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AttentionXML

AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification

Requirements

  • python==3.7.4
  • click==7.0
  • ruamel.yaml==0.16.5
  • numpy==1.16.2
  • scipy==1.3.1
  • scikit-learn==0.21.2
  • gensim==3.4.0
  • torch==1.0.1
  • nltk==3.4
  • tqdm==4.31.1
  • joblib==0.13.2
  • logzero==1.5.0

Datasets

Download the GloVe embedding (840B,300d) and convert it to gensim format (which can be loaded by gensim.models.KeyedVectors.load).

We also provide a converted GloVe embedding at here.

XML Experiments

XML experiments in paper can be run directly such as:

./scripts/run_eurlex.sh

Preprocess

Run preprocess.py for train and test datasets with tokenized texts as follows:

python preprocess.py \
--text-path data/EUR-Lex/train_texts.txt \
--label-path data/EUR-Lex/train_labels.txt \
--vocab-path data/EUR-Lex/vocab.npy \
--emb-path data/EUR-Lex/emb_init.npy \
--w2v-model data/glove.840B.300d.gensim

python preprocess.py \
--text-path data/EUR-Lex/test_texts.txt \
--label-path data/EUR-Lex/test_labels.txt \
--vocab-path data/EUR-Lex/vocab.npy

Or run preprocss.py including tokenizing the raw texts by NLTK as follows:

python preprocess.py \
--text-path data/Wiki10-31K/train_raw_texts.txt \
--tokenized-path data/Wiki10-31K/train_texts.txt \
--label-path data/Wiki10-31K/train_labels.txt \
--vocab-path data/Wiki10-31K/vocab.npy \
--emb-path data/Wiki10-31K/emb_init.npy \
--w2v-model data/glove.840B.300d.gensim

python preprocess.py \
--text-path data/Wiki10-31K/test_raw_texts.txt \
--tokenized-path data/Wiki10-31K/test_texts.txt \
--label-path data/Wiki10-31K/test_labels.txt \
--vocab-path data/Wiki10-31K/vocab.npy

Train and Predict

Train and predict as follows:

python main.py --data-cnf configure/datasets/EUR-Lex.yaml --model-cnf configure/models/AttentionXML-EUR-Lex.yaml

Or do prediction only with option "--mode eval".

Ensemble

Train and predict with an ensemble:

python main.py --data-cnf configure/datasets/Wiki-500K.yaml --model-cnf configure/models/FastAttentionXML-Wiki-500K.yaml -t 0
python main.py --data-cnf configure/datasets/Wiki-500K.yaml --model-cnf configure/models/FastAttentionXML-Wiki-500K.yaml -t 1
python main.py --data-cnf configure/datasets/Wiki-500K.yaml --model-cnf configure/models/FastAttentionXML-Wiki-500K.yaml -t 2
python ensemble.py -p results/FastAttentionXML-Wiki-500K -t 3

Evaluation

python evaluation.py --results results/AttentionXML-EUR-Lex-labels.npy --targets data/EUR-Lex/test_labels.npy

Or get propensity scored metrics together:

python evaluation.py \
--results results/FastAttentionXML-Amazon-670K-labels.npy \
--targets data/Amazon-670K/test_labels.npy \
--train-labels data/Amazon-670K/train_labels.npy \
-a 0.6 \
-b 2.6

Reference

You et al., AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification, NeurIPS 2019

Declaration

It is free for non-commercial use. For commercial use, please contact Mr. Ronghi You and Prof. Shanfeng Zhu (zhusf@fudan.edu.cn).