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infCheXbert_simple_integration.txt
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lr= 2e-5
dropout rate: 0.1
Batch size: 16
training time: 6763 min
The number of training instances: 99371
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 100, Avg loss: 0.975
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 200, Avg loss: 0.677
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 300, Avg loss: 0.555
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 400, Avg loss: 0.441
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 500, Avg loss: 0.368
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 600, Avg loss: 0.269
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 700, Avg loss: 0.310
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 800, Avg loss: 0.237
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 900, Avg loss: 0.247
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 1000, Avg loss: 0.278
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 1100, Avg loss: 0.235
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 1200, Avg loss: 0.302
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 1300, Avg loss: 0.250
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 1400, Avg loss: 0.222
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 1500, Avg loss: 0.269
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 1600, Avg loss: 0.248
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 1700, Avg loss: 0.244
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 1800, Avg loss: 0.253
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 1900, Avg loss: 0.294
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 2000, Avg loss: 0.249
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 2100, Avg loss: 0.249
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 2200, Avg loss: 0.269
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 2300, Avg loss: 0.234
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 2400, Avg loss: 0.260
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 2500, Avg loss: 0.247
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 2600, Avg loss: 0.229
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 2700, Avg loss: 0.291
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 2800, Avg loss: 0.258
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 2900, Avg loss: 0.239
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 3000, Avg loss: 0.249
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 3100, Avg loss: 0.289
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 3200, Avg loss: 0.267
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 3300, Avg loss: 0.251
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 3400, Avg loss: 0.230
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 3500, Avg loss: 0.261
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 3600, Avg loss: 0.249
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 3700, Avg loss: 0.258
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 3800, Avg loss: 0.223
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 3900, Avg loss: 0.243
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 4000, Avg loss: 0.229
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 4100, Avg loss: 0.224
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 4200, Avg loss: 0.206
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 4300, Avg loss: 0.239
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 4400, Avg loss: 0.183
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 4500, Avg loss: 0.218
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 4600, Avg loss: 0.215
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 4700, Avg loss: 0.237
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 4800, Avg loss: 0.192
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 4900, Avg loss: 0.182
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 5000, Avg loss: 0.189
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 5100, Avg loss: 0.232
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 5200, Avg loss: 0.191
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 5300, Avg loss: 0.187
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 5400, Avg loss: 0.213
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 5500, Avg loss: 0.258
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 5600, Avg loss: 0.218
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 5700, Avg loss: 0.191
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 5800, Avg loss: 0.213
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 5900, Avg loss: 0.240
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 6000, Avg loss: 0.220
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 6100, Avg loss: 0.217
labelsname: label_Atelectasis, Epoch id: 1, Training steps: 6200, Avg loss: 0.188
Start evaluation on test dataset.
Loading sentences from ./datasets/CheXpert/impression/validation.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
labelsname: label_Atelectasis, Label_value 0: 0.985, 0.999, 0.992
labelsname: label_Atelectasis, Label_value 1: 0.774, 0.955, 0.855
model never predicts label value 2
labelsname: label_Atelectasis, Label_value 2: 0.000, 0.000, 0.000
model never predicts label value 3
labelsname: label_Atelectasis, Label_value 3: 0.000, 0.000, 0.000
Acc. (Correct/Total): 0.9338 (30931/33124)
Start evaluation on evaluation dataset.
Loading sentences from ./datasets/CheXpert/impression/test.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
The number of evaluation instances: 33124
Confusion matrix of {label_name}:
tensor([[24701, 305, 8, 70],
[ 12, 6219, 202, 1607],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0]])
Report precision, recall, and f1:
labelsname: label_Atelectasis, Label_value 0: 0.985, 1.000, 0.992
labelsname: label_Atelectasis, Label_value 1: 0.774, 0.953, 0.854
model never predicts label value 2
labelsname: label_Atelectasis, Label_value 2: 0.000, 0.000, 0.000
model never predicts label value 3
labelsname: label_Atelectasis, Label_value 3: 0.000, 0.000, 0.000
Acc. (Correct/Total): 0.9335 (30920/33124)
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 100, Avg loss: 0.211
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 200, Avg loss: 0.199
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 300, Avg loss: 0.228
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 400, Avg loss: 0.231
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 500, Avg loss: 0.230
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 600, Avg loss: 0.205
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 700, Avg loss: 0.207
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 800, Avg loss: 0.189
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 900, Avg loss: 0.193
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 1000, Avg loss: 0.208
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 1100, Avg loss: 0.186
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 1200, Avg loss: 0.248
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 1300, Avg loss: 0.187
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 1400, Avg loss: 0.181
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 1500, Avg loss: 0.227
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 1600, Avg loss: 0.224
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 1700, Avg loss: 0.178
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 1800, Avg loss: 0.201
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 1900, Avg loss: 0.211
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 2000, Avg loss: 0.175
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 2100, Avg loss: 0.208
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 2200, Avg loss: 0.181
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 2300, Avg loss: 0.162
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 2400, Avg loss: 0.195
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 2500, Avg loss: 0.199
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 2600, Avg loss: 0.177
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 2700, Avg loss: 0.227
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 2800, Avg loss: 0.177
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 2900, Avg loss: 0.164
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 3000, Avg loss: 0.158
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 3100, Avg loss: 0.200
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 3200, Avg loss: 0.194
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 3300, Avg loss: 0.185
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 3400, Avg loss: 0.175
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 3500, Avg loss: 0.188
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 3600, Avg loss: 0.199
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 3700, Avg loss: 0.209
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 3800, Avg loss: 0.182
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 3900, Avg loss: 0.190
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 4000, Avg loss: 0.176
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 4100, Avg loss: 0.178
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 4200, Avg loss: 0.197
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 4300, Avg loss: 0.185
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 4400, Avg loss: 0.150
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 4500, Avg loss: 0.209
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 4600, Avg loss: 0.169
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 4700, Avg loss: 0.209
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 4800, Avg loss: 0.153
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 4900, Avg loss: 0.153
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 5000, Avg loss: 0.153
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 5100, Avg loss: 0.208
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 5200, Avg loss: 0.125
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 5300, Avg loss: 0.152
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 5400, Avg loss: 0.182
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 5500, Avg loss: 0.224
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 5600, Avg loss: 0.174
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 5700, Avg loss: 0.139
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 5800, Avg loss: 0.176
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 5900, Avg loss: 0.198
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 6000, Avg loss: 0.170
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 6100, Avg loss: 0.172
labelsname: label_Atelectasis, Epoch id: 2, Training steps: 6200, Avg loss: 0.155
Start evaluation on test dataset.
