How do I get all tokens in a AutoTokenizer?
How do I get all tokens in a AutoTokenizer?
If you're using Hugging Face's Transformers library, you can retrieve all tokens from an AutoTokenizer by accessing its vocabulary. Depending on the tokenizer and your needs, here are a couple of common methods: 1. Using get_vocab(): • This method returns a dictionary mapping tokens to their corresponding IDs. • Example: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") vocab = tokenizer.get_vocab() # Dictionary: token -> id all_tokens = list(vocab.keys()) print(all_tokens) 2. Using vocab_size and convert_ids_to_tokens: • Some tokenizers support converting a range of IDs back to tokens. • Example: all_tokens = [tokenizer.convert_ids_to_tokens(i) for i in range(tokenizer.vocab_size)] print(all_tokens) Note: • The first method (get_vocab) directly gives you the mapping, which is often the easiest way to get all tokens. • Depending on the tokenizer type (e.g., whether it’s "fast" or not) and its underlying structure (like if subword tokens are handled differently), ensure that you verify that the output meets your needs. • Also, be aware that some tokenizers might include additional special tokens that you may or may not want to include. You can inspect these using tokenizer.special_tokens_map if needed. This is how you can get all tokens in an AutoTokenizer from Hugging Face.
Testing if the previous output contains the string get_vocab: True
How do I get all tokens in a AutoTokenizer?
How do I get all tokens in a AutoTokenizer?
If you want to retrieve all the tokens (i.e. the entire vocabulary) from an AutoTokenizer, you have a couple of options. Here are two common approaches: 1. Using get_vocab(): • When you load the tokenizer, you can call its get_vocab() method. This returns a dictionary that maps tokens to their corresponding IDs. • For example: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") vocab = tokenizer.get_vocab() # This is a dict: {token: id} all_tokens = list(vocab.keys()) print(all_tokens) 2. Using convert_ids_to_tokens(): • Another way is to use the tokenizer’s vocab_size attribute and then convert a range of IDs to tokens. • This ensures that the tokens are in the order of their IDs: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") all_tokens = [tokenizer.convert_ids_to_tokens(i) for i in range(tokenizer.vocab_size)] print(all_tokens) Both methods will give you the list of tokens in the tokenizer’s vocabulary. Choose the one that best fits your needs (for example, if order matters, the second method might be preferable).
Testing if the previous output contains the string get_vocab: True