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, you can use the `get_vocab()` method. This method returns a dictionary where the keys are the tokens and the values are their corresponding token IDs. Here's an example of how you can retrieve all the tokens: 

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from transformers import AutoTokenizer

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

# Get the vocabulary (all tokens)
vocab = tokenizer.get_vocab()

# Print the tokens
for token, token_id in vocab.items():
    print(f"Token: {token}, ID: {token_id}")
In this example, we first load the tokenizer using `AutoTokenizer.from_pretrained()`, specifying the desired pre-trained model (e.g., "bert-base-uncased"). Then, we call `tokenizer.get_vocab()` to retrieve the vocabulary, which is a dictionary containing all the tokens as keys and their corresponding token IDs as values. Finally, we iterate over the vocabulary items using `vocab.items()` and print each token along with its token ID. Note that the output may be quite large, as the vocabulary size of pre-trained models is typically in the tens of thousands. If you just want to see a few examples, you can slice the dictionary or print a subset of the tokens. For example, to print only the first 10 tokens: 
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for token, token_id in list(vocab.items())[:10]:
    print(f"Token: {token}, ID: {token_id}")
This will give you an idea of what the tokens look like without printing 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`, you can use the `get_vocab()` method. Here's an example of how to retrieve all the tokens: 

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from transformers import AutoTokenizer

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

# Get the vocabulary (all tokens)
vocab = tokenizer.get_vocab()

# Print the tokens
for token, index in vocab.items():
    print(f"Token: {token}, Index: {index}")
In this example: 1. We import the `AutoTokenizer` class from the `transformers` library. 2. We load a pre-trained tokenizer using `AutoTokenizer.from_pretrained()`. In this case, we're using the "bert-base-uncased" tokenizer, but you can replace it with the desired pre-trained tokenizer name or path. 3. We call the `get_vocab()` method on the tokenizer, which returns a dictionary where the keys are the tokens and the values are their corresponding indices in the vocabulary. 4. We iterate over the `vocab` dictionary and print each token along with its index. The `get_vocab()` method returns all the tokens in the tokenizer's vocabulary, including special tokens like `[CLS]`, `[SEP]`, `[PAD]`, etc. Note that the exact tokens and their indices may vary depending on the specific tokenizer you are using. The "bert-base-uncased" tokenizer used in the example is just one of many available tokenizers. By using this approach, you can access and examine all the tokens in the tokenizer's vocabulary, which can be helpful for understanding the tokenization process and the specific tokens used by the tokenizer.

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

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from transformers import AutoTokenizer

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

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

# Print the number of tokens in the vocabulary
print("Number of tokens:", len(vocab))

# Print the first 10 tokens and their token IDs
print("First 10 tokens:")
for token, token_id in list(vocab.items())[:10]:
    print(f"Token: {token}, Token ID: {token_id}")
In this example: 1. We load the `AutoTokenizer` with the desired pretrained model (`"bert-base-uncased"` in this case). 2. We call the `get_vocab()` method on the tokenizer to obtain the vocabulary, which is a dictionary mapping tokens to their token IDs. 3. We print the total number of tokens in the vocabulary using `len(vocab)`. 4. We iterate over the first 10 items in the vocabulary dictionary using `list(vocab.items())[:10]` and print each token and its corresponding token ID. The output will display the number of tokens in the vocabulary and the first 10 tokens along with their token IDs. Note that the specific tokens and their token IDs will depend on the pretrained tokenizer you are using. Different tokenizers may have different vocabularies and token mappings.

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

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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 the total number of tokens
print("Total number of tokens:", len(vocab))

# Print the first 10 tokens and their IDs
print("First 10 tokens:")
for token, token_id in list(vocab.items())[:10]:
    print(f"Token: {token}, ID: {token_id}")
In this example: 1. We load the `AutoTokenizer` with the desired pre-trained model (e.g., "bert-base-uncased"). 2. We call the `get_vocab()` method on the tokenizer to retrieve the vocabulary as a dictionary. 3. We print the total number of tokens in the vocabulary using `len(vocab)`. 4. We iterate over the first 10 items in the vocabulary dictionary using `list(vocab.items())[:10]` and print each token along with its corresponding token ID. The output will display the total number of tokens in the tokenizer's vocabulary and the first 10 tokens with their respective IDs. Note that the specific tokens and their IDs will depend on the pre-trained model you are using with the `AutoTokenizer`.

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`, you can use the `get_vocab()` method. This method returns the vocabulary of the tokenizer as a dictionary where the keys are the tokens and the values are their corresponding token IDs. Here's an example of how to get all the tokens using the `AutoTokenizer`: 

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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 the tokens
for token in vocab.keys():
    print(token)
In this example: 1. First, we import the `AutoTokenizer` class from the `transformers` library. 2. We load a pre-trained tokenizer, in this case, the "bert-base-uncased" tokenizer, using `AutoTokenizer.from_pretrained()`. You can replace "bert-base-uncased" with the desired pre-trained tokenizer name or path. 3. We call the `get_vocab()` method on the tokenizer to obtain the vocabulary dictionary. 4. Finally, we iterate over the keys of the `vocab` dictionary using a `for` loop and print each token. The output will be a list of all the tokens in the tokenizer's vocabulary. Note that the number of tokens can be quite large, depending on the tokenizer you are using. If you just want to see a few examples of tokens, you can modify the loop to print only a subset of the tokens or use slicing to limit the number of tokens printed. 
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# Print the first 10 tokens
for token in list(vocab.keys())[:10]:
    print(token)
This will print only the first 10 tokens in the vocabulary.

Substring Evaluation

Testing if the previous output contains the string get_vocab: True