>> Decoding
   Decoding is the process of converting these numbers back into text. Here’s how you can do that:

python code

'''
# Decode the input IDs back into a string
decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])

# Print the decoded string
print(decoded_string)
'''

When you run this code, you'll see the original sentence:

python code

'''
'Using a Transformer network is simple'
'''

The decode method not only converts the numbers back into tokens but also combines tokens that were part of the same word, restoring the original text.

Summary
  - Encoding: Turning text into numbers.

    - Tokenization: Splitting text into smaller pieces (tokens).

    - Converting Tokens to Numbers: Turning tokens into numbers based on a model's vocabulary.

  - Decoding: Turning numbers back into text.

Including the code:

python code

'''
from transformers import AutoTokenizer

# Tokenization
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
sequence = "Using a Transformer network is simple"
tokens = tokenizer.tokenize(sequence)
print(tokens)  # Output: ['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']

# Converting Tokens to Input IDs
ids = tokenizer.convert_tokens_to_ids(tokens)
print(ids)  # Output: [7993, 170, 11303, 1200, 2443, 1110, 3014]

# Decoding
decoded_string = tokenizer.decode(ids)
print(decoded_string)  # Output: 'Using a Transformer network is simple'
'''

This gives you a full overview of how text is processed into numbers and back into text using a tokenizer.