WSDM Cup 2023 Source Code:

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• This repo contains the source code of our competition in WSDM Cup 2023: Pre-training for Web Search and Unbiased Learning for Web Search.
• In the Pre-training task, we implement all codes in both Pytorch and PaddlePaddle version (You can pretrain & finetune in anyone of these two frameworks.).
• In the Unbiased LTR task, we implement codes in Pytorch version.
• All checkpoints are available here: Download

Paper released

Please refer to our paper for details in this competition:

• Task1 Unbiased Learning to rank: Multi-Feature Integration for Perception-Dependent Examination-Bias Estimation
• Task2 Pretraining for web search: Pretraining De-Biased Language Model with Large-scale Click Logs for Document Ranking

Below is details for Pre-training task. For Unbiased LTR task, see README.md for details.

Quick Links

• Method Overview
• Training
• Ensemble learning
• Reproduce results on leaderboard
• Environment

Method Overview

• Pre-training BERT with MLM and CTR prediction loss (or multi-task CTR prediction loss).
• Finetuning BERT with pairwise ranking loss.
• Obtain prediction scores from different BERTs.
• Ensemble learning to combine BERT features and sparse features.

Details will be updated in the submission paper.

Training

• In all start.sh files in pytorch_pretrain (or paddle_pretrain), modify all data_root={Palce Your Data Root Path Here}/baidu_ultr as your file path.
• Set NPROC as your GPU number.

1) Pretraining

cd pytorch_pretrain/pretrain (or paddle_pretrain/pretrain)
sh start.sh

2) Finetuning

cd pytorch_pretrain/finetune (or paddle_pretrain/finetune)
sh start.sh

3) Inference for submission

cd pytorch_pretrain/submit (or paddle_pretrain/submit)
sh start.sh

Ensemble learning

We use lambdamart by lightgbm to ensemble different scores from the finetuned bert models.

Sparse features:

• query length
• document length
• query frequency
• number of hit words of query in document
• BM25 score
• TF-IDF score

BERT features:

1) Model details: Checkpoints Download Here

Index	Model Flag	Method	Pretrain step	Finetune step	DCG on leaderboard
1	large_group2_wwm_from_unw4625K	M1	1700K	5130	11.96214
2	large_group2_wwm_from_unw4625K	M1	1700K	5130	NAN
3	base_group2_wwm	M2	2150K	5130	~11.32363
4	large_group2_wwm_from_unw4625K	M1	590K	5130	11.94845
5	large_group2_wwm_from_unw4625K	M1	1700K	4180	NAN
6	large_group2_mt_pretrain	M3	1940K	5130	NAN

2) Method details

Method	Model Layers	Details
M1	24	WWM & CTR prediction as pretraining tasks
M2	12	WWM & CTR prediction as pretraining tasks
M3	24	WWM & Multi-task CTR prediction as pretraining tasks

The procedure contains two steps:

1. Cross validation on validation set to determine best parameters. See ./lambdamart/cross_validation.ipynb.
2. Generate the final scores based on the determined parameters in step 1. See ./lambdamart/run.ipynb.

Reproduce results on leaderboard

1) Convert Torch checkpoint to Paddle checkpoint.

• Install X2Paddle and onnxsim
• We use method one in this link. Using the following three commands:

python ./paddle_pretrain/convert/convert-onnx.py 
python -m onnxsim model.onnx model_sim.onnx
x2paddle --framework=onnx --model=model_sim.onnx --save_dir=./pd_model

It will output a folder named ./pd_model which contains x2paddle.py(Model definition in paddlepaddle) and model.pdparams (Trained parameters). Copy x2paddle.py to ./paddle_pretrain/review/x2paddle.py. We already generate a file there which converts a 24-layer model, you can directly use this file if your always use a 24-layer model.

2) Inference score for each bert model

• Modify data_root as your path in ./paddle_pretrain/review/start.sh and then run it by sh start.sh.
• It uses PaddlePaddle framework to inference the score of each query-document pair.

3) Ensemble learning

• We already inference scores of 6 different models. The Scores are all contained in ./lambdamart/features.
• Run all cells in ./lambdamart/run.ipynb. It will reproduce the scores of our final submission by ensembling all scores from different models, which is the same as ./lambdamart/features/final_result_submit.csv.

Environment

We opensource dockers for both pytorch and paddlepaddle to save your configuration time of environment.

Version	Key configuration
Pytorch	Python 3.6 torch1.8.0 transformers-4.18.0
PaddlePaddle	Python 3.9 Paddle2.4 cuda11.2-cudnn8.2
To be updated.

Contacts

• Xiangsheng Li: lixsh6@gmail.com.
• Xiaoshu Chen: xschenranker@gmail.com