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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:
pip install tokenizers
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
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:
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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