Loading sentences from ./datasets/CheXpert/impression/validation.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
labelsname: label_Atelectasis, Label_value 0: 0.984, 0.999, 0.991
labelsname: label_Atelectasis, Label_value 1: 0.824, 0.931, 0.874
model never predicts label value 2
labelsname: label_Atelectasis, Label_value 2: 0.000, 0.000, 0.000
labelsname: label_Atelectasis, Label_value 3: 0.792, 0.299, 0.434
Acc. (Correct/Total): 0.9445 (31285/33124)
Start evaluation on evaluation dataset.
Loading sentences from ./datasets/CheXpert/impression/test.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
The number of evaluation instances: 33124
Confusion matrix of {label_name}:
tensor([[24699, 334, 8, 82],
[ 13, 6089, 197, 1068],
[ 0, 0, 0, 0],
[ 1, 101, 5, 527]])
Report precision, recall, and f1:
labelsname: label_Atelectasis, Label_value 0: 0.983, 0.999, 0.991
labelsname: label_Atelectasis, Label_value 1: 0.827, 0.933, 0.877
model never predicts label value 2
labelsname: label_Atelectasis, Label_value 2: 0.000, 0.000, 0.000
labelsname: label_Atelectasis, Label_value 3: 0.831, 0.314, 0.456
Acc. (Correct/Total): 0.9454 (31315/33124)
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 100, Avg loss: 0.182
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 200, Avg loss: 0.156
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 300, Avg loss: 0.196
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 400, Avg loss: 0.199
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 500, Avg loss: 0.194
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 600, Avg loss: 0.162
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 700, Avg loss: 0.192
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 800, Avg loss: 0.164
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 900, Avg loss: 0.152
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 1000, Avg loss: 0.169
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 1100, Avg loss: 0.161
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 1200, Avg loss: 0.213
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 1300, Avg loss: 0.153
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 1400, Avg loss: 0.151
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 1500, Avg loss: 0.207
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 1600, Avg loss: 0.181
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 1700, Avg loss: 0.166
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 1800, Avg loss: 0.169
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 1900, Avg loss: 0.203
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 2000, Avg loss: 0.153
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 2100, Avg loss: 0.178
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 2200, Avg loss: 0.163
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 2300, Avg loss: 0.132
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 2400, Avg loss: 0.158
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 2500, Avg loss: 0.181
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 2600, Avg loss: 0.160
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 2700, Avg loss: 0.206
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 2800, Avg loss: 0.168
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 2900, Avg loss: 0.149
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 3000, Avg loss: 0.161
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 3100, Avg loss: 0.174
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 3200, Avg loss: 0.189
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 3300, Avg loss: 0.179
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 3400, Avg loss: 0.168
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 3500, Avg loss: 0.178
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 3600, Avg loss: 0.185
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 3700, Avg loss: 0.192
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 3800, Avg loss: 0.179
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 3900, Avg loss: 0.177
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 4000, Avg loss: 0.176
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 4100, Avg loss: 0.169
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 4200, Avg loss: 0.162
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 4300, Avg loss: 0.173
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 4400, Avg loss: 0.141
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 4500, Avg loss: 0.183
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 4600, Avg loss: 0.166
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 4700, Avg loss: 0.193
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 4800, Avg loss: 0.144
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 4900, Avg loss: 0.147
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 5000, Avg loss: 0.142
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 5100, Avg loss: 0.182
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 5200, Avg loss: 0.115
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 5300, Avg loss: 0.156
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 5400, Avg loss: 0.181
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 5500, Avg loss: 0.204
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 5600, Avg loss: 0.171
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 5700, Avg loss: 0.138
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 5800, Avg loss: 0.166
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 5900, Avg loss: 0.184
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 6000, Avg loss: 0.159
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 6100, Avg loss: 0.159
labelsname: label_Atelectasis, Epoch id: 3, Training steps: 6200, Avg loss: 0.143
Start evaluation on test dataset.
Loading sentences from ./datasets/CheXpert/impression/validation.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
labelsname: label_Atelectasis, Label_value 0: 0.983, 0.999, 0.991
labelsname: label_Atelectasis, Label_value 1: 0.836, 0.928, 0.880
model never predicts label value 2
labelsname: label_Atelectasis, Label_value 2: 0.000, 0.000, 0.000
labelsname: label_Atelectasis, Label_value 3: 0.790, 0.360, 0.494
Acc. (Correct/Total): 0.9467 (31358/33124)
Start evaluation on evaluation dataset.
Loading sentences from ./datasets/CheXpert/impression/test.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
The number of evaluation instances: 33124
Confusion matrix of {label_name}:
tensor([[24695, 318, 9, 95],
[ 14, 6041, 201, 963],
[ 0, 0, 0, 0],
[ 4, 165, 0, 619]])
Report precision, recall, and f1:
labelsname: label_Atelectasis, Label_value 0: 0.983, 0.999, 0.991
labelsname: label_Atelectasis, Label_value 1: 0.837, 0.926, 0.879
model never predicts label value 2
labelsname: label_Atelectasis, Label_value 2: 0.000, 0.000, 0.000
labelsname: label_Atelectasis, Label_value 3: 0.786, 0.369, 0.502
Acc. (Correct/Total): 0.9466 (31355/33124)
Final evaluation on the evaluation dataset.
