IPRLS
Code and data for the SIGIR'2021 paper "Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification"
Install / Use
/learn @bzGeng/IPRLSREADME
IPRLS code for Iterative Pruning with Regularization for Lifelong Sentiment Classification
requirements
- Python >=3.7
- Pytorch 1.2.0
- transformers
bert-base-uncased version BERT model need to be download from https://huggingface.co/bert-base-uncased , set it under path BERT/
You can run IPRLS with
$ bash experiment/run_IPRLS.sh
After completing the above process, you need to run following bash to obtain final results
$ bash experiment/eval_middle_results.sh
Run IPRLS with random task order
$ bash experiment/run_with_random_task_order.sh
To evaluate shuffle order results
$ bash experiment/eval_shuffle_middle_results.sh
Related Skills
node-connect
352.0kDiagnose OpenClaw node connection and pairing failures for Android, iOS, and macOS companion apps
frontend-design
111.1kCreate distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.
openai-whisper-api
352.0kTranscribe audio via OpenAI Audio Transcriptions API (Whisper).
qqbot-media
352.0kQQBot 富媒体收发能力。使用 <qqmedia> 标签,系统根据文件扩展名自动识别类型(图片/语音/视频/文件)。
View on GitHub55/100
Security Score
Audited on Oct 1, 2024
No findings
