BERT Returns NaN as Lost
If you are familiar with natural language processing (NLP) and deep learning, you have probably heard of BERT (Bidirectional Encoder Representations from Transformers). BERT is a pre-trained language model developed by Google that has revolutionized various NLP tasks, including text classification and named entity recognition.
However, one common issue that researchers and developers have encountered when working with BERT is the occurrence of "NaN" values during training or inference. In this blog post, we will explore why BERT sometimes returns NaN and how to address this problem.
NaN, which stands for "Not a Number," is a special floating-point value used to represent undefined or unrepresentable results in computations. When training or using BERT, encountering NaN values can be frustrating and can hinder the model's performance. There are a few reasons why BERT might return NaN:
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Diverging Gradients: BERT is trained using a process called backpropagation, where gradients are computed and used to update the model's parameters. If the gradients become too large or too small, they can lead to numerical instability, resulting in NaN values. This issue is often referred to as "exploding" or "vanishing" gradients.
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Out-of-Vocabulary (OOV) Tokens: BERT relies on a fixed-size vocabulary, meaning that any words or tokens that are not included in the vocabulary will be treated as out-of-vocabulary tokens. When processing text, if BERT encounters an OOV token, it may struggle to generate meaningful representations, leading to NaN values.
To address the issue of NaN values in BERT, here are some potential solutions:
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Gradient Clipping: One approach to mitigate the problem of diverging gradients is to apply gradient clipping. Gradient clipping bounds the magnitude of the gradients during training, preventing them from growing too large. This technique helps stabilize training and reduces the likelihood of NaN values.
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Vocabulary Expansion: Since OOV tokens can contribute to the occurrence of NaN values, expanding the vocabulary of BERT can be beneficial. This can be done by adding more words or subwords to the vocabulary or by using subword tokenization techniques such as Byte-Pair Encoding (BPE) or SentencePiece. By including a wider range of tokens, BERT can better handle unseen words and reduce the chances of NaN values.
While these approaches can help mitigate the issue of NaN values in BERT, it's important to note that they might not completely eliminate the problem. Fine-tuning hyperparameters, adjusting learning rates, or trying different optimization algorithms can also be effective in reducing the likelihood of NaN values.
Remember that NaN values in BERT are not an inherent flaw of the model. They are a challenge that can be overcome with careful optimization and fine-tuning. By understanding the causes and implementing appropriate measures, developers and researchers can make the most of BERT's powerful language representation capabilities without being lost in NaN.
