2023-02-11 07:29:37,539 ---------------------------------------------------------------------------------------------------- 2023-02-11 07:29:37,541 Model: "EntityLinker( (embeddings): TransformerWordEmbeddings( (model): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(30523, 768) (position_embeddings): Embedding(512, 768) (token_type_embeddings): Embedding(2, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (1): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (2): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (3): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (4): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (5): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (6): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (7): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (8): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (9): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (10): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (11): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=768, out_features=768, bias=True) (activation): Tanh() ) ) ) (decoder): Linear(in_features=768, out_features=44462, bias=True) (dropout): Dropout(p=0.0, inplace=False) (locked_dropout): LockedDropout(p=0.0) (word_dropout): WordDropout(p=0.0) (loss_function): CrossEntropyLoss() (weights): None (weight_tensor) None )" 2023-02-11 07:29:37,542 ---------------------------------------------------------------------------------------------------- 2023-02-11 07:29:37,542 Corpus: "Corpus: 2250822 train + 70408 dev + 70408 test sentences" 2023-02-11 07:29:37,542 ---------------------------------------------------------------------------------------------------- 2023-02-11 07:29:37,542 Parameters: 2023-02-11 07:29:37,542 - learning_rate: "0.000050" 2023-02-11 07:29:37,542 - mini_batch_size: "64" 2023-02-11 07:29:37,542 - patience: "3" 2023-02-11 07:29:37,542 - anneal_factor: "0.5" 2023-02-11 07:29:37,542 - max_epochs: "10" 2023-02-11 07:29:37,542 - shuffle: "True" 2023-02-11 07:29:37,542 - train_with_dev: "False" 2023-02-11 07:29:37,543 - batch_growth_annealing: "False" 2023-02-11 07:29:37,543 ---------------------------------------------------------------------------------------------------- 2023-02-11 07:29:37,543 Model training base path: "resources/taggers/zelda_eval/link+context-bert-base-uncased-lr-5e-05x1-mbz-64-min-10-chunk-8" 2023-02-11 07:29:37,543 ---------------------------------------------------------------------------------------------------- 2023-02-11 07:29:37,543 Device: cuda:2 2023-02-11 07:29:37,543 ---------------------------------------------------------------------------------------------------- 2023-02-11 07:29:37,543 Embeddings storage mode: none 2023-02-11 07:29:37,543 ---------------------------------------------------------------------------------------------------- 2023-02-11 08:05:15,967 epoch 1 - iter 3517/35170 - loss 8.28061754 - time (sec): 2138.42 - samples/sec: 143.45 - lr: 0.000005 2023-02-11 08:38:07,077 epoch 1 - iter 7034/35170 - loss 6.68744448 - time (sec): 4109.53 - samples/sec: 138.19 - lr: 0.000010 2023-02-11 09:09:17,397 epoch 1 - iter 10551/35170 - loss 6.13122458 - time (sec): 5979.85 - samples/sec: 137.20 - lr: 0.000015 2023-02-11 09:41:17,968 epoch 1 - iter 14068/35170 - loss 5.78727579 - time (sec): 7900.43 - samples/sec: 135.96 - lr: 0.000020 2023-02-11 10:12:42,735 epoch 1 - iter 17585/35170 - loss 5.49487485 - time (sec): 9785.19 - samples/sec: 136.19 - lr: 0.000025 2023-02-11 10:44:14,875 epoch 1 - iter 21102/35170 - loss 5.26321390 - time (sec): 11677.33 - samples/sec: 137.17 - lr: 0.000030 2023-02-11 11:14:54,485 epoch 1 - iter 24619/35170 - loss 5.01014195 - time (sec): 13516.94 - samples/sec: 139.43 - lr: 0.000035 2023-02-11 11:44:52,471 