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_splitter import NLTKTextSplittertext_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‚Äôs 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.Hugging Face tokenizer‚ÄãHugging Face has many tokenizers.We use Hugging Face tokenizer, the GPT2TokenizerFast to count the text length in tokens.How the text is split: by character passed in.How the chunk size is measured: by number of tokens calculated by the Hugging Face tokenizer.from transformers import GPT2TokenizerFasttokenizer = 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_splitter import CharacterTextSplittertext_splitter =
Language models have a token limit. You should not exceed the token limit. When you split your text into chunks it is therefore a good idea to count the number of tokens. There are many tokenizers. When you count tokens in your text you should use the same tokenizer as used in the language model.
Language models have a token limit. You should not exceed the token limit. When you split your text into chunks it is therefore a good idea to count the number of tokens. There are many tokenizers. When you count tokens in your text you should use the same tokenizer as used in the language model. ->: 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_splitter import NLTKTextSplittertext_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‚Äôs 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.Hugging Face tokenizer‚ÄãHugging Face has many tokenizers.We use Hugging Face tokenizer, the GPT2TokenizerFast to count the text length in tokens.How the text is split: by character passed in.How the chunk size is measured: by number of tokens calculated by the Hugging Face tokenizer.from transformers import GPT2TokenizerFasttokenizer = 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_splitter import CharacterTextSplittertext_splitter =