Loading sentences from ./datasets/CheXpert/impression/test.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
The number of evaluation instances: 33124
Confusion matrix of {label_name}:
tensor([[24695, 318, 9, 95],
[ 14, 6041, 201, 963],
[ 0, 0, 0, 0],
[ 4, 165, 0, 619]])
Report precision, recall, and f1:
labelsname: label_Atelectasis, Label_value 0: 0.983, 0.999, 0.991
labelsname: label_Atelectasis, Label_value 1: 0.837, 0.926, 0.879
model never predicts label value 2
labelsname: label_Atelectasis, Label_value 2: 0.000, 0.000, 0.000
labelsname: label_Atelectasis, Label_value 3: 0.786, 0.369, 0.502
Acc. (Correct/Total): 0.9466 (31355/33124)
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 100, Avg loss: 0.985
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 200, Avg loss: 0.728
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 300, Avg loss: 0.678
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 400, Avg loss: 0.609
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 500, Avg loss: 0.630
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 600, Avg loss: 0.588
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 700, Avg loss: 0.485
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 800, Avg loss: 0.475
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 900, Avg loss: 0.481
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 1000, Avg loss: 0.441
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 1100, Avg loss: 0.368
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 1200, Avg loss: 0.340
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 1300, Avg loss: 0.406
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 1400, Avg loss: 0.359
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 1500, Avg loss: 0.372
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 1600, Avg loss: 0.326
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 1700, Avg loss: 0.335
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 1800, Avg loss: 0.376
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 1900, Avg loss: 0.425
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 2000, Avg loss: 0.287
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 2100, Avg loss: 0.339
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 2200, Avg loss: 0.352
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 2300, Avg loss: 0.292
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 2400, Avg loss: 0.305
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 2500, Avg loss: 0.337
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 2600, Avg loss: 0.271
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 2700, Avg loss: 0.275
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 2800, Avg loss: 0.256
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 2900, Avg loss: 0.219
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 3000, Avg loss: 0.257
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 3100, Avg loss: 0.250
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 3200, Avg loss: 0.245
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 3300, Avg loss: 0.283
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 3400, Avg loss: 0.248
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 3500, Avg loss: 0.235
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 3600, Avg loss: 0.232
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 3700, Avg loss: 0.203
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 3800, Avg loss: 0.219
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 3900, Avg loss: 0.215
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 4000, Avg loss: 0.268
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 4100, Avg loss: 0.235
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 4200, Avg loss: 0.221
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 4300, Avg loss: 0.217
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 4400, Avg loss: 0.208
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 4500, Avg loss: 0.209
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 4600, Avg loss: 0.235
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 4700, Avg loss: 0.232
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 4800, Avg loss: 0.236
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 4900, Avg loss: 0.215
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 5000, Avg loss: 0.196
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 5100, Avg loss: 0.298
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 5200, Avg loss: 0.194
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 5300, Avg loss: 0.202
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 5400, Avg loss: 0.204
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 5500, Avg loss: 0.178
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 5600, Avg loss: 0.229
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 5700, Avg loss: 0.203
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 5800, Avg loss: 0.234
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 5900, Avg loss: 0.196
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 6000, Avg loss: 0.207
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 6100, Avg loss: 0.230
labelsname: label_Cardiomegaly, Epoch id: 1, Training steps: 6200, Avg loss: 0.192
Start evaluation on test dataset.
Loading sentences from ./datasets/CheXpert/impression/validation.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
labelsname: label_Cardiomegaly, Label_value 0: 0.971, 0.987, 0.979
labelsname: label_Cardiomegaly, Label_value 1: 0.958, 0.827, 0.888
labelsname: label_Cardiomegaly, Label_value 2: 0.735, 0.927, 0.820
labelsname: label_Cardiomegaly, Label_value 3: 0.677, 0.576, 0.623
Acc. (Correct/Total): 0.9445 (31285/33124)
Start evaluation on evaluation dataset.
Loading sentences from ./datasets/CheXpert/impression/test.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
The number of evaluation instances: 33124
Confusion matrix of {label_name}:
tensor([[23895, 430, 62, 233],
[ 48, 4990, 71, 86],
[ 196, 502, 1933, 21],
[ 16, 179, 23, 439]])
Report precision, recall, and f1:
labelsname: label_Cardiomegaly, Label_value 0: 0.971, 0.989, 0.980
labelsname: label_Cardiomegaly, Label_value 1: 0.961, 0.818, 0.883
labelsname: label_Cardiomegaly, Label_value 2: 0.729, 0.925, 0.815
labelsname: label_Cardiomegaly, Label_value 3: 0.668, 0.564, 0.611
Acc. (Correct/Total): 0.9436 (31257/33124)
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 100, Avg loss: 0.282
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 200, Avg loss: 0.188
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 300, Avg loss: 0.225
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 400, Avg loss: 0.206
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 500, Avg loss: 0.229
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 600, Avg loss: 0.216
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 700, Avg loss: 0.206
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 800, Avg loss: 0.190
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 900, Avg loss: 0.218
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 1000, Avg loss: 0.215
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 1100, Avg loss: 0.218
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 1200, Avg loss: 0.185
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 1300, Avg loss: 0.193
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 1400, Avg loss: 0.180
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 1500, Avg loss: 0.211
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 1600, Avg loss: 0.165
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 1700, Avg loss: 0.203
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 1800, Avg loss: 0.239
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 1900, Avg loss: 0.208
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 2000, Avg loss: 0.147
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 2100, Avg loss: 0.231
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 2200, Avg loss: 0.218
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 2300, Avg loss: 0.192
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 2400, Avg loss: 0.181
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 2500, Avg loss: 0.226
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 2600, Avg loss: 0.179
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 2700, Avg loss: 0.177
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 2800, Avg loss: 0.197
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 2900, Avg loss: 0.178
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 3000, Avg loss: 0.181
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 3100, Avg loss: 0.189
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 3200, Avg loss: 0.201
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 3300, Avg loss: 0.219
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 3400, Avg loss: 0.207
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 3500, Avg loss: 0.190
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 3600, Avg loss: 0.190
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 3700, Avg loss: 0.181
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 3800, Avg loss: 0.183
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 3900, Avg loss: 0.167
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 4000, Avg loss: 0.222
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 4100, Avg loss: 0.202
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 4200, Avg loss: 0.202
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 4300, Avg loss: 0.197
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 4400, Avg loss: 0.190
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 4500, Avg loss: 0.187
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 4600, Avg loss: 0.224
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 4700, Avg loss: 0.188
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 4800, Avg loss: 0.208
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 4900, Avg loss: 0.186
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 5000, Avg loss: 0.166
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 5100, Avg loss: 0.228
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 5200, Avg loss: 0.172
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 5300, Avg loss: 0.179
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 5400, Avg loss: 0.177
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 5500, Avg loss: 0.160
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 5600, Avg loss: 0.198
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 5700, Avg loss: 0.176
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 5800, Avg loss: 0.211
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 5900, Avg loss: 0.167
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 6000, Avg loss: 0.179
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 6100, Avg loss: 0.214
labelsname: label_Cardiomegaly, Epoch id: 2, Training steps: 6200, Avg loss: 0.181
Start evaluation on test dataset.