epoch 1 - iter 28136/35170 - loss 4.78443875 - time (sec): 15314.93 - samples/sec: 140.93 - lr: 0.000040 2023-02-11 12:11:02,266 epoch 1 - iter 31653/35170 - loss 4.57408367 - time (sec): 16884.72 - samples/sec: 140.57 - lr: 0.000045 2023-02-11 12:40:03,315 epoch 1 - iter 35170/35170 - loss 4.34795635 - time (sec): 18625.77 - samples/sec: 140.93 - lr: 0.000050 2023-02-11 12:40:03,316 ---------------------------------------------------------------------------------------------------- 2023-02-11 12:40:03,316 EPOCH 1 done: loss 4.3480 - lr 0.000050 2023-02-11 12:42:02,856 Evaluating as a multi-label problem: False 2023-02-11 12:42:03,445 DEV : loss 4.122243881225586 - precision (macro avg) 0.2389 2023-02-11 12:42:20,403 saving best model 2023-02-11 12:42:23,573 ---------------------------------------------------------------------------------------------------- 2023-02-11 13:15:20,249 epoch 2 - iter 3517/35170 - loss 1.74284950 - time (sec): 1976.68 - samples/sec: 155.19 - lr: 0.000049 2023-02-11 13:46:57,971 epoch 2 - iter 7034/35170 - loss 1.63737463 - time (sec): 3874.40 - samples/sec: 146.58 - lr: 0.000049 2023-02-11 14:17:33,473 epoch 2 - iter 10551/35170 - loss 1.55538011 - time (sec): 5709.90 - samples/sec: 143.69 - lr: 0.000048 2023-02-11 14:47:54,062 epoch 2 - iter 14068/35170 - loss 1.48044851 - time (sec): 7530.49 - samples/sec: 142.64 - lr: 0.000048 2023-02-11 15:17:56,543 epoch 2 - iter 17585/35170 - loss 1.41167290 - time (sec): 9332.97 - samples/sec: 142.79 - lr: 0.000047 2023-02-11 15:48:25,274 epoch 2 - iter 21102/35170 - loss 1.37993571 - time (sec): 11161.70 - samples/sec: 143.51 - lr: 0.000047 2023-02-11 16:18:40,360 epoch 2 - iter 24619/35170 - loss 1.32826516 - time (sec): 12976.79 - samples/sec: 145.23 - lr: 0.000046 2023-02-11 16:48:23,916 epoch 2 - iter 28136/35170 - loss 1.28903024 - time (sec): 14760.34 - samples/sec: 146.23 - lr: 0.000046 2023-02-11 17:13:23,456 epoch 2 - iter 31653/35170 - loss 1.24232587 - time (sec): 16259.88 - samples/sec: 145.98 - lr: 0.000045 2023-02-11 17:42:30,246 epoch 2 - iter 35170/35170 - loss 1.19544800 - time (sec): 18006.67 - samples/sec: 145.78 - lr: 0.000044 2023-02-11 17:42:30,247 ---------------------------------------------------------------------------------------------------- 2023-02-11 17:42:30,248 EPOCH 2 done: loss 1.1954 - lr 0.000044 2023-02-11 17:44:18,682 Evaluating as a multi-label problem: False 2023-02-11 17:44:19,159 DEV : loss 2.029381513595581 - precision (macro avg) 0.6079 2023-02-11 17:44:33,811 saving best model 2023-02-11 17:44:36,977 ---------------------------------------------------------------------------------------------------- 2023-02-11 18:17:53,614 epoch 3 - iter 3517/35170 - loss 0.56286082 - time (sec): 1996.64 - samples/sec: 153.64 - lr: 0.000044 2023-02-11 18:49:10,278 epoch 3 - iter 7034/35170 - loss 0.57656093 - time (sec): 3873.30 - samples/sec: 146.62 - lr: 0.000043 2023-02-11 19:19:32,746 epoch 3 - iter 10551/35170 - loss 0.58371236 - time (sec): 5695.77 - samples/sec: 144.05 - lr: 0.000043 2023-02-11 19:49:20,427 epoch 3 - iter 14068/35170 - loss 0.58743219 - time (sec): 7483.45 - samples/sec: 143.53 - lr: 0.000042 2023-02-11 20:19:45,796 epoch 3 - iter 17585/35170 - loss 0.58655522 - time (sec): 9308.82 - samples/sec: 143.16 - lr: 0.000042 2023-02-11 20:50:21,126 epoch 3 - iter 21102/35170 - loss 0.61243362 - time (sec): 11144.15 - samples/sec: 143.74 - lr: 0.000041 2023-02-11 21:20:53,185 epoch 3 - iter 24619/35170 - loss 0.61694600 - time (sec): 12976.21 - samples/sec: 145.24 - lr: 0.000041 2023-02-11 21:50:43,633 epoch 3 - iter 28136/35170 - loss 0.62347194 - time (sec): 14766.66 - samples/sec: 146.16 - lr: 0.000040 2023-02-11 22:15:41,927 epoch 3 - iter 31653/35170 - loss 0.61309257 - time (sec): 16264.95 - samples/sec: 