diff --git "a/exp/log/log-train-2022-06-17-19-29-44" "b/exp/log/log-train-2022-06-17-19-29-44"
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+2022-06-17 19:29:44,672 INFO [train.py:522] Training started
+2022-06-17 19:29:44,673 INFO [train.py:523] {'max_sent_len': 200, 'sos_id': 1, 'eos_id': 1, 'blank_id': 0, 'lr': 0.001, 'weight_decay': 1e-06, 'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 200, 'reset_interval': 2000, 'valid_interval': 10000, 'env_info': {'k2-version': '1.15.1', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': 'c11c0b70e91d24935514b73d6bffddc8f5a07932', 'k2-git-date': 'Sat Jun 4 06:06:20 2022', 'lhotse-version': '1.3.0.dev+git.4198446.clean', 'torch-version': '1.11.0', 'torch-cuda-available': True, 'torch-cuda-version': '11.3', 'python-version': '3.8', 'icefall-git-branch': 'rnn-lm', 'icefall-git-sha1': '71a9c33-clean', 'icefall-git-date': 'Thu Jun 16 09:17:51 2022', 'icefall-path': '/workspace/package/k2-icefall', 'k2-path': '/opt/conda/lib/python3.8/site-packages/k2-1.15.1.dev20220607+cuda11.3.torch1.11.0-py3.8-linux-x86_64.egg/k2/__init__.py', 'lhotse-path': '/opt/conda/lib/python3.8/site-packages/lhotse/__init__.py',}, 'world_size': 8, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 30, 'start_epoch': 0, 'exp_dir': PosixPath('models/rnn_tied_2048_3'), 'use_fp16': True, 'batch_size': 400, 'use_ddp_launch': False, 'lm_data': 'data/lm_training_bpe_500/sorted_lm_data.pt', 'lm_data_valid': 'data/lm_training_bpe_500/sorted_lm_data-valid.pt', 'vocab_size': 500, 'embedding_dim': 2048, 'hidden_dim': 2048, 'num_layers': 3, 'tie_weights': True, 'seed': 42}
+2022-06-17 19:29:44,674 INFO [train.py:534] Device: cuda:1
+2022-06-17 19:29:44,674 INFO [train.py:536] About to create model
+2022-06-17 19:29:45,489 INFO [model.py:69] Tying weights
+2022-06-17 19:29:52,915 INFO [train.py:562] Loading LM training data from data/lm_training_bpe_500/sorted_lm_data.pt
+2022-06-17 19:30:03,886 INFO [train.py:569] Loading LM validation data from data/lm_training_bpe_500/sorted_lm_data-valid.pt
+2022-06-17 19:30:07,777 INFO [train.py:445] Epoch 0, batch 0, loss[loss=6.342, over 62800.00 frames., ppl: 567.7399702975538] tot_loss[loss=6.342, over 62800.00 frames., ppl: 567.7399702975538], batch size: 402022-06-17 19:30:07,796 INFO [distributed.py:948] Reducer buckets have been rebuilt in this iteration.
+202022-06-17 19:31:23,202 INFO [train.py:445] Epoch 0, batch 200, loss[loss=2.991, over 48400.00 frames., ppl: 19.901776785132192] tot_loss[loss=3.942, over 3475156.61 frames., ppl: 51.524644189314984], batch size:2022022-06-17 19:32:36,027 INFO [train.py:445] Epoch 0, batch 400, loss[loss=2.85, over 23200.00 frames., ppl: 17.283567526044227] tot_loss[loss=3.331, over 5913806.64 frames., ppl: 27.96602045482732], batch size: 40222022-06-17 19:33:45,193 INFO [train.py:445] Epoch 0, batch 600, loss[loss=2.714, over 67600.00 frames., ppl: 15.089500455624256] tot_loss[loss=3.146, over 8237530.03 frames., ppl: 23.25260230821041], batch size: 422022-06-17 19:34:57,809 INFO [train.py:445] Epoch 0, batch 800, loss[loss=2.67, over 72000.00 frames., ppl: 14.44195607871795] tot_loss[loss=3.038, over 10583979.60 frames., ppl: 20.871868388376797], batch size: 40020222022-06-17 19:36:10,608 INFO [train.py:445] Epoch 0, batch 1000, loss[loss=2.646, over 70400.00 frames., ppl: 14.099971312168739] tot_loss[loss=2.966, over 12333393.26 frames., ppl: 19.41948866641584], batch size: 42202022-06-17 19:37:25,487 INFO [train.py:445] Epoch 0, batch 1200, loss[loss=2.598, over 31200.00 frames., ppl: 13.433342784083948] tot_loss[loss=2.899, over 13786422.71 frames., ppl: 18.154534537883773], batch size2022022-06-17 19:38:43,869 INFO [train.py:445] Epoch 0, batch 1400, loss[loss=2.561, over 32000.00 frames., ppl: 12.944520969020363] tot_loss[loss=2.836, over 16215664.46 frames., ppl: 17.040913619894038], batch size: 2022-02022-06-17 19:39:56,961 INFO [train.py:445] Epoch 0, batch 1600, loss[loss=2.625, over 14400.00 frames., ppl: 13.811323839828452] tot_loss[loss=2.808, over 17033833.13 frames., ppl: 16.57213474325594], batch siz2022-2022-06-17 19:41:14,727 INFO [train.py:445] Epoch 0, batch 1800, loss[loss=2.538, over 44400.00 frames., ppl: 12.651287757560597] tot_loss[loss=2.761, over 19000530.44 frames., ppl: 15.815054068944413], batch size20222022-06-17 19:42:27,223 INFO [train.py:445] Epoch 0, batch 2000, loss[loss=2.521, over 50800.00 frames., ppl: 12.44378999335016] tot_loss[loss=2.739, over 19864371.77 frames., ppl: 15.471014795828545], batch size: 2022-02022-06-17 19:43:40,567 INFO [train.py:445] Epoch 0, batch 2200, loss[loss=2.531, over 55200.00 frames., ppl: 12.57191565395381] tot_loss[loss=2.704, over 21284845.41 frames., ppl: 14.934200989817901], batch size:20222022-06-17 19:44:53,538 INFO [train.py:445] Epoch 0, batch 2400, loss[loss=2.528, over 28000.00 frames., ppl: 12.526767384208696] tot_loss[loss=2.688, over 22284157.86 frames., ppl: 14.698415851290376], batch size: 2022-06-17 19:46:02,827 INFO [train.py:445] Epoch 0, batch 2600, loss[loss=2.468, over 27600.00 frames., ppl: 11.795618484507395] tot_loss[loss=2.668, over 23099220.71 frames., ppl: 14.406159681597497], batch size: 40022022-06-17 19:47:15,312 INFO [train.py:445] Epoch 0, batch 2800, loss[loss=2.477, over 24800.00 frames., ppl: 11.90011809589156] tot_loss[loss=2.651, over 23870530.51 frames., ppl: 14.169610613306531], batch size: 40022022022-06-17 19:48:32,317 INFO [train.py:445] Epoch 0, batch 3000, loss[loss=2.529, over 