Loading sentences from ./datasets/CheXpert/impression/validation.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
labelsname: label_Cardiomegaly, Label_value 0: 0.983, 0.976, 0.980
labelsname: label_Cardiomegaly, Label_value 1: 0.891, 0.887, 0.889
labelsname: label_Cardiomegaly, Label_value 2: 0.730, 0.929, 0.818
labelsname: label_Cardiomegaly, Label_value 3: 0.891, 0.494, 0.636
Acc. (Correct/Total): 0.9454 (31314/33124)
Start evaluation on evaluation dataset.
Loading sentences from ./datasets/CheXpert/impression/test.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
The number of evaluation instances: 33124
Confusion matrix of {label_name}:
tensor([[23623, 223, 36, 151],
[ 303, 5363, 96, 229],
[ 218, 501, 1942, 23],
[ 11, 14, 15, 376]])
Report precision, recall, and f1:
labelsname: label_Cardiomegaly, Label_value 0: 0.983, 0.978, 0.980
labelsname: label_Cardiomegaly, Label_value 1: 0.895, 0.879, 0.887
labelsname: label_Cardiomegaly, Label_value 2: 0.724, 0.930, 0.814
labelsname: label_Cardiomegaly, Label_value 3: 0.904, 0.483, 0.629
Acc. (Correct/Total): 0.9451 (31304/33124)
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 100, Avg loss: 0.253
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 200, Avg loss: 0.161
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 300, Avg loss: 0.218
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 400, Avg loss: 0.179
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 500, Avg loss: 0.221
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 600, Avg loss: 0.196
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 700, Avg loss: 0.173
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 800, Avg loss: 0.157
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 900, Avg loss: 0.196
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 1000, Avg loss: 0.192
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 1100, Avg loss: 0.195
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 1200, Avg loss: 0.173
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 1300, Avg loss: 0.172
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 1400, Avg loss: 0.169
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 1500, Avg loss: 0.196
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 1600, Avg loss: 0.160
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 1700, Avg loss: 0.192
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 1800, Avg loss: 0.222
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 1900, Avg loss: 0.187
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 2000, Avg loss: 0.138
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 2100, Avg loss: 0.222
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 2200, Avg loss: 0.204
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 2300, Avg loss: 0.170
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 2400, Avg loss: 0.165
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 2500, Avg loss: 0.219
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 2600, Avg loss: 0.159
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 2700, Avg loss: 0.161
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 2800, Avg loss: 0.174
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 2900, Avg loss: 0.152
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 3000, Avg loss: 0.168
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 3100, Avg loss: 0.168
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 3200, Avg loss: 0.181
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 3300, Avg loss: 0.207
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 3400, Avg loss: 0.195
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 3500, Avg loss: 0.174
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 3600, Avg loss: 0.178
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 3700, Avg loss: 0.167
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 3800, Avg loss: 0.187
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 3900, Avg loss: 0.149
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 4000, Avg loss: 0.200
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 4100, Avg loss: 0.199
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 4200, Avg loss: 0.183
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 4300, Avg loss: 0.184
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 4400, Avg loss: 0.178
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 4500, Avg loss: 0.173
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 4600, Avg loss: 0.214
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 4700, Avg loss: 0.164
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 4800, Avg loss: 0.192
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 4900, Avg loss: 0.169
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 5000, Avg loss: 0.156
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 5100, Avg loss: 0.211
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 5200, Avg loss: 0.161
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 5300, Avg loss: 0.171
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 5400, Avg loss: 0.160
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 5500, Avg loss: 0.152
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 5600, Avg loss: 0.187
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 5700, Avg loss: 0.162
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 5800, Avg loss: 0.199
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 5900, Avg loss: 0.159
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 6000, Avg loss: 0.173
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 6100, Avg loss: 0.196
labelsname: label_Cardiomegaly, Epoch id: 3, Training steps: 6200, Avg loss: 0.170
Start evaluation on test dataset.
Loading sentences from ./datasets/CheXpert/impression/validation.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
labelsname: label_Cardiomegaly, Label_value 0: 0.983, 0.979, 0.981
labelsname: label_Cardiomegaly, Label_value 1: 0.911, 0.886, 0.898
labelsname: label_Cardiomegaly, Label_value 2: 0.737, 0.926, 0.821
labelsname: label_Cardiomegaly, Label_value 3: 0.870, 0.569, 0.688
Acc. (Correct/Total): 0.9489 (31430/33124)
Start evaluation on evaluation dataset.
Loading sentences from ./datasets/CheXpert/impression/test.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
The number of evaluation instances: 33124
Confusion matrix of {label_name}:
tensor([[23669, 234, 42, 160],
[ 261, 5353, 95, 171],
[ 207, 489, 1935, 22],
[ 18, 25, 17, 426]])
Report precision, recall, and f1:
labelsname: label_Cardiomegaly, Label_value 0: 0.982, 0.980, 0.981
labelsname: label_Cardiomegaly, Label_value 1: 0.910, 0.877, 0.894
labelsname: label_Cardiomegaly, Label_value 2: 0.729, 0.926, 0.816
labelsname: label_Cardiomegaly, Label_value 3: 0.877, 0.547, 0.674
Acc. (Correct/Total): 0.9474 (31383/33124)
Final evaluation on the evaluation dataset.
