To work with a dataset from Hugging Face and train a model with a classification layer using an encoder-only model, followed by a decoder model, we will follow the steps below. For this example, we ...

This project implements a GPT-like transformer model consisting of an encoder and a decoder for various natural language processing tasks, including text classification and language modeling. The ...

The encoder uses stacked multi-head self-attention layers to encode the input sequence and generate latent representations. The decoder then performs cross-attention on these representations to ...

The ability of transformers to handle data sequences without the need for sequential processing makes them extremely effective for various NLP tasks, including translation, text summarization, and ...

It becomes important to remember the sequence of the data in the NLP. Similarly, the sequence we have in time series is important to learn to make the forecasting more accurate. As we have discussed ...

Abstract: 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 ...

Conventional encoder-decoder sequence models often grapple with an information ... The advent of the attention mechanism has indisputably transformed the landscape of NLP model creation. Its ...