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hw2022s1
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| deepxml | 1 year ago | |
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| README.md | 1 year ago | |
| eval.py | 1 year ago | |
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| gensim-4.2.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl | 1 year ago | |
| logzero-1.7.0-py2.py3-none-any.whl | 1 year ago | |
| main.py | 1 year ago | |
| pip-requirements.txt | 1 year ago | |
| preprocess.py | 1 year ago | |
| ruamel.yaml-0.16.5-py2.py3-none-any.whl | 1 year ago | |
| ruamel.yaml.clib-0.2.7-cp37-cp37m-manylinux2014_aarch64.whl | 1 year ago | |
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README.md
CorNet
Correlation Networks for Extreme Multi-label Text Classification
Prerequisites
- python==3.6.3
- pytorch==1.2.0
- torchgpipe==0.0.5
- click==7.0
- ruamel.yaml==0.16.5
- numpy==1.16.2
- scipy==1.2.1
- scikit-learn==0.20.3
- gensim==3.7.2
- nltk==3.2.4
- tqdm==4.31.1
- joblib==0.13.2
- logzero==1.5.0
Datasets
Pretrained Word Embeddings in gensim format
Run
Preprocess (the EUR-Lex dataset is already tokenized in advance)
./scripts/preprocess_eurlex.sh
or (the other datasets need to be tokenized using NLTK)
./scripts/preprocess_others.sh
Train and evaluate
./scripts/run_models.sh
Baselines
The codes for the baseline models are adapted from the following repositories: XML-CNN, BERT, MeSHProbeNet, and AttentionXML.