18400.00 frames., ppl: 12.544842347390436] tot_loss[loss=2.635, over 25006518.09 frames., ppl: 13.942038651294501], batch size:2022-06-2022-06-17 19:49:45,468 INFO [train.py:445] Epoch 0, batch 3200, loss[loss=2.584, over 18400.00 frames., ppl: 13.25647673519082] tot_loss[loss=2.631, over 25421209.21 frames., ppl: 13.892072342305497], batch s22022-06-17 19:51:00,098 INFO [train.py:445] Epoch 0, batch 3400, loss[loss=2.455, over 56133.00 frames., ppl: 11.645292598490398] tot_loss[loss=2.608, over 25880138.06 frames., ppl: 13.569897352080755], batch size: 4022022-06-17 19:52:12,069 INFO [train.py:445] Epoch 0, batch 3600, loss[loss=2.441, over 29600.00 frames., ppl: 11.480019713664499] tot_loss[loss=2.594, over 27458443.88 frames., ppl: 13.379676396205822], batch size: 4020222022-06-17 19:53:24,458 INFO [train.py:445] Epoch 0, batch 3800, loss[loss=2.441, over 51600.00 frames., ppl: 11.481637277652691] tot_loss[loss=2.586, over 27344598.85 frames., ppl: 13.27538048869824], batch size: 2022-06-17 19:54:38,499 INFO [train.py:445] Epoch 0, batch 4000, loss[loss=2.441, over 69600.00 frames., ppl: 11.480084771528904] tot_loss[loss=2.573, over 27759530.77 frames., ppl: 13.108640061047378], batch size: 400
+20202022-06-17 19:55:50,440 INFO [train.py:445] Epoch 0, batch 4200, loss[loss=2.398, over 46000.00 frames., ppl: 10.998263888953442] tot_loss[loss=2.567, over 27680751.64 frames., ppl: 13.028348863553612], batch size:202022-06-17 19:57:03,164 INFO [train.py:445] Epoch 0, batch 4400, loss[loss=2.462, over 27600.00 frames., ppl: 11.734017175909631] tot_loss[loss=2.557, over 28889006.47 frames., ppl: 12.890850675199726], batch size: 420222022-06-17 19:58:12,896 INFO [train.py:445] Epoch 0, batch 4600, loss[loss=2.407, over 37600.00 frames., ppl: 11.096477800425598] tot_loss[loss=2.55, over 28508984.51 frames., ppl: 12.80750864682976], batch size: 4220222022-06-17 19:59:29,180 INFO [train.py:445] Epoch 0, batch 4800, loss[loss=2.414, over 78800.00 frames., ppl: 11.179437817352547] tot_loss[loss=2.542, over 29001601.51 frames., ppl: 12.699036026862517], batch siz2022202022-06-17 20:00:41,991 INFO [train.py:445] Epoch 0, batch 5000, loss[loss=2.444, over 28000.00 frames., ppl: 11.514082720121202] tot_loss[loss=2.536, over 29317632.64 frames., ppl: 12.63295167347938], batch size:202022-06-17 20:01:54,114 INFO [train.py:445] Epoch 0, batch 5200, loss[loss=2.387, over 34800.00 frames., ppl: 10.885415823515665] tot_loss[loss=2.528, over 29965632.38 frames., ppl: 12.53288017444221], batch size:2022-062022-06-17 20:03:05,228 INFO [train.py:445] Epoch 0, batch 5400, loss[loss=2.411, over 39200.00 frames., ppl: 11.144790599630362] tot_loss[loss=2.524, over 29593926.91 frames., ppl: 12.476577117757797], batch si2022-06-17 20:04:16,996 INFO [train.py:445] Epoch 0, batch 5600, loss[loss=2.402, over 18400.00 frames., ppl: 11.040821392082467] tot_loss[loss=2.518, over 29751946.11 frames., ppl: 12.398245403641106], batch size: 400
+2022202022-06-17 20:05:29,371 INFO [train.py:445] Epoch 0, batch 5800, loss[loss=2.378, over 39600.00 frames., ppl: 10.786156370123818] tot_loss[loss=2.511, over 30279354.45 frames., ppl: 12.322442828046375], batch s2022-06-12022-06-17 20:06:44,174 INFO [train.py:445] Epoch 0, batch 6000, loss[loss=2.443, over 11600.00 frames., ppl: 11.509638011889031] tot_loss[loss=2.507, over 30394903.00 frames., ppl: 12.271666711617526], batch s2022-06-17 20:08:00,302 INFO [train.py:445] Epoch 0, batch 6200, loss[loss=2.372, over 31600.00 frames., ppl: 10.723029203342643] tot_loss[loss=2.503, over 30272527.89 frames., ppl: 12.215689193230718], batch size: 400
+2022-2022-06-17 20:09:14,034 INFO [train.py:445] Epoch 0, batch 6400, loss[loss=2.417, over 66000.00 frames., ppl: 11.208111800987536] tot_loss[loss=2.498, over 30348708.07 frames., ppl: 12.164066328826616], batch size:2022-06-12022-06-17 20:10:29,661 INFO [train.py:445] Epoch 0, batch 6600, loss[loss=2.35, over 32400.00 frames., ppl: 10.484523040410739] tot_loss[loss=2.495, over 30647856.84 frames., ppl: 12.120460865286091], batch s2022022022-06-17 20:11:44,707 INFO [train.py:445] Epoch 0, batch 6800, loss[loss=2.354, over 35200.00 frames., ppl: 10.529630404134055] tot_loss[loss=2.489, over 30591582.75 frames., ppl: 12.05465525245117], batch si2022-06-17 20:12:54,412 INFO [train.py:445] Epoch 0, batch 7000, loss[loss=2.417, over 20000.00 frames., ppl: 11.212599326960811] tot_loss[loss=2.484, over 31033632.08 frames., ppl: 11.984471246050592], batch size: 400
+2022202022022-06-17 20:14:08,781 INFO [train.py:445] Epoch 0, batch 7200, loss[loss=2.396, over 22400.00 frames., ppl: 10.979373928336866] tot_loss[loss=2.481, over 31015062.10 frames., ppl: 11.956192145626371], batch 2022-062022-06-17 20:15:22,364 INFO [train.py:445] Epoch 0, batch 7400, loss[loss=2.391, over 34800.00 frames., ppl: 10.928417579755134] tot_loss[loss=2.476, over 31733817.54 frames., ppl: 11.888745304140173], batch siz2022-2022022-06-17 20:16:34,920 INFO [train.py:445] Epoch 0, batch 7600, loss[loss=2.354, over 44400.00 frames., ppl: 10.532048644537701] tot_loss[loss=2.475, over 31182390.74 frames., ppl: 11.885592508228061], batch si22022-02022-06-17 20:17:50,848 INFO [train.py:445] Epoch 0, batch 7800, loss[loss=2.336, over 44400.00 frames., ppl: 10.337492955943395] tot_loss[loss=2.47, over 31496824.73 frames., ppl: 11.823806932303256], batch siz222022-06-17 20:19:01,247 INFO [train.py:445] Epoch 0, batch 8000, loss[loss=2.379, over 20800.00 frames., ppl: 