text
stringlengths
54
260
06-07 08:08 - modeling.trainer - INFO - train - iter 115400: loss 3.1097, time 5.08s
06-07 08:08 - modeling.trainer - INFO - train - iter 115450: loss 3.1003, time 5.08s
06-07 08:08 - modeling.trainer - INFO - train - iter 115500: loss 3.0990, time 5.07s
06-07 08:08 - modeling.trainer - INFO - train - iter 115550: loss 3.0977, time 5.07s
06-07 08:08 - modeling.trainer - INFO - train - iter 115600: loss 3.0952, time 5.07s
06-07 08:08 - modeling.trainer - INFO - train - iter 115650: loss 3.1072, time 5.07s
06-07 08:08 - modeling.trainer - INFO - train - iter 115700: loss 3.1033, time 5.08s
06-07 08:08 - modeling.trainer - INFO - train - iter 115750: loss 3.0880, time 5.07s
06-07 08:08 - modeling.trainer - INFO - train - iter 115800: loss 3.0991, time 5.07s
06-07 08:08 - modeling.trainer - INFO - train - iter 115850: loss 3.1082, time 5.10s
06-07 08:08 - modeling.trainer - INFO - train - iter 115900: loss 3.1152, time 5.07s
06-07 08:08 - modeling.trainer - INFO - train - iter 115950: loss 3.1120, time 5.07s
06-07 08:09 - modeling.trainer - INFO - train - iter 116000: loss 3.1033, time 5.06s
06-07 08:09 - modeling.trainer - INFO - train - iter 116050: loss 3.0997, time 5.06s
06-07 08:09 - modeling.trainer - INFO - train - iter 116100: loss 3.0971, time 5.07s
06-07 08:09 - modeling.trainer - INFO - train - iter 116150: loss 3.1092, time 5.07s
06-07 08:09 - modeling.trainer - INFO - train - iter 116200: loss 3.1044, time 5.07s
06-07 08:09 - modeling.trainer - INFO - train - iter 116250: loss 3.0946, time 5.08s
06-07 08:09 - modeling.trainer - INFO - train - iter 116300: loss 3.0991, time 5.75s
06-07 08:09 - modeling.trainer - INFO - train - iter 116350: loss 3.0953, time 5.10s
06-07 08:09 - modeling.trainer - INFO - train - iter 116400: loss 3.0939, time 5.09s
06-07 08:09 - modeling.trainer - INFO - train - iter 116450: loss 3.1013, time 5.09s
06-07 08:09 - modeling.trainer - INFO - train - iter 116500: loss 3.0976, time 5.09s
06-07 08:09 - modeling.trainer - INFO - train - iter 116550: loss 3.0965, time 5.08s
06-07 08:10 - modeling.trainer - INFO - train - iter 116600: loss 3.0953, time 5.08s
06-07 08:10 - modeling.trainer - INFO - train - iter 116650: loss 3.0895, time 5.09s
06-07 08:10 - modeling.trainer - INFO - train - iter 116700: loss 3.1027, time 5.09s
06-07 08:10 - modeling.trainer - INFO - train - iter 116750: loss 3.1096, time 5.08s
06-07 08:10 - modeling.trainer - INFO - train - iter 116800: loss 3.0921, time 5.09s
06-07 08:10 - modeling.trainer - INFO - train - iter 116850: loss 3.0872, time 5.07s
06-07 08:10 - modeling.trainer - INFO - train - iter 116900: loss 3.0958, time 5.08s
06-07 08:10 - modeling.trainer - INFO - train - iter 116950: loss 3.1002, time 5.08s
06-07 08:10 - modeling.trainer - INFO - train - iter 117000: loss 3.0948, time 5.08s
06-07 08:10 - modeling.trainer - INFO - train - iter 117050: loss 3.1035, time 5.09s
06-07 08:10 - modeling.trainer - INFO - train - iter 117100: loss 3.1124, time 5.10s
06-07 08:10 - modeling.trainer - INFO - train - iter 117150: loss 3.1059, time 5.10s
06-07 08:11 - modeling.trainer - INFO - train - iter 117200: loss 3.1077, time 5.09s
06-07 08:11 - modeling.trainer - INFO - train - iter 117250: loss 3.1007, time 5.10s
06-07 08:11 - modeling.trainer - INFO - train - iter 117300: loss 3.0934, time 5.09s
06-07 08:11 - modeling.trainer - INFO - train - iter 117350: loss 3.1007, time 5.10s
06-07 08:11 - modeling.trainer - INFO - train - iter 117400: loss 3.1058, time 5.08s
06-07 08:11 - modeling.trainer - INFO - train - iter 117450: loss 3.0970, time 5.10s
06-07 08:11 - modeling.trainer - INFO - train - iter 117500: loss 3.0896, time 5.09s
06-07 08:11 - modeling.trainer - INFO - train - iter 117550: loss 3.0917, time 5.09s
06-07 08:11 - modeling.trainer - INFO - train - iter 117600: loss 3.1000, time 5.08s
06-07 08:11 - modeling.trainer - INFO - train - iter 117650: loss 3.1070, time 5.07s
06-07 08:11 - modeling.trainer - INFO - train - iter 117700: loss 3.1086, time 5.07s
06-07 08:12 - modeling.trainer - INFO - train - iter 117750: loss 3.1004, time 5.07s
06-07 08:12 - modeling.trainer - INFO - train - iter 117800: loss 3.0999, time 5.07s
06-07 08:12 - modeling.trainer - INFO - train - iter 117850: loss 3.1011, time 5.07s