Loading sentences from ./datasets/CheXpert/impression/test.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
The number of evaluation instances: 33124
Confusion matrix of {label_name}:
tensor([[23669, 234, 42, 160],
[ 261, 5353, 95, 171],
[ 207, 489, 1935, 22],
[ 18, 25, 17, 426]])
Report precision, recall, and f1:
labelsname: label_Cardiomegaly, Label_value 0: 0.982, 0.980, 0.981
labelsname: label_Cardiomegaly, Label_value 1: 0.910, 0.877, 0.894
labelsname: label_Cardiomegaly, Label_value 2: 0.729, 0.926, 0.816
labelsname: label_Cardiomegaly, Label_value 3: 0.877, 0.547, 0.674
Acc. (Correct/Total): 0.9474 (31383/33124)
labelsname: label_Consolidation, Epoch id: 1, Training steps: 100, Avg loss: 0.730
labelsname: label_Consolidation, Epoch id: 1, Training steps: 200, Avg loss: 0.481
labelsname: label_Consolidation, Epoch id: 1, Training steps: 300, Avg loss: 0.421
labelsname: label_Consolidation, Epoch id: 1, Training steps: 400, Avg loss: 0.375
labelsname: label_Consolidation, Epoch id: 1, Training steps: 500, Avg loss: 0.435
labelsname: label_Consolidation, Epoch id: 1, Training steps: 600, Avg loss: 0.303
labelsname: label_Consolidation, Epoch id: 1, Training steps: 700, Avg loss: 0.210
labelsname: label_Consolidation, Epoch id: 1, Training steps: 800, Avg loss: 0.168
labelsname: label_Consolidation, Epoch id: 1, Training steps: 900, Avg loss: 0.172
labelsname: label_Consolidation, Epoch id: 1, Training steps: 1000, Avg loss: 0.132
labelsname: label_Consolidation, Epoch id: 1, Training steps: 1100, Avg loss: 0.148
labelsname: label_Consolidation, Epoch id: 1, Training steps: 1200, Avg loss: 0.153
labelsname: label_Consolidation, Epoch id: 1, Training steps: 1300, Avg loss: 0.148
labelsname: label_Consolidation, Epoch id: 1, Training steps: 1400, Avg loss: 0.157
labelsname: label_Consolidation, Epoch id: 1, Training steps: 1500, Avg loss: 0.169
labelsname: label_Consolidation, Epoch id: 1, Training steps: 1600, Avg loss: 0.115
labelsname: label_Consolidation, Epoch id: 1, Training steps: 1700, Avg loss: 0.125
labelsname: label_Consolidation, Epoch id: 1, Training steps: 1800, Avg loss: 0.161
labelsname: label_Consolidation, Epoch id: 1, Training steps: 1900, Avg loss: 0.114
labelsname: label_Consolidation, Epoch id: 1, Training steps: 2000, Avg loss: 0.122
labelsname: label_Consolidation, Epoch id: 1, Training steps: 2100, Avg loss: 0.091
labelsname: label_Consolidation, Epoch id: 1, Training steps: 2200, Avg loss: 0.127
labelsname: label_Consolidation, Epoch id: 1, Training steps: 2300, Avg loss: 0.132
labelsname: label_Consolidation, Epoch id: 1, Training steps: 2400, Avg loss: 0.107
labelsname: label_Consolidation, Epoch id: 1, Training steps: 2500, Avg loss: 0.148
labelsname: label_Consolidation, Epoch id: 1, Training steps: 2600, Avg loss: 0.122
labelsname: label_Consolidation, Epoch id: 1, Training steps: 2700, Avg loss: 0.124
labelsname: label_Consolidation, Epoch id: 1, Training steps: 2800, Avg loss: 0.118
labelsname: label_Consolidation, Epoch id: 1, Training steps: 2900, Avg loss: 0.123
labelsname: label_Consolidation, Epoch id: 1, Training steps: 3000, Avg loss: 0.120
labelsname: label_Consolidation, Epoch id: 1, Training steps: 3100, Avg loss: 0.120
labelsname: label_Consolidation, Epoch id: 1, Training steps: 3200, Avg loss: 0.141
labelsname: label_Consolidation, Epoch id: 1, Training steps: 3300, Avg loss: 0.124
labelsname: label_Consolidation, Epoch id: 1, Training steps: 3400, Avg loss: 0.109
labelsname: label_Consolidation, Epoch id: 1, Training steps: 3500, Avg loss: 0.118
labelsname: label_Consolidation, Epoch id: 1, Training steps: 3600, Avg loss: 0.138
labelsname: label_Consolidation, Epoch id: 1, Training steps: 3700, Avg loss: 0.127
labelsname: label_Consolidation, Epoch id: 1, Training steps: 3800, Avg loss: 0.124
labelsname: label_Consolidation, Epoch id: 1, Training steps: 3900, Avg loss: 0.122
labelsname: label_Consolidation, Epoch id: 1, Training steps: 4000, Avg loss: 0.106
labelsname: label_Consolidation, Epoch id: 1, Training steps: 4100, Avg loss: 0.127
labelsname: label_Consolidation, Epoch id: 1, Training steps: 4200, Avg loss: 0.101
labelsname: label_Consolidation, Epoch id: 1, Training steps: 4300, Avg loss: 0.114
labelsname: label_Consolidation, Epoch id: 1, Training steps: 4400, Avg loss: 0.101
labelsname: label_Consolidation, Epoch id: 1, Training steps: 4500, Avg loss: 0.134
labelsname: label_Consolidation, Epoch id: 1, Training steps: 4600, Avg loss: 0.153
labelsname: label_Consolidation, Epoch id: 1, Training steps: 4700, Avg loss: 0.088
labelsname: label_Consolidation, Epoch id: 1, Training steps: 4800, Avg loss: 0.119
labelsname: label_Consolidation, Epoch id: 1, Training steps: 4900, Avg loss: 0.115
labelsname: label_Consolidation, Epoch id: 1, Training steps: 5000, Avg loss: 0.120
labelsname: label_Consolidation, Epoch id: 1, Training steps: 5100, Avg loss: 0.118
labelsname: label_Consolidation, Epoch id: 1, Training steps: 5200, Avg loss: 0.118
labelsname: label_Consolidation, Epoch id: 1, Training steps: 5300, Avg loss: 0.114
labelsname: label_Consolidation, Epoch id: 1, Training steps: 5400, Avg loss: 0.130
labelsname: label_Consolidation, Epoch id: 1, Training steps: 5500, Avg loss: 0.116
labelsname: label_Consolidation, Epoch id: 1, Training steps: 5600, Avg loss: 0.111
labelsname: label_Consolidation, Epoch id: 1, Training steps: 5700, Avg loss: 0.113
labelsname: label_Consolidation, Epoch id: 1, Training steps: 5800, Avg loss: 0.132
labelsname: label_Consolidation, Epoch id: 1, Training steps: 5900, Avg loss: 0.124
labelsname: label_Consolidation, Epoch id: 1, Training steps: 6000, Avg loss: 0.119
labelsname: label_Consolidation, Epoch id: 1, Training steps: 6100, Avg loss: 0.111
labelsname: label_Consolidation, Epoch id: 1, Training steps: 6200, Avg loss: 0.105
Start evaluation on test dataset.