10.791001881116747] tot_loss[loss=2.468, over 31033737.64 frames., ppl: 11.800629681176417], batch size: 202022022-06-17 20:20:14,314 INFO [train.py:445] Epoch 0, batch 8200, loss[loss=2.374, over 44800.00 frames., ppl: 10.745191547346089] tot_loss[loss=2.464, over 31835953.11 frames., ppl: 11.750685045372133], batch size:2022022-06-17 20:21:27,266 INFO [train.py:445] Epoch 0, batch 8400, loss[loss=2.483, over 18800.00 frames., ppl: 11.973261903793103] tot_loss[loss=2.462, over 31208140.20 frames., ppl: 11.73206504406887], batch size: 422022022-06-17 20:22:38,201 INFO [train.py:445] Epoch 0, batch 8600, loss[loss=2.382, over 20800.00 frames., ppl: 10.822900304909492] tot_loss[loss=2.461, over 31073585.52 frames., ppl: 11.714415933551958], batch size: 2022-062022-06-17 20:23:51,717 INFO [train.py:445] Epoch 0, batch 8800, loss[loss=2.376, over 26800.00 frames., ppl: 10.761065617224823] tot_loss[loss=2.457, over 31522297.34 frames., ppl: 11.671366951920465], batch s2022022-06-17 20:25:05,002 INFO [train.py:445] Epoch 0, batch 9000, loss[loss=2.361, over 44020.00 frames., ppl: 10.601235072068189] tot_loss[loss=2.456, over 31431984.12 frames., ppl: 11.65636371271239], batch size: 22022022-06-17 20:26:19,978 INFO [train.py:445] Epoch 0, batch 9200, loss[loss=2.363, over 30400.00 frames., ppl: 10.625320452477174] tot_loss[loss=2.453, over 31312025.68 frames., ppl: 11.626013563009211], batch size: 2022-06-17 20:27:33,297 INFO [train.py:445] Epoch 0, batch 9400, loss[loss=2.338, over 53600.00 frames., ppl: 10.361995382368436] tot_loss[loss=2.45, over 31532653.00 frames., ppl: 11.58561676203178], batch size: 400
+2022022-06-17 20:28:47,214 INFO [train.py:445] Epoch 0, batch 9600, loss[loss=2.366, over 34400.00 frames., ppl: 10.654160201524455] tot_loss[loss=2.448, over 31253316.70 frames., ppl: 11.566870595608004], batch size: 20222022-06-17 20:29:58,184 INFO [train.py:445] Epoch 0, batch 9800, loss[loss=2.377, over 29600.00 frames., ppl: 10.776627247858617] tot_loss[loss=2.447, over 31403023.80 frames., ppl: 11.548903109222753], batch size202220202022-06-17 20:31:12,777 INFO [train.py:445] Epoch 0, batch 10000, loss[loss=2.38, over 31200.00 frames., ppl: 10.80947351211272] tot_loss[loss=2.44, over 32495124.75 frames., ppl: 11.477261478539962], batch size: 400
+2022-06-17 20:31:12,777 INFO [train.py:469] Computing validatio2022-06-17 202022-06-17 20:31:12,959 INFO [train.py:480] Epoch 0, validation: loss=2.445, over 211809.00 frames., ppl: 11.527202220222022-06-17 20:32:23,481 INFO [train.py:445] Epoch 0, batch 10200, loss[loss=2.386, over 41205.00 frames., ppl: 10.86784448336951] tot_loss[loss=2.442, over 31471874.92 frames., ppl: 11.500442490875063], batch siz202022-06-17 20:33:35,880 INFO [train.py:445] Epoch 0, batch 10400, loss[loss=2.334, over 38400.00 frames., ppl: 10.318296313063476] tot_loss[loss=2.439, over 31433959.21 frames., ppl: 11.464418767165652], batch size: 402022-202022-06-17 20:34:47,148 INFO [train.py:445] Epoch 0, batch 10600, loss[loss=2.448, over 13600.00 frames., ppl: 11.56219797201449] tot_loss[loss=2.435, over 32325582.88 frames., ppl: 11.4174168334869], batch size:20222022-06-17 20:36:00,749 INFO [train.py:445] Epoch 0, batch 10800, loss[loss=2.353, over 34000.00 frames., ppl: 10.512724673025268] tot_loss[loss=2.436, over 31127403.22 frames., ppl: 11.4328868846347], batch size: 22022-06-17 20:37:12,164 INFO [train.py:445] Epoch 0, batch 11000, loss[loss=2.351, over 40848.00 frames., ppl: 10.498807207524846] tot_loss[loss=2.433, over 31666290.64 frames., ppl: 11.393833163864528], batch size: 201202022-06-17 20:38:26,236 INFO [train.py:445] Epoch 0, batch 11200, loss[loss=2.379, over 28000.00 frames., ppl: 10.791058914324926] tot_loss[loss=2.433, over 31165998.65 frames., ppl: 11.392268277723636], batch size: 42022022-06-17 20:39:40,981 INFO [train.py:445] Epoch 0, batch 11400, loss[loss=2.319, over 38400.00 frames., ppl: 10.165514472435119] tot_loss[loss=2.43, over 31492335.32 frames., ppl: 11.354570337820917], batch size: 4202022-06-17 20:40:53,640 INFO [train.py:445] Epoch 0, batch 11600, loss[loss=2.355, over 35600.00 frames., ppl: 10.534595776751452] tot_loss[loss=2.428, over 31806417.66 frames., ppl: 11.339619142549497], batch size: 202022-06-17 20:42:11,674 INFO [train.py:445] Epoch 0, batch 11800, loss[loss=2.343, over 42800.00 frames., ppl: 10.415424383951231] tot_loss[loss=2.426, over 31822298.12 frames., ppl: 11.313630958692865], batch size: 402022-06-17 20:43:28,935 INFO [train.py:445] Epoch 0, batch 12000, loss[loss=2.331, over 68800.00 frames., ppl: 10.290100555417977] tot_loss[loss=2.423, over 31911394.94 frames., ppl: 11.285255722760539], batch size: 400
+2022-06-17 20:44:38,770 INFO [train.py:445] Epoch 0, batch 12200, loss[loss=2.311, over 44800.00 frames., ppl: 10.084239628896336] tot_loss[loss=2.423, over 31817478.72 frames., ppl: 11.276555010313876], batch size: 400
