Whether to use an inhouse split or not depends on the dataset

XikunZhang · XikunZhang · commit 803946bba327 · 2021-11-12T20:47:48.000-08:00
diff --git a/run_greaselm.sh b/run_greaselm.sh
@@ -36,7 +36,6 @@ fi
 
 max_seq_len=100
 ent_emb=tzw
-inhouse=false
 
 # Added for GreaseLM
 info_exchange=true
@@ -58,7 +57,7 @@ echo "******************************"
 save_dir_pref='runs'
 mkdir -p $save_dir_pref
 
-run_name=greaselm__ds_${dataset}__ih_${inhouse}__enc_${encoder}__k${k}__sd${seed}__iedim${ie_dim}__${dt}
+run_name=greaselm__ds_${dataset}__enc_${encoder}__k${k}__sd${seed}__iedim${ie_dim}__${dt}
 log=logs/train_${dataset}__${run_name}.log.txt
 
 ###### Training ######
@@ -68,7 +67,7 @@ python3 -u greaselm.py \
     --n_epochs $n_epochs --max_epochs_before_stop ${max_epochs_before_stop} \
     --save_dir ${save_dir_pref}/${dataset}/${run_name} \
     --run_name ${run_name} \
-    --ie_dim ${ie_dim} --info_exchange ${info_exchange} --ie_layer_num ${ie_layer_num} --resume_checkpoint ${resume_checkpoint} --resume_id ${resume_id} --sep_ie_layers ${sep_ie_layers} --random_ent_emb ${random_ent_emb} --ent_emb ${ent_emb//,/ } --lr_schedule ${lr_schedule} -ih ${inhouse} \
+    --ie_dim ${ie_dim} --info_exchange ${info_exchange} --ie_layer_num ${ie_layer_num} --resume_checkpoint ${resume_checkpoint} --resume_id ${resume_id} --sep_ie_layers ${sep_ie_layers} --random_ent_emb ${random_ent_emb} --ent_emb ${ent_emb//,/ } --lr_schedule ${lr_schedule} \
     $args \
 # > ${log} 2>&1 &
 # echo log: ${log}
diff --git a/run_greaselm__medqa_usmle.sh b/run_greaselm__medqa_usmle.sh
@@ -31,7 +31,6 @@ ie_dim=400
 
 max_seq_len=512
 ent_emb=ddb
-inhouse=false
 
 # Added for GreaseLM
 info_exchange=true
@@ -53,7 +52,7 @@ echo "******************************"
 save_dir_pref='runs'
 mkdir -p $save_dir_pref
 
-run_name=greaselm__ds_${dataset}__ih_${inhouse}__enc_sapbert__k${k}__sd${seed}__iedim${ie_dim}__unfrz${unfreeze_epoch}__${dt}
+run_name=greaselm__ds_${dataset}__enc_sapbert__k${k}__sd${seed}__iedim${ie_dim}__unfrz${unfreeze_epoch}__${dt}
 log=logs/train_${dataset}__${run_name}.log.txt
 
 ###### Training ######
@@ -63,7 +62,7 @@ python3 -u greaselm.py \
     --n_epochs $n_epochs --max_epochs_before_stop ${max_epochs_before_stop} \
     --save_dir ${save_dir_pref}/${dataset}/${run_name} \
     --run_name ${run_name} \
-    --ie_dim ${ie_dim} --info_exchange ${info_exchange} --ie_layer_num ${ie_layer_num} --resume_checkpoint ${resume_checkpoint} --resume_id ${resume_id} --sep_ie_layers ${sep_ie_layers} --random_ent_emb ${random_ent_emb} --ent_emb ${ent_emb//,/ } --lr_schedule ${lr_schedule} -ih ${inhouse} \
+    --ie_dim ${ie_dim} --info_exchange ${info_exchange} --ie_layer_num ${ie_layer_num} --resume_checkpoint ${resume_checkpoint} --resume_id ${resume_id} --sep_ie_layers ${sep_ie_layers} --random_ent_emb ${random_ent_emb} --ent_emb ${ent_emb//,/ } --lr_schedule ${lr_schedule} \
     --data_dir data \
 > ${log}
 # echo log: ${log}
