AttributeError: ‘MPNetTokenizerFast’ object has no attribute ‘split_special_tokens’ after recent update in sentence-transformer==2.2.2? Don’t Panic! Here’s the Solution!

Have you been hit with the infamous “AttributeError: ‘MPNetTokenizerFast’ object has no attribute ‘split_special_tokens'” error after updating to sentence-transformer version 2.2.2? Well, you’re not alone! In this article, we’ll walk you through the causes, symptoms, and most importantly, the solutions to get your code up and running again.

What is sentence-transformer and why is it essential?

Sentence-transformer is a popular Python library used for sentence embeddings, text classification, and semantic search. It provides an efficient way to compute dense vector representations for sentences, allowing you to perform various NLP tasks, such as clustering, classification, and information retrieval. With its ease of use and impressive performance, sentence-transformer has become a go-to tool for many natural language processing enthusiasts and professionals alike.

The Error: AttributeError: ‘MPNetTokenizerFast’ object has no attribute ‘split_special_tokens’

The error message “AttributeError: ‘MPNetTokenizerFast’ object has no attribute ‘split_special_tokens'” typically occurs when you’re trying to use the `split_special_tokens` method on an `MPNetTokenizerFast` object, which no longer exists in the updated version of sentence-transformer.

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer('paraphrase-mpnet-base-v2')
tokenizer = model.tokenizer

# This line will raise the AttributeError
tokenizer.split_special_tokens("This is a test sentence")

Causes of the Error

So, what led to this error? The main culprit is the recent update to sentence-transformer version 2.2.2, which introduced significant changes to the `MPNetTokenizerFast` class. Specifically, the `split_special_tokens` method was removed, breaking backward compatibility and causing this error.

Symptoms of the Error

When you encounter this error, you might experience the following symptoms:

• Python code crashes with an AttributeError message
• Unable to use the `split_special_tokens` method with `MPNetTokenizerFast` objects
• Inability to perform sentence embeddings, text classification, or semantic search using sentence-transformer

Solutions to the Error

Fear not, dear reader! We’ve got you covered. Here are some solutions to help you overcome this obstacle:

Solution 1: Downgrade sentence-transformer to version 2.1.0

The easiest solution is to revert to the previous version of sentence-transformer, where the `split_special_tokens` method still exists. You can do this by running the following command in your terminal:

pip install sentence-transformers==2.1.0

Once you’ve downgraded, your code should work as expected. Note that this solution might not be ideal, as you’ll miss out on new features and bug fixes introduced in version 2.2.2.

Solution 2: Modify your code to use the `encode` method

In sentence-transformer version 2.2.2, the `encode` method is the recommended way to tokenize and encode text. You can modify your code to use the `encode` method instead of `split_special_tokens`. Here’s an example:

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer('paraphase-mpnet-base-v2')
tokenizer = model.tokenizer

input_text = "This is a test sentence"
encoded_input = tokenizer.encode(input_text, return_tensors='pt')

# Use the encoded input for further processing
print(encoded_input)

In this example, we use the `encode` method to tokenize and encode the input text, which returns a tensor that can be used for further processing.

Solution 3: Create a custom tokenizer class

If you’re feeling adventurous, you can create a custom tokenizer class that inherits from `MPNetTokenizerFast` and adds the `split_special_tokens` method. Here’s an example:

from transformers import MPNetTokenizerFast

class CustomTokenizer(MPNetTokenizerFast):
    def split_special_tokens(self, text):
        # Implement your custom logic for splitting special tokens
        return tokens

tokenizer = CustomTokenizer('paraphase-mpnet-base-v2')
tokenizer.split_special_tokens("This is a test sentence")

In this example, we create a custom tokenizer class `CustomTokenizer` that inherits from `MPNetTokenizerFast`. We then implement the `split_special_tokens` method according to our needs.

Conclusion

In conclusion, the “AttributeError: ‘MPNetTokenizerFast’ object has no attribute ‘split_special_tokens'” error is a minor setback that can be easily overcome with the solutions provided in this article. Whether you choose to downgrade, modify your code, or create a custom tokenizer class, you’ll be back to computing sentence embeddings and performing text classification in no time.

Additional Tips and Resources

For more information on sentence-transformer and its usage, we recommend checking out the official documentation and GitHub repository:

• Sentence-Transformer Documentation
• Sentence-Transformer GitHub Repository

Additionally, if you’re experiencing issues with sentence-transformer or have questions about its usage, you can reach out to the community on the GitHub issues page or relevant forums.

We hope this article has been informative and helpful in resolving the “AttributeError: ‘MPNetTokenizerFast’ object has no attribute ‘split_special_tokens'” error. Happy coding!

Solution	Pros	Cons
Downgrade to version 2.1.0	+ Easy to implement, + No code changes required	– Miss out on new features and bug fixes
Modify code to use the `encode` method	+ Recommended way of tokenizing and encoding text, + No need to create custom classes	– Requires code changes, – Might require adjustments to downstream processing
Create a custom tokenizer class	+ Flexibility to implement custom logic, + Can be used with other models	– Requires more implementation effort, – Might require additional testing

This table provides a summary of the pros and cons of each solution, helping you make an informed decision based on your specific needs.

Frequently Asked Question

Are you struggling with the “AttributeError: ‘MPNetTokenizerFast’ object has no attribute ‘split_special_tokens'” error after updating to sentence-transformer==2.2.2? Worry no more! We’ve got you covered with these FAQs.