Jingju baseline - A baseline model of our project of Beijing opera script generation

Related tags

Deep Learningjingju_baseline
Overview

Jingju Baseline

It is a baseline of our project about Beijing opera script generation. Our baseline model is based on gpt2-chinese-ancient which is pretrained with 1.5GB literary Chinese.Please refer to our paper for details.

Directory Annotation

jingju_baseline/
	|-- finetuning.py 	#the finetuning script
	|-- jingju_test.py 	#test script
	|-- preprocess.py 	#data preprocess script
	|-- config/ 		#model configuration files 
	|-- corpora/ 		#corpora files
	|-- models/ 		# vocab file, model checkpoints and some necessary files
	|-- scripts/ 		# several functional scripts
	|-- test/ 		#test files
	|-- uer/ 		#files from UER-py

Environment Preparation

Our baseline model is fineturned with a pretraining framework UER-py. Refer to the part for environment requirements.

Finetuning

  1. Data preprocess
python3 preprocess.py --corpus_path corpora/jingju_train.txt\
   	  --vocab_path models/vocab.txt \
   	  --tokenizer bert \
   	  --dataset_path corpora/jingju_train.pt \
   	  --processes_num 32 --seq_length 1024 --target lm
python3 preprocess.py --corpus_path corpora/jingju_dev.txt\
   	  --vocab_path models/vocab.txt \
   	  --tokenizer bert \
   	  --dataset_path corpora/jingju_dev.pt \
   	  --processes_num 32 --seq_length 1024 --target lm
  1. Finetuning
export CUDA_VISIBLE_DEVICES=0
nohup python3 -u finetuning.py --dataset_path corpora/jingju_train.pt\
		 --devset_path corpora/jingju_dev.pt\
		 --vocab_path models/vocab.txt \
		 --config_path config/jingju_config.json \
		 --output_model_path models/finetuned_model.bin\
		 --pretrained_model_path models/uer-ancient-chinese.bin\
		 --world_size 1 --gpu_ranks 0  \
		 --total_steps 100000 --save_checkpoint_steps 50000\
		 --report_steps 1000 --learning_rate 5e-5\
		 --batch_size 5 --accumulation_steps 4 \
		 --embedding word_pos  --fp16 --fp16_opt_level O1 \
		 --remove_embedding_layernorm --encoder transformer \
		 --mask causal --layernorm_positioning pre \
		 --target lm --tie_weights > fineturning.log 2>&1 &

Refer to here for function of every argument.

Specificly, you may change environment variable CUDA_VISIBLE_DEVICES and --world_size paired with --gpu_ranks option for multi-GPU training. To enable --fp16 coordinated with --fp16_opt_level needs apex.

Test

You can finetuning by yourself with instructions above, or download the checkpoint(extracting code: q0yn) to directory ./models Then run as follows:

python3 preprocess.py --corpus_path corpora/jingju_test.txt\
		  --vocab_path models/vocab.txt \
		  --tokenizer bert \
		  --dataset_path corpora/jingju_test.pt \
		  --processes_num 32 --seq_length 1024 --target lm
nohup python3 -u jingju_test.py --load_model_path models/finetuned_model.bin-100000 \
		--vocab_path models/vocab.txt \
		--beginning_path test/jingju_beginning.txt  \
		--reference_path test/jingju_reference.txt \
		--prediction_path test/jingju_candidates.txt \
		--test_path test/jingju_beginning.txt \
		--testset_path datasets/jingju_test.pt \
		--config_path config/jingju_config.json \
		--seq_length 1024 --embedding word_pos \
		--remove_embedding_layernorm \
		--encoder transformer --mask causal \
		--layernorm_positioning pre --target lm \
		--tie_weights > test_candidate_generation.log 2>&1 &

The automatic mertics(i.e., F1, Perplexity, BLEU and Distinct) will be displayed on stdout.

Generation

nohup python3 -u scripts/generate_lm.py \
		--load_model_path models/finetuned_model.bin-100000 \
		--vocab_path models/vocab.txt \
		--test_path test/beginning.txt \
		--prediction_path test/generation.txt \
		--config_path config/jingju_config.json \
		--seq_length 1024 --embedding word_pos \
		--remove_embedding_layernorm --encoder transformer \
		--mask causal --layernorm_positioning pre \
		--target lm --tie_weights > generation_log.log 2>&1 &

Given the beginning, the model will generates script corresponding with it. The generate_lm.py script only generates sequence no longer than 1024. If you want longer script, replace scripts/generate_lm.py with scripts/long_generate_lm.py and revise --seq_length to the length you desire. Note that the generation procedure employs auto-regressive fashion, so generating long sequence is a time-consuming process.