145.93 - lr: 0.000039 2023-02-11 22:45:16,220 epoch 3 - iter 35170/35170 - loss 0.60432909 - time (sec): 18039.24 - samples/sec: 145.51 - lr: 0.000039 2023-02-11 22:45:16,222 ---------------------------------------------------------------------------------------------------- 2023-02-11 22:45:16,222 EPOCH 3 done: loss 0.6043 - lr 0.000039 2023-02-11 22:47:12,523 Evaluating as a multi-label problem: False 2023-02-11 22:47:13,184 DEV : loss 1.995651125907898 - precision (macro avg) 0.6622 2023-02-11 22:47:31,985 saving best model 2023-02-11 22:47:35,027 ---------------------------------------------------------------------------------------------------- 2023-02-11 23:20:58,828 epoch 4 - iter 3517/35170 - loss 0.38122240 - time (sec): 2003.80 - samples/sec: 153.09 - lr: 0.000038 2023-02-11 23:52:25,795 epoch 4 - iter 7034/35170 - loss 0.39570614 - time (sec): 3890.77 - samples/sec: 145.96 - lr: 0.000038 2023-02-12 00:22:38,377 epoch 4 - iter 10551/35170 - loss 0.40469172 - time (sec): 5703.35 - samples/sec: 143.86 - lr: 0.000037 2023-02-12 00:53:40,211 epoch 4 - iter 14068/35170 - loss 0.41340503 - time (sec): 7565.18 - samples/sec: 141.98 - lr: 0.000037 2023-02-12 01:23:37,513 epoch 4 - iter 17585/35170 - loss 0.41798627 - time (sec): 9362.49 - samples/sec: 142.34 - lr: 0.000036 2023-02-12 01:52:57,839 epoch 4 - iter 21102/35170 - loss 0.44558523 - time (sec): 11122.81 - samples/sec: 144.01 - lr: 0.000036 2023-02-12 02:23:06,217 epoch 4 - iter 24619/35170 - loss 0.45541004 - time (sec): 12931.19 - samples/sec: 145.74 - lr: 0.000035 2023-02-12 02:53:28,586 epoch 4 - iter 28136/35170 - loss 0.46648146 - time (sec): 14753.56 - samples/sec: 146.29 - lr: 0.000034 2023-02-12 03:17:44,273 epoch 4 - iter 31653/35170 - loss 0.46054118 - time (sec): 16209.25 - samples/sec: 146.43 - lr: 0.000034 2023-02-12 03:47:24,818 epoch 4 - iter 35170/35170 - loss 0.45638800 - time (sec): 17989.79 - samples/sec: 145.91 - lr: 0.000033 2023-02-12 03:47:24,820 ---------------------------------------------------------------------------------------------------- 2023-02-12 03:47:24,820 EPOCH 4 done: loss 0.4564 - lr 0.000033 2023-02-12 03:49:19,983 Evaluating as a multi-label problem: False 2023-02-12 03:49:20,561 DEV : loss 2.0926501750946045 - precision (macro avg) 0.691 2023-02-12 03:49:37,711 saving best model 2023-02-12 03:49:47,625 ---------------------------------------------------------------------------------------------------- 2023-02-12 04:22:16,000 epoch 5 - iter 3517/35170 - loss 0.29657713 - time (sec): 1948.37 - samples/sec: 157.44 - lr: 0.000033 2023-02-12 04:52:38,133 epoch 5 - iter 7034/35170 - loss 0.30797428 - time (sec): 3770.51 - samples/sec: 150.62 - lr: 0.000032 2023-02-12 05:23:06,345 epoch 5 - iter 10551/35170 - loss 0.31589817 - time (sec): 5598.72 - samples/sec: 146.55 - lr: 0.000032 2023-02-12 05:53:48,326 epoch 5 - iter 14068/35170 - loss 0.32675207 - time (sec): 7440.70 - samples/sec: 144.36 - lr: 0.000031 2023-02-12 06:24:13,572 epoch 5 - iter 17585/35170 - loss 0.33107455 - time (sec): 9265.95 - samples/sec: 143.82 - lr: 0.000031 2023-02-12 06:54:21,697 epoch 5 - iter 21102/35170 - loss 0.35911025 - time (sec): 11074.07 - samples/sec: 144.65 - lr: 0.000030 2023-02-12 07:23:02,551 epoch 5 - iter 24619/35170 - loss 0.36991923 - time (sec): 12794.93 - samples/sec: 147.30 - lr: 0.000029 2023-02-12 07:53:10,439 epoch 5 - iter 28136/35170 - loss 0.38154749 - time (sec): 14602.81 - samples/sec: 147.80 - lr: 0.000029 2023-02-12 08:18:50,395 epoch 5 - iter 31653/35170 - loss 0.37655020 - time (sec): 16142.77 - samples/sec: 147.03 - lr: 0.000028 2023-02-12 08:47:59,475 epoch 5 - iter 35170/35170 - loss 0.37359143 - time (sec): 17891.85 - samples/sec: 146.71 - lr: 0.000028 2023-02-12 08:47:59,476 ---------------------------------------------------------------------------------------------------- 2023-02-12 08:47:59,476 EPOCH 5 done: loss 0.3736 - lr 0.000028 2023-02-12 08:49:51,006 Evaluating as a multi-label problem: False 2023-02-12 08:49:51,649 DEV : loss 2.0782535076141357 - precision (macro avg) 0.713 2023-02-12 08:50:06,416 saving best model 2023-02-12 08:50:09,225 ---------------------------------------------------------------------------------------------------- 2023-02-12 09:23:42,392 epoch 6 - iter 3517/35170 - loss 0.23914707 - time (sec): 2013.17 - samples/sec: 152.37 - lr: 0.000027 2023-02-12 09:54:55,011 epoch 6 - iter 7034/35170 - loss 0.24578611 - time (sec): 3885.79 - samples/sec: 146.15 - lr: 0.000027 2023-02-12 10:26:07,899 epoch 6 - iter 10551/35170 - loss 0.25263040 - time (sec): 5758.67 - samples/sec: 142.47 - lr: 0.000026 2023-02-12 10:56:26,096 epoch 6 - iter 14068/35170 - loss 0.26362638 - time (sec): 7576.87 - samples/sec: 141.76 - lr: 0.000026 2023-02-12 11:27:01,740 epoch 6 - iter 17585/35170 - loss 0.26880386 - time (sec): 9412.51 - samples/sec: 141.58 - lr: 0.000025 2023-02-12 11:56:46,252 epoch 6 - iter 21102/35170 - loss 0.29553523 - time (sec): 11197.03 - samples/sec: 143.06 - lr: 0.000024 2023-02-12 12:31:36,784 epoch 6 - iter 24619/35170 - loss 0.30657537 - time (sec): 13287.56 - samples/sec: 141.83 - lr: 0.000024 2023-02-12 13:10:55,584 epoch 6 - iter 28136/35170 - loss 0.31842438 - time (sec): 15646.36 - samples/sec: 137.95 - lr: 0.000023 2023-02-12 13:44:03,285 epoch 6 - iter 31653/35170 - loss 0.31421845 - time (sec): 17634.06 - samples/sec: 134.60 - lr: 0.000023 2023-02-12 14:21:07,781 epoch 6 - iter 35170/35170 - loss 0.31196031 - time (sec): 19858.56 - samples/sec: 132.18 - lr: 0.000022 2023-02-12 14:21:07,782 ---------------------------------------------------------------------------------------------------- 2023-02-12 14:21:07,782 EPOCH 6 done: loss 0.3120 - lr 0.000022 2023-02-12 14:23:09,429 Evaluating as a multi-label problem: False 2023-02-12 14:23:09,899 DEV : loss 2.249941110610962 - precision (macro avg) 0.721 2023-02-12 14:23:24,371 saving best model 2023-02-12 14:23:27,367 ---------------------------------------------------------------------------------------------------- 2023-02-12 15:04:50,628 epoch 7 - iter 3517/35170 - loss 0.19553262 - time (sec): 2483.26 - samples/sec: 123.53 - lr: 0.000022 2023-02-12 15:44:02,933 epoch 7 - iter 7034/35170 - loss 0.19839675 - time (sec): 4835.57 - samples/sec: 117.45 - lr: 0.000021 2023-02-12 16:22:47,684 epoch 7 - iter 10551/35170 - loss 0.20242630 - time (sec): 7160.32 - samples/sec: 114.59 - lr: 0.000021 2023-02-12 17:02:11,020 epoch 7 - iter 14068/35170 - loss 0.21273520 - time (sec): 9523.65 - samples/sec: 112.78 - lr: 0.000020 2023-02-12 17:40:20,857 epoch 7 - iter 17585/35170 - loss 0.21711518 - time (sec): 11813.49 - samples/sec: 112.81 - lr: 0.000019 2023-02-12 18:18:51,930 epoch 7 - iter 21102/35170 - loss 0.24285997 - time (sec): 14124.56 - samples/sec: 113.41 - lr: 0.000019 2023-02-12 18:56:40,729 epoch 7 - iter 24619/35170 - loss 0.25346644 - time (sec): 16393.36 - samples/sec: 114.96 - lr: 0.000018 2023-02-12 19:35:14,185 epoch 7 - iter 28136/35170 - loss 0.26583854 - time (sec): 18706.82 - samples/sec: 115.38 - lr: 0.000018 2023-02-12 20:07:23,760 epoch 7 - iter 31653/35170 - loss 0.26225986 - time (sec): 20636.39 - samples/sec: 115.02 - lr: 0.000017 2023-02-12 20:44:54,287 epoch 7 - iter 35170/35170 - loss 0.26048177 - time (sec): 22886.92 - samples/sec: 114.69 - lr: 0.000017 2023-02-12 20:44:54,288 ---------------------------------------------------------------------------------------------------- 2023-02-12 20:44:54,288 EPOCH 7 done: loss 0.2605 - lr 0.000017 2023-02-12 20:46:56,478 Evaluating as a multi-label problem: