Tokenizers** are a crucial part of Natural Language Processing (NLP) pipelines. They convert text into numerical data, which models can process. Here’s a simplified explanation with code examples:

>> 1. Word-based Tokenization
   - Concept: Splits text into words, assigning each word a unique ID. 
   - Example:
       ```python code```

       '''
       tokenized_text = "Jim Henson was a puppeteer".split()
       print(tokenized_text)
       '''

       # Output: ['Jim', 'Henson', 'was', 'a', 'puppeteer']

   - Pros: Simple and often effective.
   - Cons: Creates large vocabularies and doesn't recognize word similarities (e.g., "dog" vs. "dogs").

>> 2. Character-based Tokenization
   - Concept: Splits text into individual characters.
   - Pros: Smaller vocabulary size and fewer unknown tokens.
  - Cons: Less meaningful representation as each character alone doesn't convey much information.

>> 3. Subword Tokenization
   - Concept: Combines the benefits of word and character tokenization by splitting rare words into subwords while keeping common words intact.
   - Example: 
       - "Annoyingly" might be split into "annoying" and "ly".
       - "Tokenization" could be split into "token" and "ization".
   - Pros: Efficient with a small vocabulary and meaningful representation.

>> 4. Loading and Saving Tokenizers
   - You can easily load and save tokenizers with pre-trained models.

   - Loading a BERT Tokenizer:
       ```python code```

       '''
       from transformers import BertTokenizer

       tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
       '''


   - Using AutoTokenizer:
       ```python code```

       '''
       from transformers import AutoTokenizer

       tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
       tokenizer_output = tokenizer("Using a Transformer network is simple")
       print(tokenizer_output)
       '''

       '''
       # Output:
       # {'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],
       #  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],
       #  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}
       '''


   - Saving a Tokenizer:
       ```python code```
       
       '''
       tokenizer.save_pretrained("directory_on_my_computer")
       '''

Conclusion
Tokenizers transform text into a format that NLP models can understand, making them a vital step in preparing data for text analysis and processing.