NeuScraper

Source code for our ACL'24 paper :
Cleaner Pretraining Corpus Curation with Neural Web Scraping

If you find this work useful, please cite our paper and give us a shining star.

Quick Start

1️⃣ Clone from git

git clone https://github.com/OpenMatch/NeuScraper
cd NeuScraper

2️⃣ Data

ClueWeb22 is the newest in the Lemur Project's ClueWeb line of datasets that support research on information retrieval, natural language processing and related human language technologies.

The ClueWeb22 datasets are distributed by Carnegie Mellon University for research purposes only. A dataset may be obtained by signing a data license agreement with Carnegie Mellon University. For details on how to get it, please click the following link:

https://www.lemurproject.org/clueweb22/obtain.php

3️⃣ Environment

Install the torch first :

pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html

Install other packages :

pip install -r requirements.txt

Deploy NeuScraper on Your GPU Server

1️⃣ Open the deployment directory

cd NeuScraper/app

2️⃣ Fill in the model path in app

args.model_path = "/path/to/your/model"

3️⃣ Deploy NeuScraper

uvicorn app:app --reload --host 0.0.0.0 --port 1688

4️⃣ Use it like:

import requests

port = 'http://0.0.0.0:1688/predict/'
data = {
    'url': 'https://blog.christianperone.com/2023/06/appreciating-llms-data-pipelines/'
}

response = requests.post(port, json=data)

if response.status_code == 200:
    print('Success!')
    print(response.json())
else:
    print('Failed to call API')
    print('Status code:', response.status_code)
    print('Response:', response.text)

Reproduction

1️⃣ Download checkpoint for NeuScraper

git lfs install
git clone https://huggingface.co/OpenMatch/neuscraper-v1-clueweb

2️⃣ Preprocess the test data, we use the en0001-01 as our test set.

python src/build_test.py --path /path/to/clueweb22

3️⃣ Scraping with NeuScraper

bash scripts/inference.sh

4️⃣ Test on en0001-01

python src/eval/run_eval.py

Train NeuScraper from Scratch

Note: Training NeuScraper from scratch needs to be done on a server equipped with 8 NVIDIA A100-40G GPUs and SSDs

1️⃣ We need to preprocess the pages in Clueweb22:

python src/build_train.py --path /path/to/clueweb22

This command will place the processed data in data/train.
It need to slice some of them up and put them in data/val.

2️⃣ Run the following script to start training

bash scripts/train.sh

The training process will run for 30 epochs and take about 40 hours.

CommonCrawl WARC Support

1️⃣ Preprocess the pages in CommonCrawl

python src/warc/build.py --path /path/to/commoncrawl/warc

2️⃣ Scraping by NeuScraper

python scripts/commoncrawl.sh

3️⃣ Get Text

python src/warc/get_text.py

Citation

@inproceedings{xu2024cleaner,
  title={Cleaner Pretraining Corpus Curation with Neural Web Scraping},
  author={Xu, Zhipeng and Liu, Zhenghao and Yan, Yukun and Liu, Zhiyuan and Xiong, Chenyan and Yu, Ge},
  booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics},
  year={2024}
}

Contact Us

If you have questions, suggestions, and bug reports, please send a email to us, we will try our best to help you.

xuzhipeng@stumail.neu.edu.cn