+22022022-06-17 20:45:54,646 INFO [train.py:445] Epoch 0, batch 12400, loss[loss=2.392, over 28000.00 frames., ppl: 10.936569405940855] tot_loss[loss=2.42, over 32310774.91 frames., ppl: 11.245898847041742], batch siz2022-02022-06-17 20:47:06,985 INFO [train.py:445] Epoch 0, batch 12600, loss[loss=2.387, over 22000.00 frames., ppl: 10.884230360931337] tot_loss[loss=2.42, over 32121091.58 frames., ppl: 11.250287753874073], batch size:202022-06-17 20:47:36,440 INFO [train.py:445] Epoch 1, batch 0, loss[loss=2.235, over 50800.00 frames., ppl:2022-06-17 20:47:40,943 INFO [train.py:445] Epoch 1, batch 0, loss[loss=2.546, over 6400.00 frames., pp202022-2022-06-17 20:48:50,845 INFO [train.py:445] Epoch 1, batch 200, loss[loss=2.315, over 40400.00 frames., ppl: 10.121005829584126] tot_loss[loss=2.4, over 2798912.43 frames., ppl: 11.025032348016579], batch size2022022-062022-06-17 20:50:03,078 INFO [train.py:445] Epoch 1, batch 400, loss[loss=2.336, over 30800.00 frames., ppl: 10.33813346103983] tot_loss[loss=2.392, over 5724896.24 frames., ppl: 10.93177076184645], batch 2022-202022022-06-17 20:51:20,715 INFO [train.py:445] Epoch 1, batch 600, loss[loss=2.412, over 65075.00 frames., ppl: 11.157814213392589] tot_loss[loss=2.396, over 8077695.09 frames., ppl: 10.977694443963214], batch22022-06-17 20:52:33,804 INFO [train.py:445] Epoch 1, batch 800, loss[loss=2.291, over 43200.00 frames., ppl: 9.881165054930516] tot_loss[loss=2.394, over 10155141.79 frames., ppl: 10.956865192840896], batch size: 42022-02022-06-17 20:53:47,030 INFO [train.py:445] Epoch 1, batch 1000, loss[loss=2.328, over 34800.00 frames., ppl: 10.259231981349442] tot_loss[loss=2.386, over 12986399.09 frames., ppl: 10.869387962388409], batch si2022202222022-2022-06-17 20:54:58,832 INFO [train.py:445] Epoch 1, batch 1200, loss[loss=2.333, over 25600.00 frames., ppl: 10.311656907520652] tot_loss[loss=2.391, over 14378535.96 frames., ppl: 10.923053539197703], b2022-06-12022-06-17 20:56:12,881 INFO [train.py:445] Epoch 1, batch 1400, loss[loss=2.286, over 46000.00 frames., ppl: 9.840160057147765] tot_loss[loss=2.392, over 15953843.31 frames., ppl: 10.934820607520889], batch s20222022-06-17 22022-06-17 20:57:24,404 INFO [train.py:445] Epoch 1, batch 1600, loss[loss=2.357, over 40400.00 frames., ppl: 10.556726367124007] tot_loss[loss=2.386, over 18266061.84 frames., ppl: 10.867267936737418],2022-06-1720222022-06-17 20:58:33,866 INFO [train.py:445] Epoch 1, batch 1800, loss[loss=2.323, over 27600.00 fr2022-06-17 20:58:33,873 INFO [train.py:445] Epoch 1, batch 1800, loss[loss=2.369, over 28000.00 frames.,2022-06-172022-06-17 20:59:47,144 INFO [train.py:445] Epoch 1, batch 2000, loss[loss=2.306, over 45200.00 frames., ppl: 10.03437116763149] tot_loss[loss=2.39, over 20052000.40 frames., ppl: 10.918921955553659], batch s2022-02022022-06-202022-06-17 21:01:00,407 INFO [train.py:445] Epoch 1, batch 2200, loss[loss=2.293, over 34000.00 frames., ppl: 9.901885403671441] tot_loss[loss=2.388, over 21547501.40 frames., ppl: 10.887292298800272]2022-2022-06-17 21:02:13,233 INFO [train.py:445] Epoch 1, batch 2400, loss[loss=2.344, over 17600.00 frames., ppl: 10.424222567582884] tot_loss[loss=2.389, over 22175190.26 frames., ppl: 10.899496451752064], batch size:2022-2022-06-17 21:03:26,111 INFO [train.py:445] Epoch 1, batch 2600, loss[loss=2.307, over 28400.00 frames., ppl: 10.042964972613246] tot_loss[loss=2.39, over 23409053.21 frames., ppl: 10.91525530804466], batch size: 20222022-06-17 21:2022-06-17 21:04:41,042 INFO [train.py:445] Epoch 1, batch 2800, loss[loss=2.343, over 38000.00 frames., ppl: 10.408484588317501] tot_loss[loss=2.388, over 24055595.78 frames., ppl: 10.89013828334256420222022-06-17 21:05:50,446 INFO [train.py:445] Epoch 1, batch 3000, loss[loss=2.331, over 25600.00 frames., 2022-06-17 21:05:50,681 INFO [train.py:445] Epoch 1, batch 3000, loss[loss=2.324, over 52000.00 frames., ppl:2022-2022-06-12022-06-17 21:07:05,997 INFO [train.py:445] Epoch 1, batch 3200, loss[loss=2.327, over 79200.00 frames., ppl: 10.249975260608394] tot_loss[loss=2.392, over 24846346.10 frames., ppl: 10.930582037682692], b2022-06-17 21:08:17,402 INFO [train.py:445] Epoch 1, batch 3400, loss[loss=2.382, over 16800.00 frames., ppl: 12022-06-17 21:08:17,481 INFO [train.py:445] Epoch 1, batch 3400, loss[loss=2.309, over 25600.00 frames., pp20222022-0620222022-06-17 21:09:33,032 INFO [train.py:445] Epoch 1, batch 3600, loss[loss=2.313, over 34000.00 f2022-06-17 21:09:33,117 INFO [train.py:445] Epoch 1, batch 3600, loss[loss=2.334, over 40803.00 frames., 2022-2022-06-17 21:10:48,241 INFO [train.py:445] Epoch 1, batch 3800, loss[loss=2.329, over 27200.00 frames., ppl: 10.264674625296935] tot_loss[loss=2.39, over 26543918.38 frames., ppl: 10.909411467248994], batch size2022-06-17 21:12:02,499 INFO [train.py:445] Epoch 1, batch 4000, loss[loss=2.439, over 18000.00 frames., ppl: 11.457067297720222] tot_loss[loss=2.386, over 27995954.06 frames., ppl: 10.86816515513246], batch size: 400
+2022-022022-06-17 21:13:16,569 INFO [train.py:445] Epoch 1, batch 4200, loss[loss=2.369, over 69566.00 frames., ppl: 10.686261817430651] tot_loss[loss=2.388, over 27488575.27 frames., ppl: 10.893216325096523], batch s2022-06-17 21:14:30,653 INFO [train.py:445] Epoch 1, batch 4400, loss[loss=2.317, over 28800.00 frames., ppl: 10.144711979295776] tot_loss[loss=2.384, over 28973916.98 frames., ppl: 10.846369184245745], batch size: 400
