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 There are several ways to get all tokens in an AutoTokenizer. Here are three methods: ### 1. Using the `tokenizer.vocab` attribute The `tokenizer.vocab` attribute is a dictionary that maps each token to its corresponding integer ID. You can use the `keys()` method of this dictionary to get a list of all tokens. 

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from transformers import AutoTokenizer

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

all_tokens = list(tokenizer.vocab.keys())

print(all_tokens)
### 2. Using the `tokenizer.get_vocab()` method The `tokenizer.get_vocab()` method returns a list of all tokens in the tokenizer's vocabulary. 
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from transformers import AutoTokenizer

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

all_tokens = tokenizer.get_vocab()

print(all_tokens)
### 3. Using the `tokenizer.convert_ids_to_tokens()` method The `tokenizer.convert_ids_to_tokens()` method can be used to convert a list of token IDs to a list of tokens. You can pass a list of all token IDs (which you can get using the `tokenizer.vocab.values()` method) to this method to get a list of all tokens. 
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from transformers import AutoTokenizer

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

all_token_ids = list(tokenizer.vocab.values())

all_tokens = tokenizer.convert_ids_to_tokens(all_token_ids)

print(all_tokens)
All three methods will give you the same result: a list of all tokens in the AutoTokenizer's vocabulary. ## Additional notes * The `tokenizer.vocab` attribute also contains information about the special tokens used by the tokenizer, such as the `[CLS]` and `[SEP]` tokens. * The `tokenizer.get_vocab()` method also returns a list of these special tokens. * The `tokenizer.convert_ids_to_tokens()` method can also be used to convert a list of token IDs to a list of tokens that includes the special tokens. I hope this helps! Let me know if you have any other questions.

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 in a AutoTokenizer There are two main ways to get all tokens in a AutoTokenizer: **1. Using the `get_vocab()` method:** This method returns a dictionary where the keys are the tokens and the values are their corresponding indices. Here's an example: 

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from transformers import AutoTokenizer

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

vocab = tokenizer.get_vocab()

# Print all tokens
for token, index in vocab.items():
    print(f"{token}: {index}")
**2. Using the `all_special_tokens` and `all_tokens` properties:** The `all_special_tokens` property returns a list of all special tokens in the tokenizer, while the `all_tokens` property returns a list of all tokens, including both special and regular tokens. Here's an example: 
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from transformers import AutoTokenizer

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

# Print all special tokens
print(tokenizer.all_special_tokens)

# Print all tokens
print(tokenizer.all_tokens)
**Additional notes:** * The `get_vocab()` method is more efficient than using the `all_tokens` property, especially for large vocabularies. * The `all_tokens` property includes all special tokens, while the `get_vocab()` method only includes regular tokens. * You can also use the `convert_ids_to_tokens()` method to convert token indices to their corresponding tokens. Here are some additional resources that you may find helpful: * Hugging Face Transformers documentation on tokenizers: https://huggingface.co/docs/transformers/tokenizer_summary * Hugging Face Transformers documentation on the `AutoTokenizer` class: https://huggingface.co/docs/transformers/main_classes/tokenizer#autotokenizer * Stack Overflow question on how to get all tokens in a BertTokenizer: https://stackoverflow.com/questions/69687690/how-to-get-all-tokens-in-a-berttokenizer I hope this helps! Let me know if you have any other questions.

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 in a AutoTokenizer There are two main ways to get all tokens in a AutoTokenizer: ### 1. Using the `get_vocab()` method The `get_vocab()` method returns a dictionary where the keys are the tokens and the values are the corresponding token ids. You can then iterate over this dictionary to get all the tokens. 

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from transformers import AutoTokenizer

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

# Get all tokens and their corresponding ids
vocab = tokenizer.get_vocab()

# Print all tokens
for token in vocab:
    print(token)
### 2. Using the `convert_ids_to_tokens()` method The `convert_ids_to_tokens()` method takes a list of token ids and returns a list of the corresponding tokens. You can use this method to get all the tokens in the tokenizer's vocabulary by passing in a list of all the possible token ids. 
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from transformers import AutoTokenizer

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

# Get all token ids
token_ids = list(range(len(tokenizer)))

# Get all tokens
tokens = tokenizer.convert_ids_to_tokens(token_ids)

# Print all tokens
for token in tokens:
    print(token)
## Additional notes * The `get_vocab()` method is generally more efficient than the `convert_ids_to_tokens()` method, especially for large vocabularies. * The `get_vocab()` method returns all tokens in the tokenizer's vocabulary, including special tokens like `[CLS]` and `[SEP]`. * The `convert_ids_to_tokens()` method only returns the tokens that are actually used in the tokenizer's vocabulary. I hope this helps! Let me know if you have any other questions.

Substring Evaluation

Testing if the previous output contains the string get_vocab: True