme Court.
My fellow Americans.
Last year COVID-19 kept us apart.
This year we are finally together again.
Tonight, we meet as Democrats Republicans and Independents.
But most importantly as Americans.
With a duty to one another to the American people to the Constitution.
And with an unwavering resolve that freedom will always triumph over tyranny.
Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways.
But he badly miscalculated.
He thought he could roll into Ukraine and the world would roll over.
Instead he met a wall of strength he never imagined.
He met the Ukrainian people.
From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.
Groups of citizens blocking tanks with their bodies.
KoNLPY
KoNLPy: Korean NLP in Python 是一个用于韩语自然语言处理（NLP）的Python包。
令牌分割涉及将文本分割成更小、更易管理的单位，称为令牌。这些令牌通常是单词、短语、符号或其他对进一步处理和分析至关重要的有意义元素。在英语等语言中，令牌分割通常涉及通过空格和标点符号分隔单词。令牌分割的有效性在很大程度上取决于分词器对语言结构的理解，确保生成有意义的令牌。由于为英语设计的分词器无法理解其他语言（如韩语）的独特语义结构，因此它们不能有效地用于韩语处理。
使用KoNLPy的Kkma分析器进行韩语分词
在韩语文本的情况下，KoNLPY 包含一个名为 Kkma（韩语知识形态分析器）的形态分析器。Kkma 提供了对韩语文本的详细形态分析。它将句子分解为单词，并将单词分解为各自的词素，识别每个标记的词性。它可以将一段文本分割成单独的句子，这对于处理长文本特别有用。
使用注意事项
虽然Kkma以其详细分析而闻名，但需要注意的是，这种精确性可能会影响处理速度。因此，Kkma最适合那些优先考虑分析深度而非快速文本处理的应用。
# pip install konlpy
# This is a long Korean document that we want to split up into its component sentences.
with open("./your_korean_doc.txt") as f:
korean_document = f.read()
from langchain_text_splitters import KonlpyTextSplitter
text_splitter = KonlpyTextSplitter()
texts = text_splitter.split_text(korean_document)
# The sentences are split with "\n\n" characters.
print(texts[0])
춘향전 옛날에 남원에 이 도령이라는 벼슬아치 아들이 있었다.
그의 외모는 빛나는 달처럼 잘생겼고, 그의 학식과 기예는 남보다 뛰어났다.
한편, 이 마을에는 춘향이라는 절세 가인이 살고 있었다.
춘 향의 아름다움은 꽃과 같아 마을 사람들 로부터 많은 사랑을 받았다.
어느 봄날, 도령은 친구들과 놀러 나갔다가 춘 향을 만 나 첫 눈에 반하고 말았다.
두 사람은 서로 사랑하게 되었고, 이내 비밀스러운 사랑의 맹세를 나누었다.
하지만 좋은 날들은 오래가지 않았다.
도령의 아버지가 다른 곳으로 전근을 가게 되어 도령도 떠나 야만 했다.
이별의 아픔 속에서도, 두 사람은 재회를 기약하며 서로를 믿고 기다리기로 했다.
그러나 새로 부임한 관아의 사또가 춘 향의 아름다움에 욕심을 내 어 그녀에게 강요를 시작했다.
춘 향 은 도령에 대한 자신의 사랑을 지키기 위해, 사또의 요구를 단호히 거절했다.
이에 분노한 사또는 춘 향을 감옥에 가두고 혹독한 형벌을 내렸다.
이야기는 이 도령이 고위 관직에 오른 후, 춘 향을 구해 내는 것으로 끝난다.
두 사람은 오랜 시련 끝에 다시 만나게 되고, 그들의 사랑은 온 세상에 전해 지며 후세에까지 이어진다.
- 춘향전 (The Tale of Chunhyang)
Hugging Face 分词器
Hugging Face 有许多分词器。
我们使用Hugging Face的分词器，即GPT2TokenizerFast来计算文本的令牌长度。
- 文本如何分割：通过传入的字符进行分割。
- 块大小的测量方式：通过
Hugging Face分词器计算的标记数量。
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
# This is a long document we can split up.
with open("state_of_the_union.txt") as f:
state_of_the_union = f.read()
from langchain_text_splitters import CharacterTextSplitter
text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(
tokenizer, chunk_size=100, chunk_overlap=0
)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.
Last year COVID-19 kept us apart. This year we are finally together again.
Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans.
With a duty to one another to the American people to the Constitution.