+2022-06-20222022-06-172022-06-17 21:15:40,390 INFO [train.py:445] Epoch 1, batch 4600, loss[loss=2.309, over 41200.00 frames., ppl: 10.067131667279094] tot_loss[loss=2.389, over 28093640.43 frames., ppl: 10.89899068389202022-06-17 21:16:52,42022-06-17 21:16:52,460 INFO [train.py:445] Epoch 1, batch 4800, loss[loss=2.338, over 23600.00 frames., ppl: 10.355686116772068] tot_loss[loss=2.388, over 28389917.74 frames., ppl: 10.8879526632022-06-17 21:12022-06-17 21:18:04,899 INFO [train.py:445] Epoch 1, batch 5000, loss[loss=2.313, over 27200.00 frames., ppl: 10.102659572922914] tot_loss[loss=2.386, over 28735294.58 frames., ppl: 10.872487462519205], 22022-02022-06-17 21:19:17,263 INFO [train.py:445] Epoch 1, batch 5200, loss[loss=2.303, over 60000.00 frames., ppl: 10.00108923354339] tot_loss[loss=2.386, over 28981902.86 frames., ppl: 10.875170014396517], batch siz2022-06-17 21:202022022-06-17 21:20:29,083 INFO [train.py:445] Epoch 1, batch 5400, loss[loss=2.279, over 42800.00 frames., ppl: 9.766381633445512] tot_loss[loss=2.384, over 29537848.22 frames., ppl: 10.84819193297767720220222022-06-172022-06-17 21:21:45,235 INFO [train.py:445] Epoch 1, batch 5600, loss[loss=2.298, over 34000.00 frames., ppl: 9.953336866896166] tot_loss[loss=2.382, over 30258373.68 frames., ppl: 10.822136981628422],20222022-06-17 21:23:02,241 INFO [train.py:445] Epoch 1, batch 5800, loss[loss=2.299, over 40400.00 frames., ppl: 9.96621200440628] tot_loss[loss=2.381, over 30775561.00 frames., ppl: 10.814663963862928], batch size: 402022-06-17 21:24:15,592 INFO [train.py:445] Epoch 1, batch 6000, loss[loss=2.319, over 26800.00 frames., ppl: 10.169796752068631] tot_loss[loss=2.382, over 30184486.51 frames., ppl: 10.831758417748542], batch size: 400
+22022-06-17 21:252022-06-17 21:25:28,300 INFO [train.py:445] Epoch 1, batch 6200, loss[loss=2.32, over 32000.00 frames., ppl: 10.170721826448457] tot_loss[loss=2.382, over 30083277.89 frames., ppl: 10.831769439053762]2022-06-17 21:20222022-062022-06-17 21:26:43,414 INFO [train.py:445] Epoch 1, batch 6400, loss[loss=2.297, over 43600.00 frames., ppl: 9.941312354449517] tot_loss[loss=2.382, over 30102052.73 frames., ppl: 10.831704130820222022-06-17 21:27:56,988 INFO [train.py:445] Epoch 1, batch 6600, loss[loss=2.316, over 30000.00 frames., ppl: 10.130303213058026] tot_loss[loss=2.381, over 30714859.75 frames., ppl: 10.811609089965332], batch size: 20222022-06-17 21:29:05,756 INFO [train.py:445] Epoch 1, batch 6800, loss[loss=2.276, over 18400.00 frames., ppl: 9.737648902981102] tot_loss[loss=2.381, over 30712306.94 frames., ppl: 10.812328709085893], batch size:202022-06-17 21:2022-06-17 21:30:21,681 INFO [train.py:445] Epoch 1, batch 7000, loss[loss=2.319, over 28800.00 frames., ppl: 10.163221809654774] tot_loss[loss=2.378, over 31099376.90 frames., ppl: 10.787555037177118]202202022-06-17 212022-06-17 21:31:34,273 INFO [train.py:445] Epoch 1, batch 7200, loss[loss=2.303, over 38800.00 frames., ppl: 10.00386953502638] tot_loss[loss=2.378, over 31272855.15 frames., ppl: 10.7789835627304742022-02022-06-17 212022-06-17 21:32:47,022 INFO [train.py:445] Epoch 1, batch 7400, loss[loss=2.313, over 37200.00 frames., ppl: 10.105650855805933] tot_loss[loss=2.376, over 31678629.86 frames., ppl: 10.76496376759524220222022-06-17202202022022-06-17 21:34:01,861 INFO [train.py:445] Epoch 1, batch 7600, loss[loss=2.327, over 22800.00 frames., ppl: 10.244656684208703] tot_loss[loss=2.379, over 31135987.88 frames., ppl: 10.795800520042022-06-17 21:35:12022-06-17 21:35:13,701 INFO [train.py:445] Epoch 1, batch 7800, loss[loss=2.327, over 26000.00 frames., ppl: 10.245860784034662] tot_loss[loss=2.376, over 31529751.74 frames., ppl: 10.76210882437363],2022-06-17 21:36:28,290 INFO [train.py:445] Epoch 1, batch 8000, loss[loss=2.295, over 34800.00 frames., ppl: 9.924467204437494] tot_loss[loss=2.374, over 32246480.18 frames., ppl: 10.74353296390484], batch size: 400
+2022-06-17 21:32022-06-17 21:37:37,333 INFO [train.py:445] Epoch 1, batch 8200, loss[loss=2.307, over 48400.00 frames., ppl: 10.044469763325566] tot_loss[loss=2.379, over 31260311.26 frames., ppl: 10.790247579332352], 2022-06-17 21:38:49,035 INFO [train.py:445] Epoch 1, batch 8400, loss[loss=2.289, over 46800.00 frames., ppl: 9.867591578126971] tot_loss[loss=2.375, over 32007907.24 frames., ppl: 10.756006141358803], batch size: 40022022-06-17 21:42022022-06-17 21:40:04,876 INFO [train.py:445] Epoch 1, batch 8600, loss[loss=2.303, over 30000.00 frames., ppl: 10.008574619164113] tot_loss[loss=2.376, over 31431486.44 frames., ppl: 10.7590936772870942022-06-17 21:41:17,041 INFO [train.py:445] Epoch 1, batch 8800, loss[loss=2.301, over 40000.00 frames., ppl: 9.2022-06-17 21:41:17,042 INFO [train.py:445] Epoch 1, batch 8800, loss[loss=2.297, over 40000.00 frames., 2022-06-2022-06-172022-06-17 21:42:29,439 INFO [train.py:445] Epoch 1, batch 9000, loss[loss=2.324, over 42411.00 frames., ppl: 10.216358788658052] tot_loss[loss=2.375, over 31426962.60 frames., ppl: 10.755889465504424]2022-06-17 21:43:44,313 INFO [train.py:445] Epoch 1, batch 9200, loss[loss=2.263, over 25600.00 frames., ppl: 9.616334228858546] tot_loss[loss=2.373, over 32228525.09 frames., ppl: 10.734276421555045], batch size: 400
+2022-062022-06-17 21:2022-06-17 21:44:57,724 INFO [train.py:445] Epoch 1, batch 9400, loss[loss=2.347, over 23200.00 frames., ppl: 10.452411720307184] tot_loss[loss=2.376, over 31349073.59 frames., ppl: 10.763747992892022-06-17 21:46:2022-06-17 21:46:11,342 INFO [train.py:445] Epoch 1, batch 9600, loss[loss=2.293, over 43215.00 frames., ppl: 9.903272442091478] tot_loss[loss=2.376, over 31875684.42 frames., ppl: 10.757281892716609],2022-06-12022-06-2022-06-17 21:47:23,278 INFO [train.py:445] Epoch 1, batch 9800, loss[loss=2.322, over 27600.2022-06-17 21:47:23,403 INFO [train.py:445] Epoch 1, batch 9800, loss[loss=2.278, over 42000.00 frames., ppl2022-06-172022-06-2022-06-17 21:48:37,677 INFO [train.py:445] Epoch 1, batch 10000, loss[loss=2.304, over 50400.00 frames., ppl: 10.01170241502427] tot_loss[loss=2.372, over 32046450.72 frames., ppl: 10.720205680511432], batch size: 400
