Initial Query

How do I get all tokens in a AutoTokenizer?

LLM Generation

Query

How do I get all tokens in a AutoTokenizer?

Output

# Getting All Tokens in an AutoTokenizer To retrieve all tokens from a Hugging Face `AutoTokenizer`, you can use either the `.vocab` attribute or the `.get_vocab()` method: 

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from transformers import AutoTokenizer

# Load a tokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

# Method 1: Using get_vocab() (recommended)
vocab_dict = tokenizer.get_vocab()
# This returns a dictionary mapping tokens to their IDs

# Method 2: Accessing vocab attribute (not available in all tokenizers)
if hasattr(tokenizer, 'vocab'):
    vocab_dict = tokenizer.vocab

# If you only want the tokens as a list
all_tokens = list(vocab_dict.keys())

# If you want to check the vocabulary size
vocab_size = len(vocab_dict)
print(f"Vocabulary size: {vocab_size}")

# To see a sample of tokens (avoid printing the entire vocabulary)
print(list(vocab_dict.keys())[:10])
Note: - The `.get_vocab()` method is more universally supported across different tokenizer types - Vocabularies can be very large (tens of thousands of tokens), so be careful when printing - Some special tokens might be prefixed with special characters (like `##` in BERT) Would you like me to explain more about working with these tokens?

Substring Evaluation

Testing if the previous output contains the string get_vocab: True

Initial Query

How do I get all tokens in a AutoTokenizer?

LLM Generation

Query

How do I get all tokens in a AutoTokenizer?

Output

# Getting All Tokens from an AutoTokenizer To retrieve all tokens from a Hugging Face `AutoTokenizer`, you can use the `get_vocab()` method. Here's how to do it: 

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from transformers import AutoTokenizer

# Initialize a tokenizer with a pretrained model
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")  # or any other model

# Get the complete vocabulary dictionary (token → id)
vocab = tokenizer.get_vocab()

# If you want just the tokens as a list
all_tokens = list(vocab.keys())

# Print vocabulary size
print(f"Vocabulary size: {len(vocab)}")

# Example: Print the first 10 tokens
print("\nSample tokens:")
for token, idx in list(vocab.items())[:10]:
    print(f"{token}: {idx}")
## Additional Information 1. The vocabulary can be large (typically 30,000+ tokens for most models) 2. To see special tokens specifically: 
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   print(tokenizer.special_tokens_map)
3. If you need to check if a specific token exists: 
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   token_id = vocab.get("hello", None)
   if token_id is not None:
       print(f"Token 'hello' exists with ID {token_id}")
   else:
       print("Token 'hello' is not in the vocabulary")
The `get_vocab()` method works across different tokenizer types (WordPiece, BPE, SentencePiece, etc.).

Substring Evaluation

Testing if the previous output contains the string get_vocab: True