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To write your own emoji tokenizer for Flan-T5 XXL, we can utilize the Hugging Face library, specifically the tokenizers module, which provides an easy way to create custom tokenizers.

Here's a step-by-step guide on how to write your own emoji tokenizer:

Step 1: Install the required libraries

You'll need to install the tokenizers library from Hugging Face. You can do this by running the following command:

Step 2: Import the necessary modules

In your Python script, import the required modules:

from tokenizers import Tokenizer, pre_tokenizers, decoders, trainers

Step 3: Create a new Tokenizer

Instantiate a new Tokenizer object:

tokenizer = Tokenizer(models.BPE())

Step 4: Define your Emoji PreTokenizer

The PreTokenizer is responsible for splitting the input text into smaller units. In our case, we want to split emojis as separate tokens. Here's an example implementation:

class EmojiPreTokenizer(pre_tokenizers.PreTokenizer):
    def tokenize(self, text):
        tokens = []
        current_token = ""

        for char in text:
            if is_emoji(char):  # Implement your own logic to detect emojis
                if current_token:
                    tokens.append(current_token)
                    current_token = ""
                tokens.append(char)
            else:
                current_token += char

        if current_token:
            tokens.append(current_token)

        return tokens

def is_emoji(char):
    # Implement your own logic to detect emojis
    pass

In the EmojiPreTokenizer class, you'll need to define the logic to detect emojis. You can use regex patterns or other techniques based on your requirements.

Step 5: Configure the Tokenizer

Configure the Tokenizer to use your custom Emoji PreTokenizer:

tokenizer.pre_tokenizer = EmojiPreTokenizer()

Step 6: Train the Tokenizer (optional)

If you have a training corpus specifically for the emojis you want to tokenize, you can train the tokenizer using the trainers module. Otherwise, you can skip this step and directly save and load the tokenizer.

Step 7: Save and Load the Tokenizer

Save the tokenizer to a file for later use:

tokenizer.save("emoji_tokenizer.json")

To load the tokenizer from a file:

tokenizer = Tokenizer.from_file("emoji_tokenizer.json")

That's it! You now have your own emoji tokenizer based on Flan-T5 XXL.

Remember to implement the is_emoji function inside the EmojiPreTokenizer class to detect emojis accurately.

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