Loading sentences from ./datasets/CheXpert/impression/validation.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
labelsname: label_Consolidation, Label_value 0: 0.993, 1.000, 0.996
labelsname: label_Consolidation, Label_value 1: 0.714, 0.841, 0.772
labelsname: label_Consolidation, Label_value 2: 0.774, 0.848, 0.809
model never predicts label value 3
labelsname: label_Consolidation, Label_value 3: 0.000, 0.000, 0.000
Acc. (Correct/Total): 0.9673 (32040/33124)
Start evaluation on evaluation dataset.
Loading sentences from ./datasets/CheXpert/impression/test.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
The number of evaluation instances: 33124
Confusion matrix of {label_name}:
tensor([[29565, 158, 39, 12],
[ 6, 1420, 144, 458],
[ 3, 111, 1020, 188],
[ 0, 0, 0, 0]])
Report precision, recall, and f1:
labelsname: label_Consolidation, Label_value 0: 0.993, 1.000, 0.996
labelsname: label_Consolidation, Label_value 1: 0.700, 0.841, 0.764
labelsname: label_Consolidation, Label_value 2: 0.772, 0.848, 0.808
model never predicts label value 3
labelsname: label_Consolidation, Label_value 3: 0.000, 0.000, 0.000
Acc. (Correct/Total): 0.9662 (32005/33124)
labelsname: label_Consolidation, Epoch id: 2, Training steps: 100, Avg loss: 0.117
labelsname: label_Consolidation, Epoch id: 2, Training steps: 200, Avg loss: 0.113
labelsname: label_Consolidation, Epoch id: 2, Training steps: 300, Avg loss: 0.133
labelsname: label_Consolidation, Epoch id: 2, Training steps: 400, Avg loss: 0.099
labelsname: label_Consolidation, Epoch id: 2, Training steps: 500, Avg loss: 0.116
labelsname: label_Consolidation, Epoch id: 2, Training steps: 600, Avg loss: 0.136
labelsname: label_Consolidation, Epoch id: 2, Training steps: 700, Avg loss: 0.123
labelsname: label_Consolidation, Epoch id: 2, Training steps: 800, Avg loss: 0.128
labelsname: label_Consolidation, Epoch id: 2, Training steps: 900, Avg loss: 0.129
labelsname: label_Consolidation, Epoch id: 2, Training steps: 1000, Avg loss: 0.103
labelsname: label_Consolidation, Epoch id: 2, Training steps: 1100, Avg loss: 0.119
labelsname: label_Consolidation, Epoch id: 2, Training steps: 1200, Avg loss: 0.120
labelsname: label_Consolidation, Epoch id: 2, Training steps: 1300, Avg loss: 0.125
labelsname: label_Consolidation, Epoch id: 2, Training steps: 1400, Avg loss: 0.111
labelsname: label_Consolidation, Epoch id: 2, Training steps: 1500, Avg loss: 0.132
labelsname: label_Consolidation, Epoch id: 2, Training steps: 1600, Avg loss: 0.092
labelsname: label_Consolidation, Epoch id: 2, Training steps: 1700, Avg loss: 0.096
labelsname: label_Consolidation, Epoch id: 2, Training steps: 1800, Avg loss: 0.123
labelsname: label_Consolidation, Epoch id: 2, Training steps: 1900, Avg loss: 0.108
labelsname: label_Consolidation, Epoch id: 2, Training steps: 2000, Avg loss: 0.115
labelsname: label_Consolidation, Epoch id: 2, Training steps: 2100, Avg loss: 0.078
labelsname: label_Consolidation, Epoch id: 2, Training steps: 2200, Avg loss: 0.114
labelsname: label_Consolidation, Epoch id: 2, Training steps: 2300, Avg loss: 0.116
labelsname: label_Consolidation, Epoch id: 2, Training steps: 2400, Avg loss: 0.096
labelsname: label_Consolidation, Epoch id: 2, Training steps: 2500, Avg loss: 0.123
labelsname: label_Consolidation, Epoch id: 2, Training steps: 2600, Avg loss: 0.104
labelsname: label_Consolidation, Epoch id: 2, Training steps: 2700, Avg loss: 0.111
labelsname: label_Consolidation, Epoch id: 2, Training steps: 2800, Avg loss: 0.107
labelsname: label_Consolidation, Epoch id: 2, Training steps: 2900, Avg loss: 0.102
labelsname: label_Consolidation, Epoch id: 2, Training steps: 3000, Avg loss: 0.104
labelsname: label_Consolidation, Epoch id: 2, Training steps: 3100, Avg loss: 0.109
labelsname: label_Consolidation, Epoch id: 2, Training steps: 3200, Avg loss: 0.123
labelsname: label_Consolidation, Epoch id: 2, Training steps: 3300, Avg loss: 0.112
labelsname: label_Consolidation, Epoch id: 2, Training steps: 3400, Avg loss: 0.102
labelsname: label_Consolidation, Epoch id: 2, Training steps: 3500, Avg loss: 0.108
labelsname: label_Consolidation, Epoch id: 2, Training steps: 3600, Avg loss: 0.124
labelsname: label_Consolidation, Epoch id: 2, Training steps: 3700, Avg loss: 0.114
labelsname: label_Consolidation, Epoch id: 2, Training steps: 3800, Avg loss: 0.111
labelsname: label_Consolidation, Epoch id: 2, Training steps: 3900, Avg loss: 0.108
labelsname: label_Consolidation, Epoch id: 2, Training steps: 4000, Avg loss: 0.089
labelsname: label_Consolidation, Epoch id: 2, Training steps: 4100, Avg loss: 0.102
labelsname: label_Consolidation, Epoch id: 2, Training steps: 4200, Avg loss: 0.095
labelsname: label_Consolidation, Epoch id: 2, Training steps: 4300, Avg loss: 0.102
labelsname: label_Consolidation, Epoch id: 2, Training steps: 4400, Avg loss: 0.091
