Encoder: A multi-layer transformer encoder that processes input sequences and generates contextual embeddings.; Classifier: A feedforward neural network that takes the encoder's output and predicts ...

The Transformer architecture revolutionized NLP by replacing recurrent layers with attention mechanisms, enabling more efficient parallelization and better modeling of long-range dependencies. This ...

CNNs excel in handling grid-like data such as images, RNNs are unparalleled in their ability to process sequential data, GANs offer remarkable capabilities in generating new data samples, Transformers ...

Tamil language processing in NLP has yet to be outstanding, mainly because of the absence of high-quality resources. In this project, a novel approach to address these limitations is to build an ...

The positional encodings and the word vector embeddings are summed together then passed into both the encoder and decoder networks. While transformer neural networks use encoder/decoder schemas just ...

BERT: Encoder-only architecture with multiple layers of transformer blocks. GPT: Decoder-only architecture, also with multiple layers but designed for generative tasks. T5: Encoder-decoder ...