Loading pre-trained Transformer model with AddedTokens using from_pretrained

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04-10-2024

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Loading Pre-trained Transformer Models with Added Tokens: A Guide for NLP Practitioners

In the realm of Natural Language Processing (NLP), transformers have become the dominant architecture for various tasks, such as text classification, question answering, and machine translation. When working with specific domains or specialized tasks, you might need to extend the vocabulary of a pre-trained transformer model with additional tokens, such as special symbols, domain-specific terms, or even entire sentences.

This article will guide you through the process of loading pre-trained transformer models with added tokens using the from_pretrained function provided by popular NLP libraries like Hugging Face's Transformers. We will cover the essential concepts and provide practical examples to ensure you can seamlessly integrate your custom tokens into your NLP workflows.

Scenario: Adding Custom Tokens to a Pre-trained Model

Let's imagine you're building a chatbot for a medical domain. You want to use a pre-trained BERT model but need to add tokens for medical terms like "symptom," "diagnosis," and "treatment." To achieve this, you first need to specify the new tokens in a vocabulary file and then leverage the from_pretrained function to load the pre-trained model with these added tokens.

Code Example: Loading BERT with Custom Tokens

Here's a simple example using the Transformers library:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Specify the path to your custom vocabulary file
custom_vocab_file = "medical_vocabulary.txt"

# Load the pre-trained tokenizer with the custom vocabulary
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased",
                                       add_prefix_space=True,
                                       additional_special_tokens=[
                                           "<symptom>", "<diagnosis>", "<treatment>"
                                       ],
                                       additional_special_tokens_prefixes=["<", ">"],
                                       use_fast=True)

# Update the tokenizer's vocabulary with the custom tokens
tokenizer.add_tokens(tokenizer.vocab_size + 1)

# Load the pre-trained model with the updated tokenizer
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", 
                                                  from_tf=False, 
                                                  config=tokenizer.config)

# Resize the model's embedding layer to accommodate the added tokens
model.resize_token_embeddings(len(tokenizer))

Explanation:

1. AutoTokenizer.from_pretrained: This function loads the tokenizer associated with the specified pre-trained model ("bert-base-uncased"). We use the add_prefix_space argument to ensure proper tokenization when using spaces. We then define our new tokens and their prefixes.
2. tokenizer.add_tokens: This method expands the tokenizer's vocabulary by adding new tokens.
3. AutoModelForSequenceClassification.from_pretrained: This function loads the pre-trained model with the updated configuration from the tokenizer. The from_tf=False argument specifies that the model is not a TensorFlow model.
4. model.resize_token_embeddings: This step is crucial to ensure that the model's embedding layer is correctly sized to handle the added tokens.

Insights and Considerations

• Vocabulary File: Creating a vocabulary file is crucial for specifying the added tokens. Each line of the file should represent a unique token.
• Tokenization: Make sure your chosen tokenizer (BERT, RoBERTa, etc.) handles tokenization appropriately. The add_prefix_space argument in the tokenizer initialization is essential to prevent token splitting.
• Model Compatibility: Ensure the added tokens align with the capabilities of the pre-trained model. For example, adding specialized medical terms might improve the model's performance on medical text, but adding arbitrary tokens might not necessarily enhance its capabilities.
• Fine-tuning: After adding tokens, you may need to fine-tune the model on your domain-specific data to optimize its performance.
• Resource Management: Be aware that adding tokens will increase the model's memory footprint. Consider using a GPU with sufficient memory, especially when dealing with large vocabulary extensions.

Conclusion

Adding custom tokens to pre-trained transformer models is a powerful technique to tailor your NLP models to specific domains or tasks. By using the from_pretrained function with careful attention to vocabulary management, tokenization, and model compatibility, you can efficiently integrate custom tokens into your NLP workflows. This approach empowers you to leverage the power of pre-trained models while adapting them to your specific requirements.

For further information, refer to the official documentation of your chosen NLP library (e.g., Hugging Face Transformers) and consider exploring resources such as tutorials and blog posts focusing on specific model architectures and use cases.