transformer-encoder

[Preprint] "TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up", Yifan Jiang, Shiyu Chang, Zhangyang Wang

Pre-training of Deep Bidirectional Transformers for Language Understanding: pre-train TextCNN

The implementation of DeBERTa

Implementation of Transformer Model in Tensorflow

Seq2SeqSharp is a tensor based fast & flexible encoder-decoder deep neural network framework written by .NET (C#). It has many highlighted features, such as automatic differentiation, many different types of encoders/decoders(Transformer, LSTM, BiLSTM and so on), multi-GPUs supported and so on.

This repository contains PyTorch implementations of the models from the paper An Empirical Study MIME: MIMicking Emotions for Empathetic Response Generation.

Generating English Rock lyrics using BERT

Official Pytorch implementation of (Roles and Utilization of Attention Heads in Transformer-based Neural Language Models), ACL 2020

Universal Transforming Geometric Network for protein structure prediction.

French English Machine Translation. Natural language processing (NLP) transformer model from "Attention Is All You Need"

Contains pytorch implementation of Transformer and EvolvedTransformer architectures. WIP

Contextual embedding for text blobs.

Detection of MBTI-type personality with NLP and Deep Learning

Semantic Textual Similarity between two document

Code for the ACL 2019 paper "Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes"

Pytorch实现transformer编码器+attention的文本分类算法

PyTorch implementation of RealFormer: Transformer Likes Residual Attention

pretrained BERT model for cyber security domain, learned CyberSecurity Knowledge

Topic mediling using BERT and LDA combination.

use transformer as the decoder model for OCR

题目知识点预测标注。Question knowledge point prediction.

Code for the paper "NABU - Multilingual Graph-based Neural RDF Verbalizer"

Repository for the Homework 3 - Neural Networks and Deep Learning course @ UniPD. Second version. Transformer for text generation.

The repo is for the Heart Disease classification project using Transformer Encoders in PyTorch.

unbiased toxicity detection from comments

Quickly fine-tune language models for your downstream NLP tasks.

Text matching using several deep models.

Julia experimentation using sequence-based NLP models

Objective: Predicting whether the customer will return or not in the next month. Techniques used: XGBoost, logistic regression, attention based LSTM neural network, self attention based transformer neural network