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train.sh
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train.sh
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dataset=$1
python3 -u -m parser.train --tok_vocab ${dataset}/tok_vocab\
--lem_vocab ${dataset}/lem_vocab\
--pos_vocab ${dataset}/pos_vocab\
--ner_vocab ${dataset}/ner_vocab\
--concept_vocab ${dataset}/concept_vocab\
--predictable_concept_vocab ${dataset}/predictable_concept_vocab\
--rel_vocab ${dataset}/rel_vocab\
--word_char_vocab ${dataset}/word_char_vocab\
--concept_char_vocab ${dataset}/concept_char_vocab\
--train_data ${dataset}/train.txt.features.preproc \
--dev_data ${dataset}/dev.txt.features.preproc \
--with_bert \
--bert_path ../bert-base-cased \
--word_char_dim 32\
--word_dim 300\
--pos_dim 32\
--ner_dim 16\
--concept_char_dim 32\
--concept_dim 300 \
--rel_dim 100 \
--cnn_filter 3 256\
--char2word_dim 128\
--char2concept_dim 128\
--embed_dim 512\
--ff_embed_dim 1024\
--num_heads 8\
--snt_layers 4\
--graph_layers 2\
--inference_layers 4\
--dropout 0.2\
--unk_rate 0.33\
--epochs 100000\
--train_batch_size 4444\
--dev_batch_size 4444 \
--lr_scale 1. \
--warmup_steps 2000\
--print_every 100 \
--eval_every 1000 \
--batches_per_update 4 \
--ckpt ckpt\
--world_size 2\
--gpus 2\
--MASTER_ADDR localhost\
--MASTER_PORT 29505\
--start_rank 0