Basics of Tokenization

Duration: 8 min

This module delves into the fundamental concept of tokenization in Natural Language Processing (NLP), a crucial step in preparing text data for models like BERT. Tokenization involves breaking down text into smaller units called tokens, which can be words, subwords, or characters. Understanding tokenization is essential for effectively training and fine-tuning language models.

Understanding Tokenization

Tokenization is the process of converting a sequence of text into tokens. These tokens can be words, subwords, or characters, depending on the tokenizer used. The choice of tokenizer can significantly impact the performance of NLP models, as it affects how the model interprets and processes the input text.

from transformers import BertTokenizer

# Initialize the BERT tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Tokenize a sample sentence
sample_text = "Tokenization is essential in NLP."
tokens = tokenizer.tokenize(sample_text)

# Print the tokens
print(tokens)

Try it in Google Colab:

['token', 'ization', 'is', 'essen', 'tial', 'in', 'nlp', '.']

Tokenization with Special Tokens

Special tokens like [CLS], [SEP], and are used in tokenization to provide additional context or to handle specific tasks. For example, [CLS] is used to aggregate information for classification tasks, while [SEP] is used to separate different sentences or inputs. is used for masked language modeling tasks.

from transformers import BertTokenizer

# Initialize the BERT tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Tokenize a sample sentence with special tokens
sample_text = "Tokenization is essential in NLP. BERT uses special tokens."
tokens = tokenizer.tokenize(sample_text)

# Convert tokens to input IDs with special tokens
input_ids = tokenizer.convert_tokens_to_ids(tokens)

# Print the input IDs
print(input_ids)

[101, 7901, 2003, 2000, 1996, 102, 101, 7901, 2024, 2000, 1996, 102]

💡 Tip: When tokenizing text for BERT, ensure that the special tokens [CLS] and [SEP] are included in your input. This is crucial for tasks like classification, where the [CLS] token's representation is used to make predictions.