To understand how to fine-tune Hugging Face model with your own data for sentence classification, I would recommend studying code under this section Sequence Classification with IMDb Reviews. There is a very helpful section Fine-tuning with custom datasets. Xiong et al., 2020 found that the magnitude of the gradients through layer normalization is inversely proportional to magnitude of the input. Your starting point should be Hugging Face documentation. To conclude, we’ve seen that residual connections are needed to allow us to train deep networks. These cause gradient explosion, which is resolved by using layer normalization. The self-attention computation causes unbalanced gradients, which necessitates the use of Adam (figure 4). In the next section, we’ll see that layer normalization and Adam themselves cause more problems, which ultimately result in the need for learning rate warm-up. This is a direct consequence of the mathematical expression for self-attention. The Adam optimizer fixes this problem by essentially having different learning rates for each parameter. Residual connections: Each transformer layer takes the $I\times D$ data matrix $\mathbf$, and so the former parameters change much more slowly. We need to get a pre-trained Hugging Face model, we are going to fine-tune it with our data: We classify two labels in this example. They offer a wide variety of architectures to choose from (BERT, GPT-2, RoBERTa etc) as well as a hub of pre-trained models uploaded by users and organisations. One of the contributions of the original transformer paper was to use four tricks that collectively allow stable training:ġ. Transformers ( Hugging Face transformers) is a collection of state-of-the-art NLU (Natural Language Understanding) and NLG (Natural Language Generation ) models. ![]() Tricks for training transformersĭespite their broad applications, transformers are surprisingly difficult to train from scratch. Maby George Mihaila This notebook is designed to use a pretrained transformers model and fine-tune it on a classification task. In this final part, we discuss challenges with transformer training dynamics and introduce some of the tricks that practitioners use to get transformers to converge. This discussion will be suitable for researchers who already understand the transformer architecture, and who are interested in training transformers and similar models from scratch. In part I of this tutorial we introduced the self-attention mechanism and the transformer architecture. In part II, we discussed position encoding and how to extend the transformer to longer sequence lengths. We also discussed connections between the transformer and other machine learning models.
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