Step Guide for the Setup of the Single Zone Zokhozer with tiktoken of the NLP Advanced NLP apps Epython

In this lesson, we will learn how to build a custom Toxenizer using Tiktoken the library. This process includes loading model of the previously trained Tokenzer, describing both special bases and tokens, starting token with a particular typical statement of breaking down tokens, and checking a specific text. This setup is important for the NLP functions that require direct control of the text.

from pathlib import Path
import tiktoken
from tiktoken.load import load_tiktoken_bpe
import json

Here, we import several libraries key to the operation of the text and the learning of the machine. It uses the method from Pathlib to Easy File Path Management, while Tiktoken and Sad_tiktoken_BPE makes the upload and working with the Byte pair Pair Pair Pair oncodizer.

tokenizer_path = "./content/tokenizer.model"
num_reserved_special_tokens = 256


mergeable_ranks = load_tiktoken_bpe(tokenizer_path)


num_base_tokens = len(mergeable_ranks)
special_tokens = [
    "<|begin_of_text|>",
    "<|end_of_text|>",
    "<|reserved_special_token_0|>",
    "<|reserved_special_token_1|>",
    "<|finetune_right_pad_id|>",
    "<|step_id|>",
    "<|start_header_id|>",
    "<|end_header_id|>",
    "<|eom_id|>",
    "<|eot_id|>",
    "<|python_tag|>",
]

Here, we put the way to the Tokenzer model, explaining 256 special tokens stored. Then purchase regular levels, which forms the basic standards, which is the amount of basic tokens, and describes a list of special types of text and other databased purposes.

reserved_tokens = [
    f"<|reserved_special_token_{2 + i}|>"
    for i in range(num_reserved_special_tokens - len(special_tokens))
]
special_tokens = special_tokens + reserved_tokens


tokenizer = tiktoken.Encoding(
    name=Path(tokenizer_path).name,
    pat_str=r"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^rnp{L}p{N}]?p{L}+|p{N}{1,3}| ?[^sp{L}p{N}]+[rn]*|s*[rn]+|s+(?!S)|s+",
    mergeable_ranks=mergeable_ranks,
    special_tokens={token: len(mergeable_ranks) + i for i, token in enumerate(special_tokens)},
)

Now, we are actively developing the installed tokens stored to up to 256, and then informs the list of previously defined tokens. Starts Tokenzer using Tiktoken. Including a code with the ordinary specification of the text, integrated levels are used as a basis figure, and special map tokens in different Token IDs.

#-------------------------------------------------------------------------
# Test the tokenizer with a sample text
#-------------------------------------------------------------------------
sample_text = "Hello, this is a test of the updated tokenizer!"
encoded = tokenizer.encode(sample_text)
decoded = tokenizer.decode(encoded)


print("Sample Text:", sample_text)
print("Encoded Tokens:", encoded)
print("Decoded Text:", decoded)

We examine Tokenzer by entering the Token ID code and converts those IDs to the text. Print first text, listed tokens, and a fixed text to ensure that the token is effective.

Here, including the thread “hey” in its corresponding Token IDs using a method to enter the Tokenzer code.

In conclusion, following this lesson will teach you how to set the BPE Bpenizer Tiktoken Toktoken library. You have seen how you can download the model of previously trained, explain both basic tokens and tokens, and start token with a specific Token talk. Finally, you have confirmed Tokelzer's performance by entering code and decorations of the sample. This setup is a basic step for any NLP project that requires customized custom and performance processing.

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