False 2023-02-12 20:46:56,917 DEV : loss 2.3033623695373535 - precision (macro avg) 0.7269 2023-02-12 20:47:08,835 saving best model 2023-02-12 20:47:11,733 ---------------------------------------------------------------------------------------------------- 2023-02-12 21:28:26,118 epoch 8 - iter 3517/35170 - loss 0.15695903 - time (sec): 2474.38 - samples/sec: 123.97 - lr: 0.000016 2023-02-12 22:08:04,673 epoch 8 - iter 7034/35170 - loss 0.15791237 - time (sec): 4852.94 - samples/sec: 117.02 - lr: 0.000016 2023-02-12 22:45:24,927 epoch 8 - iter 10551/35170 - loss 0.16035912 - time (sec): 7093.19 - samples/sec: 115.67 - lr: 0.000015 2023-02-12 23:23:23,382 epoch 8 - iter 14068/35170 - loss 0.17015418 - time (sec): 9371.65 - samples/sec: 114.61 - lr: 0.000014 2023-02-13 00:00:40,018 epoch 8 - iter 17585/35170 - loss 0.17404749 - time (sec): 11608.28 - samples/sec: 114.80 - lr: 0.000014 2023-02-13 00:38:20,153 epoch 8 - iter 21102/35170 - loss 0.19803429 - time (sec): 13868.42 - samples/sec: 115.50 - lr: 0.000013 2023-02-13 01:15:49,000 epoch 8 - iter 24619/35170 - loss 0.20821470 - time (sec): 16117.27 - samples/sec: 116.93 - lr: 0.000013 2023-02-13 01:53:32,182 epoch 8 - iter 28136/35170 - loss 0.22046530 - time (sec): 18380.45 - samples/sec: 117.43 - lr: 0.000012 2023-02-13 02:25:03,810 epoch 8 - iter 31653/35170 - loss 0.21776111 - time (sec): 20272.08 - samples/sec: 117.08 - lr: 0.000012 2023-02-13 03:01:28,421 epoch 8 - iter 35170/35170 - loss 0.21658157 - time (sec): 22456.69 - samples/sec: 116.89 - lr: 0.000011 2023-02-13 03:01:28,422 ---------------------------------------------------------------------------------------------------- 2023-02-13 03:01:28,422 EPOCH 8 done: loss 0.2166 - lr 0.000011 2023-02-13 03:03:36,092 Evaluating as a multi-label problem: False 2023-02-13 03:03:36,595 DEV : loss 2.4392502307891846 - precision (macro avg) 0.7346 2023-02-13 03:03:52,756 saving best model 2023-02-13 03:03:56,224 ---------------------------------------------------------------------------------------------------- 2023-02-13 03:38:07,064 epoch 9 - iter 3517/35170 - loss 0.12064491 - time (sec): 2050.84 - samples/sec: 149.58 - lr: 0.000011 2023-02-13 04:08:40,845 epoch 9 - iter 7034/35170 - loss 0.12119502 - time (sec): 3884.62 - samples/sec: 146.20 - lr: 0.000010 2023-02-13 04:40:11,839 epoch 9 - iter 10551/35170 - loss 0.12313048 - time (sec): 5775.61 - samples/sec: 142.06 - lr: 0.000009 2023-02-13 05:12:07,821 epoch 9 - iter 14068/35170 - loss 0.13241493 - time (sec): 7691.60 - samples/sec: 139.65 - lr: 0.000009 2023-02-13 05:44:55,110 epoch 9 - iter 17585/35170 - loss 0.13550663 - time (sec): 9658.88 - samples/sec: 137.97 - lr: 0.000008 2023-02-13 06:17:36,844 epoch 9 - iter 21102/35170 - loss 0.15790628 - time (sec): 11620.62 - samples/sec: 137.84 - lr: 0.000008 2023-02-13 06:50:05,260 epoch 9 - iter 24619/35170 - loss 0.16777143 - time (sec): 13569.04 - samples/sec: 138.89 - lr: 0.000007 2023-02-13 07:22:30,013 epoch 9 - iter 28136/35170 - loss 0.18024951 - time (sec): 15513.79 - samples/sec: 139.12 - lr: 0.000007 2023-02-13 07:50:23,046 epoch 9 - iter 31653/35170 - loss 0.17837590 - time (sec): 17186.82 - samples/sec: 138.10 - lr: 0.000006 2023-02-13 08:21:55,201 epoch 9 - iter 35170/35170 - loss 0.17755869 - time (sec): 19078.98 - samples/sec: 137.58 - lr: 0.000006 2023-02-13 08:21:55,202 ---------------------------------------------------------------------------------------------------- 2023-02-13 08:21:55,202 EPOCH 9 done: loss 0.1776 - lr 0.000006 2023-02-13 08:23:34,835 Evaluating as a multi-label problem: False 2023-02-13 08:23:35,311 DEV : loss 2.496391534805298 - precision (macro avg) 0.7374 2023-02-13 08:23:49,891 saving best model 2023-02-13 08:23:53,206 ---------------------------------------------------------------------------------------------------- 2023-02-13 08:58:17,193 epoch 10 - iter 3517/35170 - loss 0.09099741 - time (sec): 2063.99 - samples/sec: 148.62 - lr: 0.000005 2023-02-13 09:31:54,631 epoch 10 - iter 7034/35170 - loss 0.09224199 - time (sec): 4081.42 - samples/sec: 139.15 - lr: 0.000004 2023-02-13 10:04:41,319 epoch 10 - iter 10551/35170 - loss 0.09332461 - time (sec): 6048.11 - samples/sec: 135.66 - lr: 0.000004 2023-02-13 10:36:57,517 epoch 10 - iter 14068/35170 - loss 0.10201757 - time (sec): 7984.31 - samples/sec: 134.53 - lr: 0.000003 2023-02-13 11:12:45,434 epoch 10 - iter 17585/35170 - loss 0.10493018 - time (sec): 10132.23 - samples/sec: 131.52 - lr: 0.000003 2023-02-13 11:50:48,776 epoch 10 - iter 21102/35170 - loss 0.12588592 - time (sec): 12415.57 - samples/sec: 129.02 - lr: 0.000002 2023-02-13 12:28:58,300 epoch 10 - iter 24619/35170 - loss 0.13534278 - time (sec): 14705.09 - samples/sec: 128.16 - lr: 0.000002 2023-02-13 13:07:41,958 epoch 10 - iter 28136/35170 - loss 0.14745632 - time (sec): 17028.75 - samples/sec: 126.75 - lr: 0.000001 2023-02-13 13:40:28,847 epoch 10 - iter 31653/35170 - loss 0.14657745 - time (sec): 18995.64 - samples/sec: 124.95 - lr: 0.000001 2023-02-13 14:18:01,928 epoch 10 - iter 35170/35170 - loss 0.14743975 - time (sec): 21248.72 - samples/sec: 123.53 - lr: 0.000000 2023-02-13 14:18:01,929 ---------------------------------------------------------------------------------------------------- 2023-02-13 14:18:01,929 EPOCH 10 done: loss 0.1474 - lr 0.000000 2023-02-13 14:20:03,282 Evaluating as a multi-label problem: False 2023-02-13 14:20:03,719 DEV : loss 2.4960052967071533 - precision (macro avg) 0.745 2023-02-13 14:20:17,013 saving best model 2023-02-13 14:20:23,065 ---------------------------------------------------------------------------------------------------- 2023-02-13 14:22:20,759 Evaluating as a multi-label problem: False 2023-02-13 14:22:21,310 0.7495 0.7495 0.7495 0.7495 2023-02-13 14:22:21,310 Results: - F-score (micro) 0.7495 - F-score (macro) 0.7288 - Accuracy 0.7495 By class: precision recall f1-score support 0.3297 0.8907 0.4813 2268 United_States 0.9391 0.5331 0.6801 347 United_Kingdom 0.9506 0.7196 0.8191 107 Germany 1.0000 0.7500 0.8571 104 France 0.9322 0.4661 0.6215 118 Australia 0.9667 0.5370 0.6905 108 New_York_City 0.9153 0.5000 0.6467 108 London 0.9740 0.9259 0.9494 81 Canada 0.9672 0.6705 0.7919 88 India 0.8889 0.6667 0.7619 84 Europe 0.8730 0.6962 0.7746 79 Italy 0.9787 0.5111 0.6715 90 California 0.9524 0.8333 0.8889 72 China 1.0000 0.7681 0.8689 69 Japan 0.9800 0.7101 0.8235 69 England 0.6500 0.3662 0.4685 71 Russia 0.9524 0.6061 0.7407 66 Australia_national_cricket_team 0.8214 0.9020 0.8598 51 Acer_Inc. 1.0000 1.0000 1.0000 49 Switzerland 0.9750 0.7647 0.8571 51 Poland 0.9750 0.7647 0.8571 51 God 0.8718 0.6939 0.7727 49 Ohio 1.0000 0.9318 0.9647 44 Spain 1.0000 0.5385 0.7000 52 Florida 0.8125 0.6047 0.6933 43 Israel 1.0000 0.8158 0.8986 38 Austria 1.0000 0.8649 0.9275 37 Singapore 1.0000 0.8611 0.9254 36 Jesus 1.0000 0.8529 0.9206 34 Donald_Trump 0.9643 0.7714 0.8571 35 Indonesia 0.9000 0.8438 0.8710 32 England_cricket_team 0.9286 0.7647 0.8387 34 Texas 0.9259 0.7576 0.8333 33 Greece 1.0000 0.5946 0.7458 37 Washington,_D.C. 0.8800 0.6667 0.7586 33 English_people 0.7407 0.6667 0.7018 30 Mexico 0.9259 0.8333 0.8772 30 Cricket 0.9655 1.0000 0.9825 28 Dubai 1.0000 0.4359 0.6071 39 West_Indies_cricket_team 1.0000 0.8889 0.9412 27 Brazil 1.0000 0.7500 0.8571 28 FC_Barcelona 0.9565 0.8800 0.9167 25 India_national_cricket_team 0.6538 0.7727 0.7083 22 Norway 1.0000 0.9583 0.9787 24 Africa 0.6250 0.3333 0.4348 30 North_Carolina 0.9000 0.6923 0.7826 26 Belgium 1.0000 0.7308 0.8444 26 Scotland 0.7647 0.4643 0.5778 28 Republic_of_Ireland 