+2022-06-17 21:48:37,678 INFO [train.py:469] Computin2022-06-17 21:48:37,863 INFO [train.py:480] Epoch 1, validation: loss=2.4, over 211809.00 frames., ppl: 11.025238452207521
+2022-06-17 21:49:52,764 INFO [train.py:445] Epoch 1, batch 10200, loss[loss=2.303, over 34000.00 frames., ppl: 10.000798918147826] tot_loss[loss=2.372, over 31969534.11 frames., ppl: 10.721714013874626], batch size: 40022022-06-17 21:51:04,082022-06-17 21:51:04,115 INFO [train.py:445] Epoch 1, batch 10400, loss[loss=2.306, over 24400.00 frames., ppl: 10.029663329827754] tot_loss[loss=2.372, over 31704629.46 frames., ppl: 10.7204504691202022-06-17 21:52:17,300 INFO [train.py:445] Epoch 1, batch 10600, loss[loss=2.294, over 29200.00 frames., ppl: 9.917619552968263] tot_loss[loss=2.372, over 31987704.26 frames., ppl: 10.714489087373794], batch size: 40202022-06-17 21:2022-06-17 21:53:31,291 INFO [train.py:445] Epoch 1, batch 10800, loss[loss=2.333, over 25600.00 frames., ppl: 10.309886946218581] tot_loss[loss=2.371, over 31931259.08 frames., ppl: 10.710432835642244], 22022-06-17 21:54:44,033 INFO [train.py:445] Epoch 1, batch 11000, loss[loss=2.292, over 57200.00 frames., ppl: 9.890903475422686] tot_loss[loss=2.37, over 32197091.31 frames., ppl: 10.695717985575635], batch size: 400
+2022-06-2022-2022-06-172022-06-17 21:55:59,160 INFO [train.py:445] Epoch 1, batch 11200, loss[loss=2.33, over 22400.00 frames., ppl: 10.278823397609896] tot_loss[loss=2.37, over 31551307.65 frames., ppl: 10.70146261552022-06-17 21:52022-06-172022-06-17 21:57:12,422 INFO [train.py:445] Epoch 1, batch 11400, loss[loss=2.301, over 33600.00 frames., ppl: 9.984907308796386] tot_loss[loss=2.369, over 32185497.72 frames., ppl: 10.6885246932202022-06-17 2022-06-17 21:58:22,705 INFO [train.py:445] Epoch 1, batch 11600, loss[loss=2.238, over 29200.00 frames., ppl: 9.37720287465747] tot_loss[loss=2.372, over 31482327.43 frames., ppl: 10.72109645211052], batc2022022-06-17 21:59:38,722 INFO [train.py:445] Epoch 1, batch 11800, loss[loss=2.386, over 15200.00 frames., ppl: 10.87513206943758] tot_loss[loss=2.368, over 31816337.40 frames., ppl: 10.679764288614747], batch size:2022-20220222022-06-17 22:00:50,905 INFO [train.py:445] Epoch 1, batch 12000, loss[loss=2.35, over 16800.00 fra2022-06-17 22:00:51,031 INFO [train.py:445] Epoch 1, batch 12000, loss[loss=2.295, over 30400.00 frames., pp2022-06-12022-06-17 22:02022-06-17 22:02:06,613 INFO [train.py:445] Epoch 1, batch 12200, loss[loss=2.283, over 29200.00 frames., ppl: 9.806417081292569] tot_loss[loss=2.368, over 32151766.07 frames., ppl: 10.672424525212022-2022-06-17 22:03:20,262 INFO [train.py:445] Epoch 1, batch 12400, loss[loss=2.265, over 60400.00 frames., ppl: 9.634508646236634] tot_loss[loss=2.367, over 31667355.17 frames., ppl: 10.669656351110776], batch size: 2022-062022-02022-06-17 22:04:28,889 INFO [train.py:445] Epoch 1, batch 12600, loss[loss=2.317, over 19200.00 frames., ppl: 10.144409959556649] tot_loss[loss=2.366, over 32036314.11 frames., ppl: 10.658503974249303], ba2022-06-17 22:04:57,682022-06-17 22:04:57,771 INFO [train.py:445] Epoch 2, batch 0, loss[loss=2.334, over 31200.00 frames., ppl: 10.321282058089002] tot_loss[loss=2.334, over 31200.00 frames., ppl: 10.3212820580892022-06-17 22022-02022-06-17 22:06:14,921 INFO [train.py:445] Epoch 2, batch 200, loss[loss=2.247, over 28000.00 frames., ppl: 9.458961619013488] tot_loss[loss=2.358, over 2758374.34 frames., ppl: 10.570724631027], ba2022022-06-17 22:07:26,052 INFO [train.py:445] Epoch 2, batch 400, loss[loss=2.279, over 27200.00 frames., ppl: 9.763482831727053] tot_loss[loss=2.348, over 5542741.14 frames., ppl: 10.463416065965921], batch si2022-2022-2022-06-17 22:08:2022-06-17 22:08:38,303 INFO [train.py:445] Epoch 2, batch 600, loss[loss=2.277, over 46800.00 frames., ppl: 9.748514201563985] tot_loss[loss=2.348, over 8123617.48 frames., ppl: 10.4671337827950202022022-06-2022-06-17 22:09:48,632 INFO [train.py:445] Epoch 2, batch 800, loss[loss=2.328, over 16400.00 frames., ppl: 10.2601644135763] tot_loss[loss=2.352, over 9981199.78 frames., ppl: 10.510105963718607], batc20222022-06-17 22:11:02022-06-17 22:11:02,158 INFO [train.py:445] Epoch 2, batch 1000, loss[loss=2.283, over 26400.00 frames., ppl: 9.808624156687832] tot_loss[loss=2.35, over 12277238.82 frames., ppl: 10.4881999626438202022-02022022-06-17 22:12:16,267 INFO [train.py:445] Epoch 2, batch 1200, loss[loss=2.285, over 34000.00 frames., ppl: 9.82232728056493] tot_loss[loss=2.347, over 14375302.37 frames., ppl: 10.450070689255876], batch 2022-06-17 22:13:28,177 INFO [train.py:445] Epoch 2, batch 1400, loss[loss=2.296, over 22000.00 frames., ppl: 9.93066540913558] tot_loss[loss=2.35, over 15898048.57 frames., ppl: 10.483728751898681], batch size: 400
+2022-06-122022-06-17 22:14:46,017 INFO [train.py:445] Epoch 2, batch 1600, loss[loss=2.294, over 21200.00 frames., ppl: 9.918332035978661] tot_loss[loss=2.348, over 17717374.79 frames., ppl: 10.46374729259533], batch s2022-062022-06-17 22:15:55,789 INFO [train.py:445] Epoch 2, batch 1800, loss[loss=2.345, over 16000.00 frames., ppl: 10.428989240715723] tot_loss[loss=2.349, over 18865756.51 frames., ppl: 10.472145073225676], batch si2022-06-17 22:17:05,715 INFO [train.py:445] Epoch 2, batch 2000, loss[loss=2.322, over 22800.00 frames., ppl: 10.192138004941624] tot_loss[loss=2.347, over 20204998.76 frames., ppl: 10.455830954776992], batch size: 400
