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 retrieve all tokens from an `AutoTokenizer` using Hugging Face's `transformers` library, you can follow these steps: 1. **Install the Transformers Library** (if you haven't already): 

1
2
   pip install transformers
2. **Load the AutoTokenizer:** The `AutoTokenizer` class automatically selects the appropriate tokenizer class for the specified pretrained model. 
1
2
3
4
5
   from transformers import AutoTokenizer

   # Replace 'bert-base-uncased' with your desired model name
   tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
3. **Retrieve the Vocabulary:** The tokenizer's vocabulary maps each token to a unique integer ID. You can access this vocabulary using the `get_vocab()` method, which returns a dictionary where keys are tokens and values are their corresponding IDs. 
1
2
   vocab = tokenizer.get_vocab()
4. **Extract All Tokens:** To get a list of all tokens, simply extract the keys from the vocabulary dictionary. 
1
2
   tokens = list(vocab.keys())
5. **(Optional) Sort Tokens by Their IDs:** If you want the tokens ordered by their ID (e.g., as they are used by the model), you can sort them accordingly. 
1
2
3
4
5
6
   # Create a list of tokens sorted by their IDs
   tokens_sorted = sorted(vocab.keys(), key=lambda x: vocab[x])

   # Alternatively, using a list comprehension with sorted items
   tokens_sorted = [token for token, idx in sorted(vocab.items(), key=lambda item: item[1])]
6. **Example Usage:** Here's a complete example that prints out the first 20 tokens: 
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
   from transformers import AutoTokenizer

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

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

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

   # (Optional) Sort tokens by their IDs
   tokens_sorted = sorted(vocab.keys(), key=lambda x: vocab[x])

   # Print the first 20 tokens
   print(tokens_sorted[:20])
**Output:** 
1
2
   ['[PAD]', '[unused1]', '[unused2]', '[unused3]', '[unused4]', '[unused5]', '[unused6]', '[unused7]', '[unused8]', '[unused9]', '[unused10]', '[unused11]', '[unused12]', '[unused13]', '[unused14]', '[unused15]', '[unused16]', '[unused17]', '[unused18]', '[unused19]']
*Note:* The actual tokens and their order will depend on the specific tokenizer and pretrained model you choose. 7. **Handling Large Vocabularies:** Be aware that tokenizers for large models (like BERT, GPT-2, etc.) can have vocabularies containing tens of thousands of tokens. If you attempt to print all tokens, it may overwhelm your console or consume significant memory. It's often useful to inspect specific parts of the vocabulary or analyze it in batches. 8. **Additional Notes:** - **Special Tokens:** Tokenizers typically include special tokens like `[PAD]`, `[CLS]`, `[SEP]`, etc. These are important for model functioning and should be considered when analyzing the vocabulary. - **Subword Tokenization:** Modern tokenizers often use subword units (like WordPiece, Byte-Pair Encoding, or SentencePiece). This means that what appears as individual tokens may not correspond to complete words, especially for rare or complex words. - **Saving and Loading Vocabularies:** If you need to save the tokens for later use: 
1
2
3
4
     with open("vocab.txt", "w", encoding="utf-8") as f:
         for token in tokens_sorted:
             f.write(token + "\n")
And to load them back: 
1
2
3
     with open("vocab.txt", "r", encoding="utf-8") as f:
         tokens_loaded = [line.strip() for line in f]
By following these steps, you can effectively retrieve and work with all tokens in an `AutoTokenizer`. This can be useful for tasks like vocabulary analysis, visualization, or custom token processing.

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 retrieve all tokens from an `AutoTokenizer` in Hugging Face's Transformers library, you can access the tokenizer's vocabulary. The vocabulary contains a mapping of tokens to their corresponding IDs. Here's a step-by-step guide on how to do this: ### 1. Install the Transformers Library If you haven't already installed the Transformers library, you can do so using `pip`: 

1
pip install transformers
### 2. Load the `AutoTokenizer` First, you'll need to load the appropriate tokenizer for your model. Replace `'bert-base-uncased'` with the model name you're using. 
1
2
3
4
from transformers import AutoTokenizer

# Replace 'bert-base-uncased' with your desired model
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
### 3. Access the Vocabulary The tokenizer's vocabulary can be accessed using the `get_vocab()` method. This method returns a dictionary where keys are token strings and values are their corresponding IDs. 
1
2
3
4
5
6
7
8
9
# Get the vocabulary dictionary
vocab = tokenizer.get_vocab()

# Example: Print the total number of tokens in the vocabulary
print(f"Vocabulary size: {len(vocab)}")

# Example: Print the first 10 tokens and their IDs
for token, idx in list(vocab.items())[:10]:
    print(f"Token: {token}\tID: {idx}")
### 4. Retrieving All Tokens as a List If you prefer to have a list of all tokens without their IDs, you can extract the keys from the vocabulary dictionary and sort them based on their IDs: 
1
2
3
4
5
6
7
8
# Sort tokens by their IDs to maintain the correct order
sorted_vocab = sorted(vocab.items(), key=lambda item: item[1])

# Extract just the tokens
all_tokens = [token for token, idx in sorted_vocab]

# Example: Print the first 10 tokens
print(all_tokens[:10])
### 5. Saving the Tokens to a File (Optional) If you want to save all tokens to a text file, you can do so as follows: 
1
2
3
4
5
with open('tokens.txt', 'w', encoding='utf-8') as f:
    for token in all_tokens:
        f.write(f"{token}\n")

print("All tokens have been saved to 'tokens.txt'.")
### 6. Example with a Different Model Here's how you can perform the same steps with a different model, such as GPT-2: 
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
from transformers import AutoTokenizer

# Load GPT-2 tokenizer
tokenizer = AutoTokenizer.from_pretrained('gpt2')

# Get vocabulary
vocab = tokenizer.get_vocab()
print(f"GPT-2 Vocabulary size: {len(vocab)}")

# Get all tokens sorted by their IDs
sorted_vocab = sorted(vocab.items(), key=lambda item: item[1])
all_tokens = [token for token, idx in sorted_vocab]

# Print first 10 tokens
print(all_tokens[:10])
### Notes - **Special Tokens**: Most tokenizers include special tokens like `<s>`, `</s>`, `<pad>`, `<unk>`, etc. These are used by the model for various purposes such as padding, unknown words, sentence boundaries, etc. - **Tokenizers with Subword Units**: If you're using a tokenizer based on subword units (like BPE or WordPiece), the tokens may include prefixes like `##` or special characters to indicate that they are part of a larger word. - **Large Vocabularies**: Be cautious when handling very large vocabularies, as loading all tokens into memory or writing them to a file can consume significant resources. ### Additional Resources - [Hugging Face Transformers Documentation](https://huggingface.co/docs/transformers/index) - [AutoTokenizer Class Reference](https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.AutoTokenizer) By following these steps, you can effectively retrieve and utilize all tokens from an `AutoTokenizer` for your NLP tasks.

Substring Evaluation

Testing if the previous output contains the string get_vocab: True