06-07 08:12 - modeling.trainer - INFO - train - iter 117900: loss 3.1057, time 5.08s
06-07 08:12 - modeling.trainer - INFO - train - iter 117950: loss 3.1146, time 5.07s
06-07 08:12 - modeling.trainer - INFO - train - iter 118000: loss 3.1023, time 5.69s
06-07 08:12 - modeling.trainer - INFO - train - iter 118050: loss 3.0928, time 5.07s
06-07 08:12 - modeling.trainer - INFO - train - iter 118100: loss 3.0945, time 5.07s
06-07 08:12 - modeling.trainer - INFO - train - iter 118150: loss 3.0926, time 5.06s
06-07 08:12 - modeling.trainer - INFO - train - iter 118200: loss 3.0921, time 5.07s
06-07 08:12 - modeling.trainer - INFO - train - iter 118250: loss 3.0990, time 5.06s
06-07 08:12 - modeling.trainer - INFO - train - iter 118300: loss 3.1065, time 5.06s
06-07 08:13 - modeling.trainer - INFO - train - iter 118350: loss 3.1032, time 5.06s
06-07 08:13 - modeling.trainer - INFO - train - iter 118400: loss 3.1023, time 5.07s
06-07 08:13 - modeling.trainer - INFO - train - iter 118450: loss 3.0942, time 5.07s
06-07 08:13 - modeling.trainer - INFO - train - iter 118500: loss 3.0911, time 5.07s
06-07 08:13 - modeling.trainer - INFO - train - iter 118550: loss 3.0966, time 5.06s
06-07 08:13 - modeling.trainer - INFO - train - iter 118600: loss 3.1018, time 5.07s
06-07 08:13 - modeling.trainer - INFO - train - iter 118650: loss 3.1097, time 5.07s
06-07 08:13 - modeling.trainer - INFO - train - iter 118700: loss 3.0987, time 5.07s
06-07 08:13 - modeling.trainer - INFO - train - iter 118750: loss 3.0900, time 5.07s
06-07 08:13 - modeling.trainer - INFO - train - iter 118800: loss 3.0909, time 5.06s
06-07 08:13 - modeling.trainer - INFO - train - iter 118850: loss 3.1030, time 5.06s
06-07 08:13 - modeling.trainer - INFO - train - iter 118900: loss 3.1043, time 5.07s
06-07 08:14 - modeling.trainer - INFO - train - iter 118950: loss 3.0910, time 5.07s
06-07 08:14 - modeling.trainer - INFO - train - iter 119000: loss 3.0924, time 5.06s
06-07 08:14 - modeling.trainer - INFO - train - iter 119050: loss 3.1024, time 5.06s
06-07 08:14 - modeling.trainer - INFO - train - iter 119100: loss 3.1031, time 5.06s
06-07 08:14 - modeling.trainer - INFO - train - iter 119150: loss 3.1002, time 5.07s
06-07 08:14 - modeling.trainer - INFO - train - iter 119200: loss 3.0998, time 5.06s
06-07 08:14 - modeling.trainer - INFO - train - iter 119250: loss 3.1003, time 5.06s
06-07 08:14 - modeling.trainer - INFO - train - iter 119300: loss 3.1024, time 5.07s
06-07 08:14 - modeling.trainer - INFO - train - iter 119350: loss 3.1006, time 5.07s
06-07 08:14 - modeling.trainer - INFO - train - iter 119400: loss 3.0955, time 5.06s
06-07 08:14 - modeling.trainer - INFO - train - iter 119450: loss 3.0948, time 5.06s
06-07 08:14 - modeling.trainer - INFO - train - iter 119500: loss 3.1017, time 5.06s
06-07 08:15 - modeling.trainer - INFO - train - iter 119550: loss 3.1005, time 5.07s
06-07 08:15 - modeling.trainer - INFO - train - iter 119600: loss 3.1036, time 5.05s
06-07 08:15 - modeling.trainer - INFO - train - iter 119650: loss 3.0976, time 5.06s
06-07 08:15 - modeling.trainer - INFO - train - iter 119700: loss 3.0929, time 5.07s
06-07 08:15 - modeling.trainer - INFO - train - iter 119750: loss 3.0956, time 5.72s
06-07 08:15 - modeling.trainer - INFO - train - iter 119800: loss 3.0943, time 5.06s
06-07 08:15 - modeling.trainer - INFO - train - iter 119850: loss 3.1001, time 5.07s
06-07 08:15 - modeling.trainer - INFO - train - iter 119900: loss 3.1040, time 5.06s
06-07 08:15 - modeling.trainer - INFO - train - iter 119950: loss 3.1047, time 5.06s
06-07 08:15 - modeling.trainer - INFO - val - iter 120000: lm_loss 1.5298, value_loss 0.7583, time_loss 0.7038, loss 2.9920, time 4.02s
06-07 08:15 - modeling.trainer - INFO - new best val loss 2.9920
06-07 08:15 - modeling.trainer - INFO - saved checkpoint to models/ablations/half/best.pt
06-07 08:16 - modeling.trainer - INFO - saved checkpoint to models/ablations/half/last.pt
06-07 08:16 - modeling.trainer - INFO - train - iter 120000: loss 3.1021, time 16.43s
06-07 08:16 - modeling.trainer - INFO - train - iter 120050: loss 3.1024, time 5.07s
06-07 08:16 - modeling.trainer - INFO - train - iter 120100: loss 3.0894, time 5.09s
06-07 08:16 - modeling.trainer - INFO - train - iter 120150: loss 3.0873, time 5.11s