labelsname: label_Consolidation, Epoch id: 2, Training steps: 4500, Avg loss: 0.124
labelsname: label_Consolidation, Epoch id: 2, Training steps: 4600, Avg loss: 0.119
labelsname: label_Consolidation, Epoch id: 2, Training steps: 4700, Avg loss: 0.071
labelsname: label_Consolidation, Epoch id: 2, Training steps: 4800, Avg loss: 0.101
labelsname: label_Consolidation, Epoch id: 2, Training steps: 4900, Avg loss: 0.098
labelsname: label_Consolidation, Epoch id: 2, Training steps: 5000, Avg loss: 0.114
labelsname: label_Consolidation, Epoch id: 2, Training steps: 5100, Avg loss: 0.109
labelsname: label_Consolidation, Epoch id: 2, Training steps: 5200, Avg loss: 0.112
labelsname: label_Consolidation, Epoch id: 2, Training steps: 5300, Avg loss: 0.096
labelsname: label_Consolidation, Epoch id: 2, Training steps: 5400, Avg loss: 0.121
labelsname: label_Consolidation, Epoch id: 2, Training steps: 5500, Avg loss: 0.096
labelsname: label_Consolidation, Epoch id: 2, Training steps: 5600, Avg loss: 0.104
labelsname: label_Consolidation, Epoch id: 2, Training steps: 5700, Avg loss: 0.092
labelsname: label_Consolidation, Epoch id: 2, Training steps: 5800, Avg loss: 0.115
labelsname: label_Consolidation, Epoch id: 2, Training steps: 5900, Avg loss: 0.113
labelsname: label_Consolidation, Epoch id: 2, Training steps: 6000, Avg loss: 0.108
labelsname: label_Consolidation, Epoch id: 2, Training steps: 6100, Avg loss: 0.095
labelsname: label_Consolidation, Epoch id: 2, Training steps: 6200, Avg loss: 0.089
Start evaluation on test dataset.
Loading sentences from ./datasets/CheXpert/impression/validation.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
labelsname: label_Consolidation, Label_value 0: 0.993, 1.000, 0.996
labelsname: label_Consolidation, Label_value 1: 0.736, 0.819, 0.775
labelsname: label_Consolidation, Label_value 2: 0.818, 0.866, 0.841
labelsname: label_Consolidation, Label_value 3: 0.745, 0.182, 0.293
Acc. (Correct/Total): 0.9702 (32138/33124)
Start evaluation on evaluation dataset.
Loading sentences from ./datasets/CheXpert/impression/test.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
The number of evaluation instances: 33124
Confusion matrix of {label_name}:
tensor([[29566, 159, 40, 12],
[ 5, 1407, 114, 436],
[ 3, 106, 1036, 82],
[ 0, 17, 13, 128]])
Report precision, recall, and f1:
labelsname: label_Consolidation, Label_value 0: 0.993, 1.000, 0.996
labelsname: label_Consolidation, Label_value 1: 0.717, 0.833, 0.771
labelsname: label_Consolidation, Label_value 2: 0.844, 0.861, 0.853
labelsname: label_Consolidation, Label_value 3: 0.810, 0.195, 0.314
Acc. (Correct/Total): 0.9702 (32137/33124)
labelsname: label_Consolidation, Epoch id: 3, Training steps: 100, Avg loss: 0.101
labelsname: label_Consolidation, Epoch id: 3, Training steps: 200, Avg loss: 0.098
labelsname: label_Consolidation, Epoch id: 3, Training steps: 300, Avg loss: 0.113
labelsname: label_Consolidation, Epoch id: 3, Training steps: 400, Avg loss: 0.088
labelsname: label_Consolidation, Epoch id: 3, Training steps: 500, Avg loss: 0.099
labelsname: label_Consolidation, Epoch id: 3, Training steps: 600, Avg loss: 0.110
labelsname: label_Consolidation, Epoch id: 3, Training steps: 700, Avg loss: 0.109
labelsname: label_Consolidation, Epoch id: 3, Training steps: 800, Avg loss: 0.107
labelsname: label_Consolidation, Epoch id: 3, Training steps: 900, Avg loss: 0.115
labelsname: label_Consolidation, Epoch id: 3, Training steps: 1000, Avg loss: 0.091
labelsname: label_Consolidation, Epoch id: 3, Training steps: 1100, Avg loss: 0.108
labelsname: label_Consolidation, Epoch id: 3, Training steps: 1200, Avg loss: 0.108
labelsname: label_Consolidation, Epoch id: 3, Training steps: 1300, Avg loss: 0.093
labelsname: label_Consolidation, Epoch id: 3, Training steps: 1400, Avg loss: 0.089
labelsname: label_Consolidation, Epoch id: 3, Training steps: 1500, Avg loss: 0.120
labelsname: label_Consolidation, Epoch id: 3, Training steps: 1600, Avg loss: 0.081
labelsname: label_Consolidation, Epoch id: 3, Training steps: 1700, Avg loss: 0.084
labelsname: label_Consolidation, Epoch id: 3, Training steps: 1800, Avg loss: 0.106
labelsname: label_Consolidation, Epoch id: 3, Training steps: 1900, Avg loss: 0.087
labelsname: label_Consolidation, Epoch id: 3, Training steps: 2000, Avg loss: 0.097
labelsname: label_Consolidation, Epoch id: 3, Training steps: 2100, Avg loss: 0.066
labelsname: label_Consolidation, Epoch id: 3, Training steps: 2200, Avg loss: 0.099
labelsname: label_Consolidation, Epoch id: 3, Training steps: 2300, Avg loss: 0.104
labelsname: label_Consolidation, Epoch id: 3, Training steps: 2400, Avg loss: 0.077
labelsname: label_Consolidation, Epoch id: 3, Training steps: 2500, Avg loss: 0.103
labelsname: label_Consolidation, Epoch id: 3, Training steps: 2600, Avg loss: 0.083
labelsname: label_Consolidation, Epoch id: 3, Training steps: 2700, Avg loss: 0.103
