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 in an AutoTokenizer, you can use the `get_vocab()` method, which returns a dictionary mapping token strings to their respective token IDs. Once you have this dictionary, you can iterate through its keys to get all the tokens. Here's an example code snippet: 

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from transformers import AutoTokenizer

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

# Get the vocabulary dictionary
vocab = tokenizer.get_vocab()

# Print all the tokens
for token in vocab:
    print(token)
Note that some tokenizers may include special tokens such as [PAD], [UNK], [CLS], and [SEP] in their vocabulary. You may want to filter out these special tokens if you only want to get the actual words or subwords in the vocabulary. Additionally, some tokenizers may have a large vocabulary size, so iterating through all the tokens may take a long time or consume a lot of memory. If you only need to get a subset of the tokens, you may want to use a different approach, such as using the `encode()` method to tokenize a specific text input and get the corresponding token IDs.

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

To get all tokens in a Hugging Face `AutoTokenizer`, you can use the `get_vocab()` method to access the vocabulary, and then convert each token ID to its corresponding token string. Here's an example: 

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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()

# Convert token IDs to token strings
all_tokens = [vocab[i] for i in range(len(vocab))]

# Print all tokens
print(all_tokens)
Note that the `get_vocab()` method returns a dictionary mapping token IDs to token strings, so you can also access individual tokens by their ID using `vocab[token_id]`. Also note that the tokenizer may include special tokens such as `[CLS]` and `[SEP]` that are not part of the vocabulary, so you may want to filter these out if you only want to get the actual vocabulary tokens. You can do this using the `additional_special_tokens` attribute of the tokenizer: 
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# Filter out special tokens
vocab = {k: v for k, v in tokenizer.get_vocab().items() if k not in tokenizer.additional_special_tokens}
all_tokens = list(vocab.values())

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

To get all tokens from an AutoTokenizer, you can use the `get_vocab()` method to get the vocabulary, which is a dictionary mapping tokens to their IDs. Here's an example: 

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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()

# Print all the tokens
for token in vocab:
    print(token)
Note that this will print out a lot of special tokens that are used for padding, masking, and other purposes, as well as the actual tokens that make up the vocabulary. If you only want to get the actual tokens, you can filter out the special tokens like this: 
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# Define a regular expression pattern to match special tokens
special_tokens_pattern = r'^[^\w\s]+$'

# Filter out the special tokens
vocab = {token: token_id for token, token_id in tokenizer.get_vocab().items() if not re.match(special_tokens_pattern, token)}

# Print all the tokens
for token in vocab:
    print(token)
This will print out only the actual tokens that make up the vocabulary, without any of the special 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

To get all tokens from a `AutoTokenizer`, you can use the `get_vocab()` method which returns a dictionary mapping tokens to their corresponding IDs. However, this will include special tokens such as `[PAD]`, `[CLS]`, `[SEP]`, `[MASK]`, etc. If you want to get only the actual vocabulary tokens, you can filter out the special tokens from the dictionary. Here's an example code snippet: 

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from transformers import AutoTokenizer

# initialize the tokenizer with your pre-trained model name
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

# get the vocab dictionary
vocab = tokenizer.get_vocab()

# filter out special tokens
actual_tokens = {k: v for k, v in vocab.items() if k not in tokenizer.special_tokens_map.keys()}

# print the number of actual tokens
print(len(actual_tokens))

# print the first 10 tokens
print(list(actual_tokens.keys())[:10])
Note that the special tokens may vary depending on the pre-trained model you are using, so you may need to modify the filtering step accordingly.

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

To get all tokens from a Hugging Face `AutoTokenizer`, you can use the `get_vocab()` method. This will return a dictionary where the keys are the tokens and the values are the corresponding IDs. Here's an example of how you can get all tokens from a `BertTokenizer`: 

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from transformers import BertTokenizer

# Initialize the tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Get all tokens
all_tokens = list(tokenizer.get_vocab().keys())

# Print the number of tokens
print(len(all_tokens))
Note that the `get_vocab()` method returns a dictionary, so the order of the tokens is not guaranteed. If you need the tokens in a specific order, you may want to sort them before using them. Also note that some tokens, such as special tokens and subwords, may not be included in the vocabulary returned by `get_vocab()`. If you need to include all possible tokens, you may want to use a different approach. 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