Meta-classifier free negative sampling for extreme multilabel classification.
The code is adapted from the source codes of LightXML [1].
- tokenizers==0.7.0
- numpy==1.18.5
- pandas==1.0.4
- tqdm==4.46.1
- scipy==1.4.1
- transformers==2.11.0
- scikit_learn==0.23.2
- torch==1.5.1
- faiss-gpu
- apex
The datasets can be downloaded from the following links:
Please place the datasets in the data folder.
Run the following commands for training and evaluation using MIPS-s method on Amazon-670 and Wikipedia-500K:
python src/main.py --epoch 20 --dataset amazon670k --swa --batch 16 --max_len 128 --hidden_dim 400 --model_type mips --num_neg_mips 5 --nlist 818 --nprobe_eval 350
python src/main.py --epoch 10 --dataset wiki500k --swa --batch 32 --max_len 128 --hidden_dim 500 --model_type mips --num_neg_mips 5 --nlist 707 --nprobe_eval 256[1] Jiang, Ting, et al., Lightxml: Transformer with dynamic negative sampling for high-performance extreme multi-label text classification, AAAI, 2021.