Initial Query

How do I get all tokens in a AutoTokenizer?

LLM Generation

Query

How do I get all tokens in a AutoTokenizer?

Output

There are a few ways to get all tokens in an AutoTokenizer: 1. Using the `get_vocab()` method: 

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from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
vocab = tokenizer.get_vocab()  # Returns dict of {token: token_id}

# Get all tokens
all_tokens = list(vocab.keys())
2. Using the `vocab` property (if available): 
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tokens = list(tokenizer.vocab.keys())
3. Converting token IDs to tokens: 
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# Get tokens by converting all possible token IDs
vocab_size = tokenizer.vocab_size
all_tokens = [tokenizer.convert_ids_to_tokens(i) for i in range(vocab_size)]
4. Using `tokenizer.all_special_tokens` to get just the special tokens: 
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special_tokens = tokenizer.all_special_tokens
Example with complete code: 
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from transformers import AutoTokenizer

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

# Get vocabulary
vocab = tokenizer.get_vocab()

# Get all tokens
all_tokens = list(vocab.keys())

# Print first 10 tokens
print("First 10 tokens:", all_tokens[:10])

# Print vocabulary size
print("Vocabulary size:", len(all_tokens))

# Get special tokens
special_tokens = tokenizer.all_special_tokens
print("Special tokens:", special_tokens)
Note that different tokenizer implementations might have slightly different methods available, but `get_vocab()` is generally the most reliable way to get all 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

There are a few ways to get all tokens in an AutoTokenizer. Here are the main methods: 1. Using get_vocab(): 

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from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
vocab = tokenizer.get_vocab()  # Returns dictionary of {token: token_id}

# Get all tokens
all_tokens = list(vocab.keys())
2. Using vocab_dict or vocab property (not available for all tokenizers): 
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# For some tokenizers
all_tokens = list(tokenizer.vocab.keys())
3. Using the conversion from ids: 
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# Get tokens by converting all possible ids
vocab_size = tokenizer.vocab_size
all_tokens = [tokenizer.convert_ids_to_tokens(i) for i in range(vocab_size)]
4. For special tokens specifically: 
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# Get special tokens
special_tokens = tokenizer.all_special_tokens
Example of full usage: 
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from transformers import AutoTokenizer

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

# Get complete vocabulary
vocab = tokenizer.get_vocab()
all_tokens = list(vocab.keys())

# Print some information
print(f"Vocabulary size: {len(vocab)}")
print(f"First 10 tokens: {all_tokens[:10]}")
print(f"Special tokens: {tokenizer.all_special_tokens}")
Note: - Different tokenizer types might have slightly different methods to access their vocabulary - The get_vocab() method is the most reliable way across different tokenizer types - The vocabulary includes regular tokens and special tokens - Some tokens might appear as weird characters or subwords, which is normal for subword tokenizers

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

There are a few ways to get all tokens in an AutoTokenizer. Here are the main methods: 1. Using get_vocab(): 

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from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
vocab = tokenizer.get_vocab()  # Returns dictionary of token -> token_id
all_tokens = list(vocab.keys())
2. Using vocab_size and convert_ids_to_tokens(): 
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tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
all_tokens = [tokenizer.convert_ids_to_tokens(i) for i in range(tokenizer.vocab_size)]
3. Using all_special_tokens and vocab: 
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tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
# Get both regular tokens and special tokens
all_tokens = list(tokenizer.get_vocab().keys())
special_tokens = tokenizer.all_special_tokens
all_tokens_with_special = all_tokens + special_tokens
Note that the exact tokens will depend on the specific model/tokenizer you're using. Different models use different vocabularies and tokenization schemes. Example with counts: 
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tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
vocab = tokenizer.get_vocab()

print(f"Vocabulary size: {len(vocab)}")
print(f"Number of special tokens: {len(tokenizer.all_special_tokens)}")
print("\nFirst 10 tokens:")
for token in list(vocab.keys())[:10]:
    print(f"{token}: {vocab[token]}")

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

There are a few ways to get all tokens in an AutoTokenizer. Here are the main methods: 1. Using get_vocab(): 

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from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
vocab = tokenizer.get_vocab()  # Returns dictionary of token: id pairs
all_tokens = list(vocab.keys())
2. Using vocab_size and convert_ids_to_tokens(): 
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tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
all_tokens = [tokenizer.convert_ids_to_tokens(i) for i in range(tokenizer.vocab_size)]
3. If the tokenizer has a direct vocab attribute: 
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tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
if hasattr(tokenizer, 'vocab'):
    all_tokens = list(tokenizer.vocab.keys())
Note that different tokenizer implementations might have slightly different ways to access their vocabulary. The get_vocab() method is generally the most reliable approach as it's standardized across different tokenizer types. Example with complete code: 
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from transformers import AutoTokenizer

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

# Get vocabulary
vocab = tokenizer.get_vocab()
all_tokens = list(vocab.keys())

# Print first 10 tokens
print("First 10 tokens:", all_tokens[:10])
print("Vocabulary size:", len(all_tokens))
This will give you access to all tokens in the tokenizer's vocabulary.

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

There are a few ways to get all tokens in an AutoTokenizer. Here are the main methods: 1. Using get_vocab(): 

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from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
vocab = tokenizer.get_vocab()  # Returns dictionary of token -> token_id
tokens = list(vocab.keys())  # Get just the tokens
2. Using vocabulary property (if available): 
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tokens = list(tokenizer.vocab.keys())
3. Using all_special_tokens and convert_ids_to_tokens(): 
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# Get all possible token IDs
all_token_ids = range(tokenizer.vocab_size)

# Convert IDs to tokens
all_tokens = [tokenizer.convert_ids_to_tokens(id) for id in all_token_ids]
4. For specific special tokens: 
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# Get just special tokens
special_tokens = tokenizer.all_special_tokens

# Get special token IDs
special_token_ids = tokenizer.all_special_ids
The first method using get_vocab() is generally the most reliable and recommended approach since it returns the complete vocabulary as a dictionary mapping tokens to their IDs. Example with additional details: 
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from transformers import AutoTokenizer

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

# Get complete vocabulary
vocab = tokenizer.get_vocab()

# Print some statistics
print(f"Vocabulary size: {len(vocab)}")
print("\nFirst 10 tokens:")
for token, id in list(vocab.items())[:10]:
    print(f"Token: {token}, ID: {id}")

# Get special tokens
print("\nSpecial tokens:")
for token in tokenizer.all_special_tokens:
    print(token)

Substring Evaluation

Testing if the previous output contains the string get_vocab: True