langchain/docs/modules/indexes/text_splitters/examples/huggingface_length_function.ipynb

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    "# Hugging Face tokenizer\n",
    "\n",
    ">[Hugging Face](https://huggingface.co/docs/tokenizers/index) has many tokenizers.\n",
    "\n",
    "We use Hugging Face tokenizer, the [GPT2TokenizerFast](https://huggingface.co/Ransaka/gpt2-tokenizer-fast) to count the text length in tokens.\n",
    "\n",
    "1. How the text is split: by character passed in\n",
    "2. How the chunk size is measured: by number of tokens calculated by the `Hugging Face` tokenizer\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a8ce51d5",
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   "source": [
    "from transformers import GPT2TokenizerFast\n",
    "\n",
    "tokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# This is a long document we can split up.\n",
    "with open('../../../state_of_the_union.txt') as f:\n",
    "    state_of_the_union = f.read()\n",
    "from langchain.text_splitter import CharacterTextSplitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca5e72c0",
   "metadata": {},
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   "source": [
    "text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(tokenizer, chunk_size=100, chunk_overlap=0)\n",
    "texts = text_splitter.split_text(state_of_the_union)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "37cdfbeb",
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     "text": [
      "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.  \n",
      "\n",
      "Last year COVID-19 kept us apart. This year we are finally together again. \n",
      "\n",
      "Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n",
      "\n",
      "With a duty to one another to the American people to the Constitution.\n"
     ]
    }
   ],
   "source": [
    "print(texts[0])"
   ]
  }
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