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` object from 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 the corresponding token IDs. Here's an example: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
from transformers import AutoTokenizer

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

# Get the vocabulary
vocab = tokenizer.get_vocab()

# Print all tokens
for token in vocab:
    print(token)
This will print all the tokens in the vocabulary of the `bert-base-uncased` tokenizer. Alternatively, if you want to get the tokens as well as their corresponding token IDs, you can iterate over the `vocab.items()`: 
1
2
for token, token_id in vocab.items():
    print(f"Token: {token}, Token ID: {token_id}")
This will print each token along with its corresponding token ID. Note that some tokenizers may have a very large vocabulary, so printing all tokens might not be practical in some cases. You can also access specific tokens by indexing the `vocab` dictionary with the token or token ID.

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 an `AutoTokenizer` from the Hugging Face Transformers library, you can use the `get_vocab()` method. This method returns a dictionary where the keys are the token strings, and the values are their corresponding token IDs. Here's an example of how to use it: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
from transformers import AutoTokenizer

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

# Get the tokenizer's vocabulary
vocab = tokenizer.get_vocab()

# Print all tokens
for token, token_id in vocab.items():
    print(f"Token: {token}, ID: {token_id}")
This will print out all the tokens in the tokenizer's vocabulary, along with their corresponding token IDs. Note that the vocabulary of a pre-trained tokenizer can be quite large (e.g., the BERT tokenizer has around 30,000 tokens), so printing all of them might not be very practical. In most cases, you'll be more interested in tokenizing specific text inputs using the `tokenizer.encode()` or `tokenizer.batch_encode_plus()` methods, rather than inspecting the entire 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

To get all the tokens in an `AutoTokenizer` from the Hugging Face Transformers library, you can use the `get_vocab()` method. This method returns a dictionary containing all the token-to-index mappings. Here's an example of how to use it: 

1
2
3
4
5
6
7
from transformers import AutoTokenizer

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

# Get the vocabulary
vocab = tokenizer.get_vocab()
The `vocab` dictionary contains all the tokens as keys and their corresponding indices as values. If you want to get a list of all the tokens, you can use the following code: 
1
tokens = list(vocab.keys())
This will create a list `tokens` containing all the token strings. Alternatively, if you want to get a list of all the token indices, you can use: 
1
token_indices = list(vocab.values())
This will create a list `token_indices` containing all the token indices. Note that the `AutoTokenizer` uses special tokens like `[CLS]`, `[SEP]`, `[PAD]`, etc., which will also be included in the vocabulary. Additionally, the tokenizer uses subword tokenization, which means that some tokens may represent parts of words (e.g., `##er` for the word "player").

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 an AutoTokenizer, you can use the `get_vocab()` method. This method returns a dictionary where the keys are the token strings, and the values are the corresponding token IDs. Here's an example of how to use it: 

1
2
3
4
5
6
7
8
9
from transformers import AutoTokenizer

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

# Get the vocabulary (token to token_id mapping)
vocab = tokenizer.get_vocab()

# Print the vocabulary
print(vocab)
This will output a dictionary containing all the tokens and their corresponding IDs in the tokenizer's vocabulary. Alternatively, you can iterate over the `vocab_keys` attribute of the tokenizer object to get a list of all the token strings: 
1
2
3
4
5
# Get a list of all token strings
tokens = list(tokenizer.vocab_keys)

# Print the tokens
print(tokens)
Note that the order of tokens in the list may not be the same as the order of tokens in the dictionary returned by `get_vocab()` since dictionaries are inherently unordered data structures.

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 an `AutoTokenizer` from the Hugging Face Transformers library, you can use the `get_vocab()` method. This method returns a dictionary where the keys are the token IDs and the values are the corresponding token strings. Here's an example: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
from transformers import AutoTokenizer

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

# Get the tokenizer's vocabulary
vocab = tokenizer.get_vocab()

# Print the vocabulary
for token_id, token_str in vocab.items():
    print(f"Token ID: {token_id}, Token String: {token_str}")
This will print out all the token IDs and their corresponding token strings from the tokenizer's vocabulary. Note that the vocabulary can be quite large, especially for larger language models like GPT-3. If you only want to inspect a subset of the vocabulary, you can slice the `vocab` dictionary or use other dictionary methods. For example, to get the first 10 tokens: 
1
2
3
first_10_tokens = list(vocab.items())[:10]
for token_id, token_str in first_10_tokens:
    print(f"Token ID: {token_id}, Token String: {token_str}")
Alternatively, you can filter the vocabulary by token string or token ID using dictionary comprehension or other methods.

Substring Evaluation

Testing if the previous output contains the string get_vocab: True