AXCF-Pytorch

Aspect-aware eXplainable Collaborative Filtering

Requirements

torch==1.10.0
transformers==4.24.0
pandas==1.3.5
numpy==1.19.5
matplotlib==3.5.3
dgl==0.9.1
gensim

Experiment setting

NVIDIA GeForce RTX 3080 Ti 12GB
i9-11900F RAM 128GB
cuda 11.3 version

Datasets

SemEval

SemEval is a series of international natural language processing (NLP) research workshops whose mission is to advance the current state of the art in semantic analysis and to help create high-quality annotated datasets in a range of increasingly challenging problems in natural language semantics. Each year's workshop features a collection of shared tasks in which computational semantic analysis systems designed by different teams are presented and compared.

In this paper, use SemEval2014, SemEval2015, and SemEval2016.

Dataset	Train	Valid	Test
Sentence #Total	5,959	851	1,703
Aspect #Positive	3,050	422	744
#Negatvie	1,181	137	290
#Neutral	673	20	60
#Total	4,904	579	1,094

Yelp2018

Dataset	Users	Restaurant	Interaction	Sparsity
Yelp2018	25,369	45,882	1,205,628	99.90%

Docker

1.clone this repository

git clone https://github.com/ceo21ckim/AXCF-Pytorch.git
cd AXCF-Pytorch

2.build Dockerfile

docker build --tag [filename]:1.0 .

3.execute

# Docker version 2.0 or later.
docker run --itd --runtime=nvidia --name axcf -p 8888:8888 -v C:\Users\Name\:/workspace AXCF:1.0 /bin/bash

# Docker-ce 19.03 or later
docker run -itd --gpus all --name axcf -p 8888:8888 -v C:\Users\Name\:/workspace AXCF:1.0 /bin/bash

4.use jupyter notebook

docker exec -it axcf bash

jupyter notebook --ip=0.0.0.0 --port=8888 --allow-root

Execute

Aspect_Based_Sentiment_Analysis

python train.py --semeval_dir dataset/SemEval --yelp_dir dataset/Yelp2018 --fix_tfm 0 \
                --max_seq_length 512 --num_epochs 10 --batch_size 128 \
                --save_steps 100 --seed 42 --warmup_steps 0 \
                --model_name_or_path bert-base-uncased \
                --max_grad_norm 1.0 --device cuda

Recommender Systems

BERT

python bert_train.py --max_seq_length 128 --dr_rate 0.3 --hidden_dim 312 \
                     --model_name_or_path huawei-noah/TinyBERT_General_4L_312D \
                     --learning_rate 1e-5 --batch_size 32 \
                     --num_epochs 100 --device cuda

LSTM

python lstm_train.py --max_seq_length 128 --dr_rate 0.0 --hidden_dim 64 \
                     --num_layers 2 --bidirectional --learning_rate 1e-3 \
                     --batch_size 2048 --num_epochs 100

SS0mmy/AXCF-Pytorch