1.0000 0.2162 0.3556 37 Pakistan_national_cricket_team 1.0000 0.8333 0.9091 24 Apple_Inc. 0.5926 0.9412 0.7273 17 United_Nations 0.9412 0.6154 0.7442 26 Chicago 0.9333 0.5000 0.6512 28 Reuters 1.0000 1.0000 1.0000 21 Sandy_Bridge 1.0000 1.0000 1.0000 21 Great_Britain_national_rugby_league_team 1.0000 1.0000 1.0000 21 List_A_cricket 1.0000 1.0000 1.0000 21 National_Hockey_League 1.0000 0.8182 0.9000 22 Los_Angeles 0.9286 0.5000 0.6500 26 World_Trade_Organization 1.0000 0.9048 0.9500 21 IPhone 1.0000 1.0000 1.0000 20 Nepal 0.8889 0.7273 0.8000 22 Sweden 0.9412 0.7273 0.8205 22 Netherlands 0.8750 0.6087 0.7179 23 Real_Madrid_CF 1.0000 0.9500 0.9744 20 New_York_(state) 0.0000 0.0000 0.0000 4 National_Football_League 1.0000 0.8095 0.8947 21 Jews 0.9444 0.8500 0.8947 20 World_War_II 1.0000 1.0000 1.0000 19 European_Union 0.8333 0.7500 0.7895 20 Tony_Mitchell_(basketball,_born_1989) 1.0000 0.8095 0.8947 21 Colorado 1.0000 0.9474 0.9730 19 Christians 1.0000 0.7619 0.8649 21 First-class_cricket 1.0000 0.8500 0.9189 20 Sri_Lanka_national_cricket_team 0.5909 0.9286 0.7222 14 Lebanon 1.0000 0.5909 0.7429 22 Atlanta 1.0000 0.9444 0.9714 18 Boston 1.0000 0.7895 0.8824 19 Louisiana 0.8462 0.5238 0.6471 21 Borsa_Istanbul 1.0000 0.8889 0.9412 18 JaMychal_Green 1.0000 0.1724 0.2941 29 Nottinghamshire_County_Cricket_Club 0.8889 1.0000 0.9412 16 Georgia_(U.S._state) 0.7222 0.8667 0.7879 15 Republican_Party_(United_States) 0.8824 0.9375 0.9091 16 Ireland 0.1429 1.0000 0.2500 4 Australia_national_rugby_union_team 0.9375 1.0000 0.9677 15 South_Africa 0.8667 0.8125 0.8387 16 New_Mexico 1.0000 0.4091 0.5806 22 South_Korea 0.8333 0.5263 0.6452 19 Democratic_Party_(United_States) 1.0000 0.7222 0.8387 18 Barack_Obama 1.0000 0.9375 0.9677 16 Facebook 1.0000 0.9375 0.9677 16 Turkey 1.0000 0.5500 0.7097 20 Spironolactone 1.0000 0.9375 0.9677 16 Association_football 0.9375 1.0000 0.9677 15 Rugby_union 1.0000 1.0000 1.0000 15 Brian_Lara 1.0000 1.0000 1.0000 15 Twitter 0.8750 1.0000 0.9333 14 Mac_(computer) 1.0000 0.5000 0.6667 20 Southeastern_Conference 1.0000 0.8750 0.9333 16 New_Zealand 0.6667 0.4706 0.5517 17 Toronto 1.0000 0.8125 0.8966 16 Czech_Republic 1.0000 0.3810 0.5517 21 United_States_Department_of_Agriculture 1.0000 0.9333 0.9655 15 Catholic_Church 1.0000 0.9333 0.9655 15 Massachusetts 1.0000 0.9333 0.9655 15 Willie_Taggart 1.0000 0.7059 0.8276 17 Absentia_(film) 1.0000 0.9333 0.9655 15 Minnesota_Wild 1.0000 0.9333 0.9655 15 Americans 0.1429 0.3750 0.2069 8 Paris 1.0000 0.7500 0.8571 16 Syria 0.6250 0.8333 0.7143 12 Illinois 1.0000 0.8667 0.9286 15 Microsoft_Windows 1.0000 0.8667 0.9286 15 Syria_national_football_team 1.0000 0.5882 0.7407 17 Rome 1.0000 0.9286 0.9630 14 New_Zealand_national_cricket_team 0.8462 0.7857 0.8148 14 National_Basketball_Association 1.0000 0.8000 0.8889 15 San_Francisco 1.0000 0.5000 0.6667 18 Ivory_Coast 1.0000 0.9286 0.9630 14 Alabama 0.7857 0.8462 0.8148 13 Differential_scanning_calorimetry 1.0000 0.0385 0.0741 26 Monash_University 1.0000 0.5882 0.7407 17 Pelican_Bay_State_Prison 1.0000 0.9286 0.9630 14 Japan_national_football_team 0.9231 0.9231 0.9231 13 Houston 0.9000 0.5625 0.6923 16 Portugal 0.9091 0.6667 0.7692 15 Tokyo 1.0000 1.0000 1.0000 13 Federal_Bureau_of_Investigation 1.0000 1.0000 1.0000 13 World_Wide_Web 1.0000 0.3000 0.4615 20 Bre-X 1.0000 1.0000 1.0000 13 Peru,_Illinois 1.0000 1.0000 1.0000 13 Ottawa,_Illinois 1.0000 1.0000 1.0000 13 Streator,_Illinois 1.0000 1.0000 1.0000 13 Mendota,_Illinois 1.0000 1.0000 1.0000 13 Spring_Valley,_Illinois 1.0000 1.0000 1.0000 13 Earlville,_Illinois 1.0000 1.0000 1.0000 13 Granville,_Illinois 1.0000 1.0000 1.0000 13 Ladd,_Illinois 1.0000 1.0000 1.0000 13 Marseilles,_Illinois 1.0000 1.0000 1.0000 13 Oglesby,_Illinois 1.0000 1.0000 1.0000 13 Seneca,_Illinois 