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💡 Pro tip: This is one of those techniques that will make you look like a data science wizard! Introduction to Tokenization - Made Simple!

Tokenization is the process of breaking down text into smaller units called tokens. It is a crucial step in natural language processing (NLP) tasks, as it prepares the input text for further processing. Tokens can be words, subwords, or even characters, depending on the tokenization strategy.

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🎉 You’re doing great! This concept might seem tricky at first, but you’ve got this! Why Use a Custom Tokenizer? - Made Simple!

Pre-trained tokenizers are often designed for general-purpose tasks and may not work optimally for specific domains or languages. By training a custom tokenizer, you can tailor it to your specific task and data, potentially improving the performance of your NLP model.

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✨ Cool fact: Many professional data scientists use this exact approach in their daily work! Setting up the Environment - Made Simple!

Code:

Let’s make this super clear! Here’s how we can tackle this:

!pip install transformers
from transformers import AutoTokenizer

First, we need to install the Hugging Face Transformers library and import the necessary modules. The AutoTokenizer class allows us to load and use pre-trained tokenizers easily.

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🔥 Level up: Once you master this, you’ll be solving problems like a pro! Loading a Pre-trained Tokenizer - Made Simple!

Code:

Let’s make this super clear! Here’s how we can tackle this:

tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')

To train a custom tokenizer, we start with a pre-trained tokenizer as a base. Here, we load the bert-base-uncased tokenizer, which is a popular choice for English text.

🚀 Preparing the Training Data - Made Simple!

Code:

Let’s break this down together! Here’s how we can tackle this:

with open('training_data.txt', 'r') as f:
    training_data = f.read().split('\n')

Prepare your training data by loading it from a text file. The training data should consist of text samples relevant to your domain or task.

🚀 Training the Tokenizer - Made Simple!

Code:

Let’s break this down together! Here’s how we can tackle this:

from tokenizers import Tokenizer
from tokenizers.models import BPE

tokenizer = Tokenizer(BPE())
tokenizer.train_from_iterator(training_data, vocab_size=52000)

We create a new tokenizer instance using the Byte-Pair Encoding (BPE) model and train it on the prepared training data. The vocab_size parameter specifies the desired size of the tokenizer’s vocabulary.

🚀 Saving the Custom Tokenizer - Made Simple!

Code:

Let me walk you through this step by step! Here’s how we can tackle this:

tokenizer.save('custom_tokenizer.json')

After training, we can save the custom tokenizer to a file for future use.

🚀 Loading the Custom Tokenizer - Made Simple!

Code:

Let’s break this down together! Here’s how we can tackle this:

from tokenizers import Tokenizer

tokenizer = Tokenizer.from_file('custom_tokenizer.json')

To use the custom tokenizer, we can load it from the saved file.

🚀 Tokenizing Text - Made Simple!

Code:

Don’t worry, this is easier than it looks! Here’s how we can tackle this:

text = "This is a sample text."
encoded = tokenizer.encode(text)
print(encoded.tokens)

Now, we can use the custom tokenizer to tokenize text by calling the encode method. This will return an Encoding object containing the tokenized text.

🚀 Decoding Tokens - Made Simple!

Code:

Don’t worry, this is easier than it looks! Here’s how we can tackle this:

decoded = tokenizer.decode(encoded.ids)
print(decoded)

The decode method of the tokenizer allows us to convert the token IDs back into their corresponding text representation.

🚀 Using the Custom Tokenizer with Transformers - Made Simple!

Code:

Here’s a handy trick you’ll love! Here’s how we can tackle this:

from transformers import AutoModel

model = AutoModel.from_pretrained('bert-base-uncased')
model.resize_token_embeddings(len(tokenizer))

To use the custom tokenizer with a pre-trained Transformer model, we need to resize the token embeddings of the model to match the size of our custom tokenizer’s vocabulary.

🚀 Putting It All Together - Made Simple!

Code:

Don’t worry, this is easier than it looks! Here’s how we can tackle this:

# Load the custom tokenizer
tokenizer = AutoTokenizer.from_pretrained('custom_tokenizer')

# Load the pre-trained model and resize the token embeddings
model = AutoModel.from_pretrained('bert-base-uncased')
model.resize_token_embeddings(len(tokenizer))

# Tokenize and encode text
text = "This is a custom tokenized text."
encoded = tokenizer.encode(text)

# Pass the encoded text to the model
output = model(**encoded)

This slide shows you the complete workflow of loading the custom tokenizer, resizing the token embeddings of the pre-trained model, tokenizing and encoding text using the custom tokenizer, and passing the encoded text to the model for further processing.

🚀 Additional Resources - Made Simple!

For more cool topics and resources related to custom tokenization with Hugging Face Transformers, you can refer to the following sources:

• Hugging Face Tokenizers Library: https://github.com/huggingface/tokenizers
• Hugging Face Course on Custom Tokenization: https://huggingface.co/course/chapter7/1
• ArXiv paper on Subword Regularization: https://arxiv.org/abs/1804.07009

🎊 Awesome Work!

You’ve just learned some really powerful techniques! Don’t worry if everything doesn’t click immediately - that’s totally normal. The best way to master these concepts is to practice with your own data.

What’s next? Try implementing these examples with your own datasets. Start small, experiment, and most importantly, have fun with it! Remember, every data science expert started exactly where you are right now.

Keep coding, keep learning, and keep being awesome! 🚀