xiangju2017

ASGCN

Code and preprocessed dataset for EMNLP 2019 paper titled "Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks"

bb

BERT-NER-Pytorch

Chinese NER(Named Entity Recognition) using BERT(Softmax, CRF, Span)

bert-qa

BERT for question answering starting with HotpotQA

BGAT

blog

boundary-master

CMU-MultimodalSDK

CMU MultimodalSDK is a machine learning platform for development of advanced multimodal models as well as easily accessing and processing multimodal datasets.

Convert-Bert-TF-checkpoint-to-Pytorch

Convert Bert TF-checkpoint to Pytorch

DeepIE

DeepIE: Deep Learning for Information Extraction

xiangju2017/ASGCN

Code and preprocessed dataset for EMNLP 2019 paper titled "Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks"

xiangju2017/bb

xiangju2017/BERT-NER-Pytorch

Chinese NER(Named Entity Recognition) using BERT(Softmax, CRF, Span)

xiangju2017/bert-qa

BERT for question answering starting with HotpotQA

xiangju2017/BGAT

xiangju2017/blog

xiangju2017/boundary-master

xiangju2017/CMU-MultimodalSDK

CMU MultimodalSDK is a machine learning platform for development of advanced multimodal models as well as easily accessing and processing multimodal datasets.

xiangju2017/Convert-Bert-TF-checkpoint-to-Pytorch

Convert Bert TF-checkpoint to Pytorch

xiangju2017/DeepIE

DeepIE: Deep Learning for Information Extraction

xiangju2017/DialCoT

DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models

xiangju2017/EasyConnect

Mac M1 芯片 EasyConnect 安装文件

xiangju2017/Extending-Google-BERT-as-Question-and-Answering-model-and-Chatbot

BERT Question and Answer system meant and works well for only limited number of words summary like 1 to 2 paragraphs only. It can’t be able to answer well from understanding more than 10 pages of data. We can extend the BERT question and answer model to work as chatbot on large text. To accomplish the understanding of more than 10 pages of data, here we have used a specific approach of picking the data.

xiangju2017/Incontex-learning-NER-metaner-icl

Learning In-context Learning for Named Entity Recognition

xiangju2017/jieba

结巴中文分词

xiangju2017/Position-Bias-Mitigation-in-Emotion-Cause-Analysis

ACL2021:Position Bias Mitigation: A Knowledge-Aware Graph Model for EmotionCause Extraction

xiangju2017/Reinforcement-learning-with-tensorflow

Simple Reinforcement learning tutorials

xiangju2017/SECA-Sequence-tagging

xiangju2017/Seq2Seq4ATE

Codes for paper Exploring Sequence-to-Sequence Learning for Aspect Term Extraction.

xiangju2017/SeqTag-SECA

xiangju2017/SpanBERT

Code for using and evaluating SpanBERT.

xiangju2017/tensorflow-pointer-networks

Pointer Networks in Tensorflow

xiangju2017/torch-multi-head-attention

Multi-head attention in PyTorch

xiangju2017/transformers

🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.

xiangju2017/unsupervised-passage-reranking

Code, datasets, and checkpoints for the paper "Improving Passage Retrieval with Zero-Shot Question Generation (EMNLP 2022)"