# How can I write my own emoji tokenizer for Flan-T5 XXL?

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

**Published by:** [The BlogChain Newsletter](https://paragraph.com/@kazani/)
**Published on:** 2023-05-15
**Categories:** emoji, tokenizer, huggingface, flan-t5 xxl
**URL:** https://paragraph.com/@kazani/how-can-i-write-my-own-emoji-tokenizer-for-flan-t5-xxl

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Good Morning! Delivering interesting content every single week on Web3, Security, Crypto, NFTs, Design & AI. It's FREE, Takes less than 5-minutes to read, and you are guaranteed to learn something. Subscribe to get valuable Web3 News, Useful Resources and Insights every week to your Inbox!Subscribe 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:pip install tokenizersStep 2: Import the necessary modules In your Python script, import the required modules:from tokenizers import Tokenizer, pre_tokenizers, decoders, trainersStep 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 passIn 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. If you're enjoying today's newsletter, why not share it with your friends? They might find it just as informative and entertaining as you do. Sharing is caring, and by spreading the word about this newsletter, you're helping to support ME and ensure that more great content gets produced in the future. Plus, you'll get to have even more conversations with your friends about the interesting topics covered in each edition. So go ahead and hit that share button. ShareCollect this post. 100 copies available. 1 MATIC only.Collect I hope this was helpful! Thank you for reading!If you're interested in following along, feel free to subscribe!Subscribe Let’s bust some more in next article.If you want more, be sure to FOLLOW ME

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