中文实体关系抽取，pytorch，bilstm+attention

Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs

序列化标注工具，基于PyTorch实现BLSTM-CNN-CRF模型，CoNLL 2003 English NER测试集F1值为91.10%（word and char feature）。

A PyTorch implementation of the BI-LSTM-CRF model.

基于知识库的中文问答系统（biLSTM）

albert + lstm + crf实体识别，pytorch实现。识别的主要实体是人名、地名、机构名和时间。albert + lstm + crf (named entity recognition)

Source codes and corpora of paper "Iterated Dilated Convolutions for Chinese Word Segmentation"

reference tensorflow code for named entity tagging

Multivariate Time Series Prediction using Keras (CNN BiLSTM Attention)

Residual and bidirectional LSTM for epileptic seizure detection

reference pytorch code for named entity tagging

biLSTM/CNN based deep learning framework for Question Answer Selection.

Compare six baseline deep learning models on TrecQA

CNN+BiLSTM 기반 한국어 개체명 인식기입니다

:computer: 英文命名实体识别(NER)的研究

pytorch implementation of JDCNet, singing voice detection and classification network

Extract features and detect anomalies in industrial machinery vibration data using a biLSTM autoencoder

Ensemble RNN based neural network for ECG anomaly detection

In this repository you will find an end-to-end model for text generation by implementing a Bi-LSTM-LSTM based model with PyTorch's LSTMCells.

A clean and structured implementation of the RNN family with wandb and pytorch-lightning

Modified version of RusStress (https://github.com/MashaPo/russtress) — python package for placing stress in Russian text using RNN (BiLSTM) and the "Grammatical Dictionary" by A. A. Zaliznyak (from http://odict.ru/).

✨ Fake news classification using source adaptive framework - BE Project 🎓The repository contains Detailed Documentation of the project, Classification pipeline, Architecture, System Interface Design, Tech stack used.

Here I sort out some small projects I did in the process of learning NLP.

Audio-driven facial animation generator with BiLSTM used for transcribing the speech and web interface displaying the avatar and the animation

pytorch implementation of various models for snli and mnli task

⚡ 基于PyTorch的多种模型文本分类框架

Distilling Task-Specific Knowledge from Teacher Model into BiLSTM

Use Bi-LSTM neural network to classify Chinese text sentiment, including eight categories (like, disgust, happiness, sadness, anger, surprise, fear, none)

Benchmarking various Deep Learning models such as BERT, ALBERT, BiLSTMs on the task of sentence entailment using two datasets - MultiNLI and SNLI.

State of the art Chinese Word Segmentation with Bi-LSTMs