+202022-06-17 22:12022-06-17 22:18:23,430 INFO [train.py:445] Epoch 2, batch 2200, loss[loss=2.292, over 56400.00 frames., ppl: 9.898265514379494] tot_loss[loss=2.345, over 21548105.65 frames., ppl: 10.43846692022-06-12022-06-2022-06-17 22:19:34,417 INFO [train.py:445] Epoch 2, batch 2400, loss[loss=2.304, over 35600.00 frames., ppl: 10.014341049984974] tot_loss[loss=2.348, over 22192523.13 frames., ppl: 10.46536431981498], batch 22022-06-12022-06-17 22:20:43,879 INFO [train.py:445] Epoch 2, batch 2600, loss[loss=2.233, over 23200.00 frames., ppl: 9.329086712207937] tot_loss[loss=2.348, over 23225747.34 frames., ppl: 10.463149883288793], batch s2022-06-17 22:21:562022-06-17 22:21:56,508 INFO [train.py:445] Epoch 2, batch 2800, loss[loss=2.293, over 21600.00 frames., ppl: 9.904589422184621] tot_loss[loss=2.345, over 24177358.47 frames., ppl: 10.436769979656656]2022-02022022-06-12022-06-17 22:23:10,313 INFO [train.py:445] Epoch 2, batch 3000, loss[loss=2.274, over 31200.00 frames., ppl: 9.716533083566059] tot_loss[loss=2.345, over 24966192.22 frames., ppl: 10.4324827540593342022-06-2022022-06-17 22:24:24,534 INFO [train.py:445] Epoch 2, batch 3200, loss[loss=2.293, over 29200.00 frames., ppl: 9.90136444759836] tot_loss[loss=2.348, over 25399810.39 frames., ppl: 10.468812832783412], bat2022-2022-06-17 22:25:36,765 INFO [train.py:445] Epoch 2, batch 3400, loss[loss=2.286, over 38800.00 frames., ppl: 9.830726583465191] tot_loss[loss=2.347, over 26156893.44 frames., ppl: 10.457617075957598], batch siz2022-06-17 22:26:52,435 INFO [train.py:445] Epoch 2, batch 3600, loss[loss=2.305, over 25600.00 frames., ppl: 10.02278032456014] tot_loss[loss=2.348, over 26642957.53 frames., ppl: 10.461151250627523], batch size: 40202022-06-17 22:2022-06-17 22:28:05,315 INFO [train.py:445] Epoch 2, batch 3800, loss[loss=2.297, over 43200.00 frames., ppl: 9.947439451598857] tot_loss[loss=2.345, over 27722218.19 frames., ppl: 10.431990381725418], ba2022-06-17 22:29:17,936 INFO [train.py:445] Epoch 2, batch 4000, loss[loss=2.266, over 35600.00 frames., ppl: 9.641308944434076] tot_loss[loss=2.348, over 27620170.20 frames., ppl: 10.462902475748482], batch size: 400
+2022-06-17 22:30:36,929 INFO [train.py:445] Epoch 2, batch 4200, loss[loss=2.272, over 32800.00 frames., ppl: 9.698086440224014] tot_loss[loss=2.348, over 28248361.74 frames., ppl: 10.460492480792501], batch size: 400
+2022-062022-022022-06-17 22:31:50,198 INFO [train.py:445] Epoch 2, batch 4400, loss[loss=2.26, over 40400.00 frames., ppl: 9.57977813223443] tot_loss[loss=2.348, over 28455864.01 frames., ppl: 10.46201303056144], batc2022-06-20222022-06-17 22:33:01,885 INFO [train.py:445] Epoch 2, batch 4600, loss[loss=2.331, over 20400.00 frames., ppl: 10.287715575073781] tot_loss[loss=2.347, over 28962622.08 frames., ppl: 10.453064671752323], bat2022-06-120222022-06-17 22:34:15,825 INFO [train.py:445] Epoch 2, batch 4800, loss[loss=2.289, over 23600.00 frames., ppl: 9.867240597854195] tot_loss[loss=2.347, over 29187287.38 frames., ppl: 10.458430884643912], b2022-06-17 22:32022-06-17 22:35:28,231 INFO [train.py:445] Epoch 2, batch 5000, loss[loss=2.27, over 38000.00 frames., ppl: 9.676167590556704] tot_loss[loss=2.347, over 29435370.79 frames., ppl: 10.454898608542484], bat2022-06-17 22:36:40,220 INFO [train.py:445] Epoch 2, batch 5200, loss[loss=2.274, over 27200.00 frames., ppl: 9.71977903199909] tot_loss[loss=2.348, over 29513531.99 frames., ppl: 10.46484362089486], batch size: 400
+2022-06-17 22:32022-06-17 222022-06-17 22:37:51,468 INFO [train.py:445] Epoch 2, batch 5400, loss[loss=2.305, over 20800.00 frames., ppl: 10.028181343239071] tot_loss[loss=2.346, over 29805149.69 frames., ppl: 10.447522022-06-17 22:2022-06-2022-06-17 22:39:02,259 INFO [train.py:445] Epoch 2, batch 5600, loss[loss=2.247, over 61600.00 frames., ppl: 9.463292369184812] tot_loss[loss=2.348, over 30079923.67 frames., ppl: 10.46518854582362022-06-17 222022-06-17 22:2022-06-17 22:40:15,716 INFO [train.py:445] Epoch 2, batch 5800, loss[loss=2.312, over 28000.00 frames., ppl: 10.09901524427392] tot_loss[loss=2.348, over 29915137.34 frames., ppl: 10.46617032022-06-17 22:41:31,92022-06-17 22:41:32,005 INFO [train.py:445] Epoch 2, batch 6000, loss[loss=2.332, over 22000.00 frames., ppl: 10.29360761209655] tot_loss[loss=2.347, over 30547854.95 frames., ppl: 10.4531563568182022-06-17 22022-06-202022-06-17 22:42:43,223 INFO [train.py:445] Epoch 2, batch 6200, loss[loss=2.25, over 64400.00 frames., ppl: 9.489186178561324] tot_loss[loss=2.346, over 30765125.30 frames., ppl: 10.44431065426552022-06-17 22022-20222022-06-17 22:43:55,048 INFO [train.py:445] Epoch 2, batch 6400, loss[loss=2.275, over 39200.00 frames., ppl: 9.731549047216427] tot_loss[loss=2.346, over 30921833.52 frames., ppl: 10.4421080474822022-06-17 222022-06-17 22:45:11,843 INFO [train.py:445] Epoch 2, batch 6600, loss[loss=2.279, over 34400.00 frames., ppl: 9.768750288247661] tot_loss[loss=2.345, over 30986180.49 frames., ppl: 10.436802595540357], bat2022-06-17 22:2022-06-17 22:46:25,908 INFO [train.py:445] Epoch 2, batch 6800, loss[loss=2.279, over 31600.00 frames., ppl: 9.76293678765426] tot_loss[loss=2.347, over 30820868.26 frames., ppl: 10.456666797950636], batc2022-06-17 22022-06-2022-06-17 22:47:41,051 INFO [train.py:445] Epoch 2, batch 7000, loss[loss=2.276, over 26800.00 frames., ppl: 9.739880664147123] tot_loss[loss=2.343, over 31679026.77 frames., ppl: 10.4119911750612022-06-17 22:48:51,528 IN2022-06-17 22:48:51,594 INFO [train.py:445] Epoch 2, batch 7200, loss[loss=2.286, over 28400.00 frames., ppl: 9.830731183590045] tot_loss[loss=2.347, over 30764656.12 frames., ppl: 10.4542745442022-06-17 22:50:05,987 INFO [train.py:445] Epoch 2, batch 7400, loss[loss=2.275, over 43200.00 frames., ppl: 9.731323746569098] tot_loss[loss=2.345, over 31345268.51 frames., ppl: 10.43126004366619], batch size: 400
