environment.txt
torch version
conda install pytorch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2 cudatoolkit=11.3.1 -c pytorch -c conda-forge
transformers
pip install transformers==3.4.0
first put InterMWP dataset in 'data/'
change PLM's vocab, the top two lines in vocab.text in 'chinese-bert-wwm' should be modified as:
[PAD]
[NUM]
...
run_bert2tree.py
CUDA_VISIBLE_DEVICES=0 python run_bert2tree.py --save_path model/finetune/bert2tree --save
Note: We have already proveid the trained retriever's generated promots, you can skip to 2.2
SoftmaxBCELoss:
CUDA_VISIBLE_DEVICES=0 python run_retriever_attn.py --save_path model/retriever/attn_ep[20] --n_epochs 20
top K prompts learning:
CUDA_VISIBLE_DEVICES=2 python run_bert2tree.py --save_path model/finetune/bert2tree_softmacbceloss_ep[20]_top3 --save --logic_path data/logic.json --prompt_text_path model/retriever/SoftmaxBCELoss_ep\[20\]/ --retriever_postfix 3
Then generate logic generator training data:
- GTS(BERT)
CUDA_VISIBLE_DEVICES=5 python evaluate_bert2tree.py --save_path model/finetune/bert2tree/ --save_data_path data/bert2tree --save - InterSolver
CUDA_VISIBLE_DEVICES=5 python evaluate_bert2tree.py --save_path model/finetune/bert2tree_softmacbceloss_ep\[20\]_top3 --logic_path data/logic.json --prompt_text_path model/retriever/SoftmaxBCELoss_ep\[20\] --retriever_postfix 3 --save_data_path data/bert2tree_bce_ep20_top3 --save
GTS(BERT)-[logic generator]
CUDA_VISIBLE_DEVICES=5 python run_logic_generator.py --data_path data/bert2tree/ --save_path model/logic_generator/bert2tree/ --save --test_while_valid
InterSolver-[logic generator]
CUDA_VISIBLE_DEVICES=5 python run_logic_generator.py --data_path data/bert2tree_bce_ep20_top3/ --save_path model/logic_generator/bert2tree_bce_ep20_top3/ --save --test_while_valid
CUDA_VISIBLE_DEVICES=2 python run_bert2tree_math23k.py --save_path model/finetune_math23k/bert2tree --save
Note: We have already proveid the trained retriever's generated promots in Math23K, you can skip to 3.3
CUDA_VISIBLE_DEVICES=1 python retriever_attn_output.py --save_path model/retriever/SoftmaxBCELoss_ep\[20\] --loss_function SoftmaxBCELoss
CUDA_VISIBLE_DEVICES=2 python run_bert2tree_math23k.py --save_path model/finetune_math23k/bert2tree_softmacbceloss_ep[20]_top3 --save --logic_path data/logic.json --retriever_postfix 3
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