labelsname: label_Consolidation, Epoch id: 3, Training steps: 2800, Avg loss: 0.094
labelsname: label_Consolidation, Epoch id: 3, Training steps: 2900, Avg loss: 0.088
labelsname: label_Consolidation, Epoch id: 3, Training steps: 3000, Avg loss: 0.082
labelsname: label_Consolidation, Epoch id: 3, Training steps: 3100, Avg loss: 0.088
labelsname: label_Consolidation, Epoch id: 3, Training steps: 3200, Avg loss: 0.104
labelsname: label_Consolidation, Epoch id: 3, Training steps: 3300, Avg loss: 0.097
labelsname: label_Consolidation, Epoch id: 3, Training steps: 3400, Avg loss: 0.085
labelsname: label_Consolidation, Epoch id: 3, Training steps: 3500, Avg loss: 0.085
labelsname: label_Consolidation, Epoch id: 3, Training steps: 3600, Avg loss: 0.106
labelsname: label_Consolidation, Epoch id: 3, Training steps: 3700, Avg loss: 0.097
labelsname: label_Consolidation, Epoch id: 3, Training steps: 3800, Avg loss: 0.092
labelsname: label_Consolidation, Epoch id: 3, Training steps: 3900, Avg loss: 0.096
labelsname: label_Consolidation, Epoch id: 3, Training steps: 4000, Avg loss: 0.080
labelsname: label_Consolidation, Epoch id: 3, Training steps: 4100, Avg loss: 0.084
labelsname: label_Consolidation, Epoch id: 3, Training steps: 4200, Avg loss: 0.076
labelsname: label_Consolidation, Epoch id: 3, Training steps: 4300, Avg loss: 0.084
labelsname: label_Consolidation, Epoch id: 3, Training steps: 4400, Avg loss: 0.080
labelsname: label_Consolidation, Epoch id: 3, Training steps: 4500, Avg loss: 0.110
labelsname: label_Consolidation, Epoch id: 3, Training steps: 4600, Avg loss: 0.092
labelsname: label_Consolidation, Epoch id: 3, Training steps: 4700, Avg loss: 0.063
labelsname: label_Consolidation, Epoch id: 3, Training steps: 4800, Avg loss: 0.086
labelsname: label_Consolidation, Epoch id: 3, Training steps: 4900, Avg loss: 0.078
labelsname: label_Consolidation, Epoch id: 3, Training steps: 5000, Avg loss: 0.094
labelsname: label_Consolidation, Epoch id: 3, Training steps: 5100, Avg loss: 0.085
labelsname: label_Consolidation, Epoch id: 3, Training steps: 5200, Avg loss: 0.098
labelsname: label_Consolidation, Epoch id: 3, Training steps: 5300, Avg loss: 0.078
labelsname: label_Consolidation, Epoch id: 3, Training steps: 5400, Avg loss: 0.104
labelsname: label_Consolidation, Epoch id: 3, Training steps: 5500, Avg loss: 0.083
labelsname: label_Consolidation, Epoch id: 3, Training steps: 5600, Avg loss: 0.087
labelsname: label_Consolidation, Epoch id: 3, Training steps: 5700, Avg loss: 0.079
labelsname: label_Consolidation, Epoch id: 3, Training steps: 5800, Avg loss: 0.101
labelsname: label_Consolidation, Epoch id: 3, Training steps: 5900, Avg loss: 0.094
labelsname: label_Consolidation, Epoch id: 3, Training steps: 6000, Avg loss: 0.090
labelsname: label_Consolidation, Epoch id: 3, Training steps: 6100, Avg loss: 0.076
labelsname: label_Consolidation, Epoch id: 3, Training steps: 6200, Avg loss: 0.075
Start evaluation on test dataset.
Loading sentences from ./datasets/CheXpert/impression/validation.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
labelsname: label_Consolidation, Label_value 0: 0.993, 1.000, 0.996
labelsname: label_Consolidation, Label_value 1: 0.787, 0.800, 0.793
labelsname: label_Consolidation, Label_value 2: 0.862, 0.854, 0.858
labelsname: label_Consolidation, Label_value 3: 0.703, 0.451, 0.550
Acc. (Correct/Total): 0.9739 (32259/33124)
Start evaluation on evaluation dataset.
Loading sentences from ./datasets/CheXpert/impression/test.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
The number of evaluation instances: 33124
Confusion matrix of {label_name}:
tensor([[29566, 158, 39, 12],
[ 6, 1368, 101, 305],
[ 2, 79, 1028, 51],
[ 0, 84, 35, 290]])
Report precision, recall, and f1:
labelsname: label_Consolidation, Label_value 0: 0.993, 1.000, 0.996
labelsname: label_Consolidation, Label_value 1: 0.769, 0.810, 0.789
labelsname: label_Consolidation, Label_value 2: 0.886, 0.855, 0.870
labelsname: label_Consolidation, Label_value 3: 0.709, 0.441, 0.544
Acc. (Correct/Total): 0.9737 (32252/33124)
Final evaluation on the evaluation dataset.
Loading sentences from ./datasets/CheXpert/impression/test.csv
There are 33124 sentence in total. We use 1 processes to inject knowledge into sentences.
Progress of process 0: 0/33124
Progress of process 0: 10000/33124
Progress of process 0: 20000/33124
Progress of process 0: 30000/33124
The number of evaluation instances: 33124
Confusion matrix of {label_name}:
tensor([[29566, 158, 39, 12],
[ 6, 1368, 101, 305],
[ 2, 79, 1028, 51],
[ 0, 84, 35, 290]])
Report precision, recall, and f1:
labelsname: label_Consolidation, Label_value 0: 0.993, 1.000, 0.996
labelsname: label_Consolidation, Label_value 1: 0.769, 0.810, 0.789
labelsname: label_Consolidation, Label_value 2: 0.886, 0.855, 0.870
labelsname: label_Consolidation, Label_value 3: 0.709, 0.441, 0.544
Acc. (Correct/Total): 0.9737 (32252/33124)