1.0000 1.0000 1.0000 13 Sheridan,_Illinois 1.0000 1.0000 1.0000 13 Walnut,_Illinois 1.0000 1.0000 1.0000 13 Wenona,_Illinois 1.0000 1.0000 1.0000 13 Wyanet,_Illinois 1.0000 1.0000 1.0000 13 Grand_Ridge,_Illinois 1.0000 1.0000 1.0000 13 Leland,_Illinois 1.0000 1.0000 1.0000 13 Sheffield,_Illinois 1.0000 1.0000 1.0000 13 Tiskilwa,_Illinois 1.0000 1.0000 1.0000 13 Tonica,_Illinois 1.0000 1.0000 1.0000 13 Citigroup 1.0000 0.8571 0.9231 14 City 0.5000 0.0417 0.0769 24 NCAA_Division_I_men's_basketball_tournament 1.0000 0.4444 0.6154 18 Film 1.0000 0.7333 0.8462 15 United_Arab_Emirates 1.0000 0.6667 0.8000 15 David_Campese 1.0000 0.9231 0.9600 13 Wales_national_rugby_union_team 0.6667 1.0000 0.8000 10 Michigan 1.0000 0.5625 0.7200 16 Princeton,_Illinois 0.9231 1.0000 0.9600 12 Christmas 0.3125 0.5556 0.4000 9 The_New_York_Times 0.9000 0.6000 0.7200 15 Google 0.9167 0.8462 0.8800 13 Estes_Park,_Colorado 0.9231 1.0000 0.9600 12 Malaysia 1.0000 0.8462 0.9167 13 Democratic_Republic_of_the_Congo 1.0000 0.2000 0.3333 20 Mumbai 1.0000 0.8462 0.9167 13 Václav_Havel 1.0000 1.0000 1.0000 12 Arabic 1.0000 0.8462 0.9167 13 FIS_Alpine_Ski_World_Cup 1.0000 1.0000 1.0000 12 North_Korea 1.0000 1.0000 1.0000 12 YouTube 1.0000 1.0000 1.0000 12 Connecticut 1.0000 0.8462 0.9167 13 Desktop_computer 1.0000 1.0000 1.0000 12 Leicestershire_County_Cricket_Club 1.0000 1.0000 1.0000 12 Warwickshire_County_Cricket_Club 1.0000 1.0000 1.0000 12 Somerset_County_Cricket_Club 1.0000 1.0000 1.0000 12 Cambridge,_Massachusetts 1.0000 1.0000 1.0000 12 NCCA_Knockout_Trophy 1.0000 1.0000 1.0000 12 Denmark 1.0000 0.7692 0.8696 13 Chile 1.0000 0.9167 0.9565 12 Newmont 1.0000 0.6429 0.7826 14 New_Jersey 0.9091 0.8333 0.8696 12 Taiwan 0.9167 1.0000 0.9565 11 Rangers_F.C. 0.8333 0.9091 0.8696 11 Seattle 1.0000 0.7692 0.8696 13 Rensselaer,_New_York 0.9000 0.6923 0.7826 13 HIV/AIDS 1.0000 0.9167 0.9565 12 Mississippi 0.5385 0.7000 0.6087 10 Internet 0.4000 0.7500 0.5217 8 AutoCAD 0.9167 1.0000 0.9565 11 Human 1.0000 0.7692 0.8696 13 England_national_rugby_union_team 0.3750 1.0000 0.5455 6 Greenwich_Mean_Time 1.0000 1.0000 1.0000 11 Arizona 1.0000 0.6923 0.8182 13 North_America 1.0000 0.6923 0.8182 13 Bill_Clinton 1.0000 0.8333 0.9091 12 Abu_Dhabi 1.0000 0.8333 0.9091 12 DePue,_Illinois 1.0000 1.0000 1.0000 11 JavaScript 1.0000 1.0000 1.0000 11 Watt 0.0000 0.0000 0.0000 22 Financial_quote 1.0000 0.4667 0.6364 15 Central_Europe 1.0000 0.5714 0.7273 14 Yorkshire_County_Cricket_Club 1.0000 1.0000 1.0000 11 Essex_County_Cricket_Club 1.0000 1.0000 1.0000 11 Bowling_average 1.0000 1.0000 1.0000 11 England_national_rugby_league_team 1.0000 1.0000 1.0000 11 New_Zealand_national_rugby_union_team 0.6667 0.8889 0.7619 9 Wales 0.8571 0.4286 0.5714 14 Argentina 1.0000 0.9091 0.9524 11 Madeleine_Albright 1.0000 0.5000 0.6667 14 John_Major 1.0000 0.6154 0.7619 13 Yangon 1.0000 0.9091 0.9524 11 Minnesota 0.8571 0.4286 0.5714 14 Iran 0.8182 0.9000 0.8571 10 Timbaland 1.0000 0.0500 0.0952 20 Tennessee 0.9000 0.8182 0.8571 11 Polypropylene 0.0000 0.0000 0.0000 21 IBM 0.9091 1.0000 0.9524 10 Nissan_Juke 1.0000 0.5000 0.6667 14 Melbourne 1.0000 0.6667 0.8000 12 Detroit_Pistons 1.0000 0.3333 0.5000 15 Moscow 0.6667 1.0000 0.8000 8 Arsenal_F.C. 1.0000 1.0000 1.0000 10 Wolverhampton_Wanderers_F.C. 1.0000 0.4286 0.6000 14 African_Americans 0.9000 0.9000 0.9000 10 Oregon 1.0000 0.8182 0.9000 11 Pennsylvania 1.0000 0.8182 0.9000 11 Queen's_Park_F.C. 0.8182 1.0000 0.9000 9 Linux 1.0000 0.6667 0.8000 12 Printed_circuit_board 1.0000 1.0000 1.0000 10 Brian_Cowen 1.0000 0.3333 0.5000 15 Hewlett-Packard 1.0000 0.8182 0.9000 11 LSU_Tigers_football 1.0000 0.2500 0.4000 16 Northfield,_Vermont 1.0000 0.8182 0.9000 11 Kentucky 0.6000 0.6000 0.6000 10 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