This is the official repo of the paper MatchXML: An Efficient Text-label Matching Framework for Extreme Multi-label Text Classification

• Create a virtual environment

  # We recommend you to use Anaconda to create a conda environment 
  conda create --name matchxml python=3.8
  conda activate matchxml

• Install the required software:

  pip install -r requirements.txt

# eurlex-4k, wiki10-31k, amazoncat-31k, wiki-500k, amazon-670k, amazon-3m

• Download six XMC datasets from XR-Transformer

• Download our trained label embeddings from Google Drive and save them to xmc-base/{dataset}

• Download our static text features(static sentence embeddings + TF-IDF features) from Google Drive and save them to xmc-base/{dataset}/tfidf-attnxml, replace the original TF-IDF features.

# eurlex-4k, wiki10-31k, amazoncat-31k, wiki-500k, amazon-670k, amazon-3m

bash run.sh {dataset}

# eurlex-4k, wiki10-31k, amazoncat-31k, wiki-500k, amazon-670k, amazon-3m

bash ./label2vec_run/{dataset}.sh

python sentence_embedding.py

• Our pre-trained models can be downloaded from Google Drive

If you find this work useful in your research, please consider citing:

@article{ye2024matchxml,
  title={MatchXML: An Efficient Text-label Matching Framework for Extreme Multi-label Text Classification},
  author={Ye, Hui and Sunderraman, Rajshekhar and Ji, Shihao},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2024},
  publisher={IEEE}
}