Citation

@article{zhao2019uer,
  title={UER: An Open-Source Toolkit for Pre-training Models},
  author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
  journal={EMNLP-IJCNLP 2019},
  pages={241},
  year={2019}
}
@article{radford2019language,
  title={Language Models are Unsupervised Multitask Learners},
  author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
  year={2019}
}
Owner
midon
master from School of Informatics,Xiamen University
midon
GitHub Repository
Object detection evaluation metrics using Python.

Object detection evaluation metrics using Python.

Louis Facun 2 Sep 06, 2022
Trafffic prediction analysis using hybrid models - Machine Learning

Hybrid Machine learning Model Clone the Repository Create a new Directory as assests and download the model from the below link Model Link To Start th

1 Feb 08, 2022
library for nonlinear optimization, wrapping many algorithms for global and local, constrained or unconstrained, optimization

NLopt is a library for nonlinear local and global optimization, for functions with and without gradient information. It is designed as a simple, unifi

Steven G. Johnson 1.4k Dec 25, 2022
Code for paper PairRE: Knowledge Graph Embeddings via Paired Relation Vectors.

PairRE Code for paper PairRE: Knowledge Graph Embeddings via Paired Relation Vectors. This implementation of PairRE for Open Graph Benchmak datasets (

Alipay 65 Dec 19, 2022
Galaxy images labelled by morphology (shape). Aimed at ML development and teaching

Galaxy images labelled by morphology (shape). Aimed at ML debugging and teaching.

Mike Walmsley 14 Nov 28, 2022
Instance Semantic Segmentation List

Instance Semantic Segmentation List This repository contains lists of state-or-art instance semantic segmentation works. Papers and resources are list

bighead 87 Mar 06, 2022
Graduation Project

Gesture-Detection-and-Depth-Estimation This is my graduation project. (1) In this project, I use the YOLOv3 object detection model to detect gesture i

ChaosAT 1 Nov 23, 2021
Machine Learning Time-Series Platform

cesium: Open-Source Platform for Time Series Inference Summary cesium is an open source library that allows users to: extract features from raw time s

632 Dec 26, 2022
An example of Scatterbrain implementation (combining local attention and Performer)

An example of Scatterbrain implementation (combining local attention and Performer)

HazyResearch 97 Jan 02, 2023
Toward Multimodal Image-to-Image Translation

BicycleGAN Project Page | Paper | Video Pytorch implementation for multimodal image-to-image translation. For example, given the same night image, our

Jun-Yan Zhu 1.4k Dec 22, 2022
AI4Good project for detecting waste in the environment

Detect waste AI4Good project for detecting waste in environment. www.detectwaste.ml. Our latest results were published in Waste Management journal in

108 Dec 25, 2022
Sketch-Based 3D Exploration with Stacked Generative Adversarial Networks

pix2vox [Demonstration video] Sketch-Based 3D Exploration with Stacked Generative Adversarial Networks. Generated samples Single-category generation M

Takumi Moriya 232 Nov 14, 2022
Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility ICCV2021

Vis2Mesh This is the offical repository of the paper: Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Lear

71 Dec 25, 2022
Count the MACs / FLOPs of your PyTorch model.

THOP: PyTorch-OpCounter How to install pip install thop (now continously intergrated on Github actions) OR pip install --upgrade git+https://github.co

Ligeng Zhu 3.9k Dec 29, 2022
Code for our ICCV 2021 Paper "OadTR: Online Action Detection with Transformers".

Code for our ICCV 2021 Paper "OadTR: Online Action Detection with Transformers".

66 Dec 15, 2022
style mixing for animation face

An implementation of StyleGAN on Animation dataset. Install git clone https://github.com/MorvanZhou/anime-StyleGAN cd anime-StyleGAN pip install -r re

Morvan 46 Nov 30, 2022
For IBM Quantum Challenge Africa 2021, 9 September (07:00 UTC) - 20 September (23:00 UTC).

IBM Quantum Challenge Africa 2021 To ensure Africa is able to apply quantum computing to solve problems relevant to the continent, the IBM Research La

Qiskit Community 48 Dec 25, 2022
Code to reproduce results from the paper "AmbientGAN: Generative models from lossy measurements"

AmbientGAN: Generative models from lossy measurements This repository provides code to reproduce results from the paper AmbientGAN: Generative models

Ashish Bora 87 Oct 19, 2022
Official PyTorch implementation of the NeurIPS 2021 paper StyleGAN3

Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation of the NeurIPS 2021 paper Alias-Free Generative Adversarial Net

Eugenio Herrera 92 Nov 18, 2022
A framework for analyzing computer vision models with simulated data

3DB: A framework for analyzing computer vision models with simulated data Paper Quickstart guide Blog post Installation Follow instructions on: https:

3DB 112 Jan 01, 2023