This Document provides a concise overview of the key modules that constitute My Transformers Implementation from scratch. Each module plays a crucial role in the architecture, contributing to its ability to process and understand natural language.
The self-attention mechanism is the cornerstone of the Transformers architecture. It enables the model to weigh the importance of different words in a sequence relative to each other. By modeling relationships between words, the self-attention mechanism captures contextual information and long-range dependencies, making it particularly effective for understanding the nuances of language.
Positional encodings are essential for retaining the order of words in a sequence, as traditional neural networks do not inherently encode positional information. Various techniques, such as sine and cosine functions or learned embeddings, can be employed to incorporate positional encodings into the model. These encodings enhance the architecture's ability to process sequences effectively.
Multi-head Attention is a module for attention mechanisms which runs an attention mechanism in parallel. By allowing the model to attend to different positions and aspects of the input simultaneously, multi-head attention captures diverse patterns and relationships within the data. The outputs of multiple attention heads are concatenated and linearly transformed, enriching the model's representation.
Feedforward neural networks are employed to process and transform the representations learned through attention mechanisms. These networks consist of multiple layers of fully connected units and non-linear activation functions. They contribute to refining the model's understanding of the input data and facilitating feature extraction.
Layer normalization and residual connections are essential components for stabilizing and accelerating the training of deep neural networks. Layer normalization normalizes the inputs of each layer, reducing the internal covariate shift. Residual connections, also known as skip connections, mitigate the vanishing gradient problem and facilitate the flow of gradients through the network.
The true power of the Transformers architecture lies in the interaction and synergy between these modules. The self-attention mechanism captures contextual relationships, positional encodings ensure sequence order is preserved, multi-head attention captures diverse patterns, feedforward neural networks refine representations, and layer normalization with residual connections facilitate efficient training. Together, these components create a holistic and versatile architecture for various natural language processing tasks.
Feel free to explore each module's implementation details which is presented within this repo.
Heres The Link to the Paper which introduced the Transformer Architecture for those who wants to dwelve deeper.