3 Results The results of clustering using the proposed transformer-based embedding method are presented as a confusion matrix in Table 1. Each item from the IPIP-50 was encoded into a semantic vector ...

The model adeptly reconstructs complete degradation pathways, addressing the intricate challenge of pathway elucidation. Attention analyses indicate that the TP-Transformer discerns reactive moieties ...

📘 Abstractive Summarization This repository contains implementations of Abstractive Text Summarization using various transformer-based models such as BART, PEGASUS, T5, Longformer, ProphetNet, RAG, ...

Infrared (IR) spectroscopy, a type of vibrational spectroscopy, provides extensive molecular structure details and is a highly effective technique for chemists to determine molecular structures.

This project compares Transformer-based, CNN-based extractive, and LSTM-based abstractive models for long-form text summarization. It evaluates accuracy, readability, and coherence using the CNN/Daily ...

Figure 2. Model Structure. Pre-processing: Pre-processing is a crucial step in preparing raw text data for analysis. It transforms messy, inconsistent text into a clean, standardized format, enhancing ...

Transformers deep learning models for missing data imputation: an application of the ReMasker model on a psychometric scale ...

Studies were excluded if they did not describe transformer-based models, did not focus on clinical text summarization, did not engage with free-text data, were not original research, were ...

Researchers at Google Research have introduced a novel approach called Selective Attention, which aims to enhance the efficiency of transformer models by enabling the model to ignore no longer ...