Initial Query

How do I get all tokens in a AutoTokenizer?

LLM Generation

Query

How do I get all tokens in a AutoTokenizer?

Output

To get all tokens from an `AutoTokenizer` in the Hugging Face Transformers library, you can use the `get_vocab()` method. This method returns a dictionary where the keys are the tokens and the values are their corresponding IDs in the tokenizer's vocabulary. Here’s how you can do it: 

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from transformers import AutoTokenizer

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

# Get the vocabulary
vocab = tokenizer.get_vocab()

# Extract all tokens
tokens = list(vocab.keys())

# Print the tokens
print(tokens)
### Explanation: - `AutoTokenizer.from_pretrained("bert-base-uncased")`: Loads the tokenizer for the `bert-base-uncased` model. You can replace `"bert-base-uncased"` with any other model name. - `tokenizer.get_vocab()`: Returns a dictionary where the keys are tokens and the values are their corresponding IDs. - `list(vocab.keys())`: Extracts all the tokens from the vocabulary. ### Example Output: 
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['[PAD]', '[UNK]', '[CLS]', '[SEP]', '[MASK]', 'the', 'of', 'and', 'to', 'a', 'in', 'for', 'is', ...]
This will give you a list of all the tokens in the tokenizer's vocabulary. Note that the tokens include special tokens like `[PAD]`, `[UNK]`, `[CLS]`, etc., as well as regular words and subwords.

Substring Evaluation

Testing if the previous output contains the string get_vocab: True