Code for CAET5

This repository implements an end to end encoder transformer model completely from scratch in C

Transformer Encoder with Char information for text classification

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An encoder-transformer architecture-based framework for multi-variate time series prediction with a prognostics use case.

基于transformer编码器的情感四分类器

A minimal, educational C# implementation of the Transformer architecture (encoder–decoder, multi-head self-attention, feed-forward layers, and positional encodings). Perfect for learning how Transformers work under the hood without heavy frameworks.

explainable AI implemented for RUL prediction for the CMAPPS dataset, t-SNE and LIME are used for explainability, an Encoder Transformer model is used as prognostics model

[EACL 2023] Revisiting Offline Compression: Going Beyond Factorization-based Methods for Transformer Language Models

Implementation of various models for the classification of single-lead and multi-lead ECGs using the Physionet 2021 challenge data. Repository for my Master Thesis @MaastrichtUniversity.

Code for paper : http://dx.doi.org/10.2139/ssrn.4317584

use c++ to rewrite the part of encoder of transfomer(google tensor2tensor)

Representing words with balancing between context and character information

"A Transformer-based deep learning model for advanced emotion detection in tweets — combining powerful GloVe embeddings with custom text preprocessing for high accuracy classification."

"A Transformer-based deep learning model for advanced emotion detection in tweets — combining powerful GloVe embeddings with custom text preprocessing for high accuracy classification."

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Trying to convert the GraphNet repo into the PyTorch Lightning + Hydra template.

Dự án thực hiện phân loại văn bản báo chí từ bộ dữ liệu VNTC bằng cách sử dụng kiến trúc Transformer Encoder. Quy trình bao gồm tiền xử lý tiếng Việt với Pyvi, mã hóa văn bản qua mô hình pre-trained của VinAI.

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This repository implements an end to end encoder transformer model completely from scratch in C

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The notebook given under this repository contains various models for multilabel text classification. Throughout this study, the following models have been utilized. Namely, LSTM, BiLSTM, CONV-BiLSTM, CONV-BiLSTM with Multi-Head Attention, Transformer Architecture and Encoder only Transformer.

Image Captioning using CNN (for image features extraction) and Transformer (for language modelling) in Nepali Language.

Here, i trained BERT for sequence classification on custom dataset

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change the encoder of transformerCPI

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Transformer_Encoder_Classifier

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