+2022-06-17 22:51:17,920222022-06-17 22:51:18,155 INFO [train.py:445] Epoch 2, batch 7600, loss[loss=2.243, over 42000.00 frames., ppl: 9.420398752955798] tot_loss[loss=2.345, over 31324671.42 frames., ppl: 10.4358650922022-06-17 22:52022-06-17 22:52:26,077 INFO [train.py:445] Epoch 2, batch 7800, loss[loss=2.269, over 64000.00 frames., ppl: 9.669877341999417] tot_loss[loss=2.346, over 31123358.06 frames., ppl: 10.446280995505612], b2022-06-17 22:5202022-06-17 2022-06-17 22:53:40,150 INFO [train.py:445] Epoch 2, batch 8000, loss[loss=2.296, over 22400.00 frames., ppl: 9.92951687082677] tot_loss[loss=2.345, over 31319202.94 frames., ppl: 10.4366352022-06-17 22:54:522022-06-17 22:54:52,716 INFO [train.py:445] Epoch 2, batch 8200, loss[loss=2.313, over 23600.00 frames., ppl: 10.1039410380286] tot_loss[loss=2.343, over 31938563.72 frames., ppl: 10.4146094680303832022-06-17 22:56:072022-06-17 22:56:07,854 INFO [train.py:445] Epoch 2, batch 8400, loss[loss=2.276, over 34800.00 frames., ppl: 9.736199477339248] tot_loss[loss=2.349, over 30924550.18 frames., ppl: 10.4700329138870762022-06-17 22:57:2022-06-17 22:57:20,576 INFO [train.py:445] Epoch 2, batch 8600, loss[loss=2.331, over 24400.00 frames., ppl: 10.289192475970355] tot_loss[loss=2.346, over 31339366.04 frames., ppl: 10.445839507037627],2022-06-17 22:58:32,450 INFO [train.py:445] Epoch 2, batch 8800, loss[loss=2.257, over 57600.00 frames., ppl: 9.556324948163297] tot_loss[loss=2.346, over 31473433.12 frames., ppl: 10.4483739411219], batch size: 400
+2022-06-17 22:59:47,999 INFO [train.py:445] Epoch 2, batch 9000, loss[loss=2.303, over 25600.00 frames., ppl: 10.005223431843557] tot_loss[loss=2.346, over 31685629.84 frames., ppl: 10.438509657977308], batch size: 400
+2022-06-17 23:012022-06-17 232022-06-17 23:01:00,250 INFO [train.py:445] Epoch 2, batch 9200, loss[loss=2.269, over 20000.00 frames., ppl: 9.66603281928754] tot_loss[loss=2.348, over 31042227.91 frames., ppl: 10.462022-2022-06-17 23:02:12,843 INFO [train.py:445] Epoch 2, batch 9400, loss[loss=2.249, over 49200.00 frames., ppl: 9.48267362657293] tot_loss[loss=2.345, over 31599130.37 frames., ppl: 10.437934801253668], batch size: 400
+2022-06-17 23:03:22020222022-06-17 23:03:22,099 INFO [train.py:445] Epoch 2, batch 9600, loss[loss=2.286, over 44800.00 frames., ppl: 9.837749553814348] tot_loss[loss=2.344, over 31701816.15 frames., ppl: 10.424551502512022-06-17 23:02022-06-17 23:04:36,417 INFO [train.py:445] Epoch 2, batch 9800, loss[loss=2.288, over 24400.00 frames., ppl: 9.854732338097172] tot_loss[loss=2.347, over 30921765.15 frames., ppl: 10.456203777841191], b2022-06-17 23:05:52,2022-06-17 23:05:52,361 INFO [train.py:445] Epoch 2, batch 10000, loss[loss=2.273, over 28800.00 frames., ppl: 9.705988397187921] tot_loss[loss=2.346, over 31820566.20 frames., ppl: 10.439292625675588], batch size: 400
+2022-06-17 23:05:52,362 INFO [train.py:469] Compu2022-06-17 23:05:52,544 INFO [train.py:480] Epoch 2, validation: loss=2.382, over 211809.00 frames., ppl: 10.822301847783804
+2022-06-17 23:07:05,02022022-06-17 23:07:05,098 INFO [train.py:445] Epoch 2, batch 10200, loss[loss=2.273, over 26800.00 frames., ppl: 9.711439988995457] tot_loss[loss=2.345, over 31387402.01 frames., ppl: 10.4296139912022-06-17 23:02022-06-172022-06-17 23:08:14,777 INFO [train.py:445] Epoch 2, batch 10400, loss[loss=2.254, over 52400.00 frames., ppl: 9.522988342763982] tot_loss[loss=2.344, over 31523592.32 frames., ppl: 10.4254981912022-06-17 23:09:28,2022-06-17 23:09:28,993 INFO [train.py:445] Epoch 2, batch 10600, loss[loss=2.389, over 18400.00 frames., ppl: 10.903901589263954] tot_loss[loss=2.346, over 31361642.74 frames., ppl: 10.2022-06-17 23:2022-06-17 23:12022-06-17 23:10:43,491 INFO [train.py:445] Epoch 2, batch 10800, loss[loss=2.251, over 44400.00 frames., ppl: 9.50168586826925] tot_loss[loss=2.344, over 31433537.28 frames., ppl: 10.419053966198948], b2022-06-17 23:11:55,631 INFO2022-06-17 23:11:55,641 INFO [train.py:445] Epoch 2, batch 11000, loss[loss=2.282, over 24800.00 frames., ppl: 9.799988746888602] tot_loss[loss=2.342, over 31924658.89 frames., ppl: 10.40482022-06-17 23:13:10,0562022-02022-06-17 23:13:10,457 INFO [train.py:445] Epoch 2, batch 11200, loss[loss=2.266, over 56000.00 frames., ppl: 9.638977274724066] tot_loss[loss=2.342, over 32106855.82 frames., ppl: 10.399882022-06-17 23:142022-06-17 22022-06-17 23:14:23,557 INFO [train.py:445] Epoch 2, batch 11400, loss[loss=2.258, over 73600.00 frames., ppl: 9.55968901775683] tot_loss[loss=2.344, over 31451219.95 frames., ppl: 10.419842122022-06-17 23:12022-06-2022-06-17 23:15:37,612 INFO [train.py:445] Epoch 2, batch 11600, loss[loss=2.263, over 32800.00 frames., ppl: 9.613410594522131] tot_loss[loss=2.344, over 32063878.37 frames., ppl: 10.4225344572022-06-17 23:16:51,810 INFO [t2022-06-17 23:16:51,830 INFO [train.py:445] Epoch 2, batch 11800, loss[loss=2.252, over 27200.00 frames., ppl: 9.504032336097078] tot_loss[loss=2.344, over 31640949.85 frames., ppl: 10.422022-06-17 23:12022-06-17 202022022-06-17 23:18:01,760 INFO [train.py:445] Epoch 2, batch 12000, loss[loss=2.362, over 13200.00 frames., ppl: 10.616128258204151] tot_loss[loss=2.345, over 31371141.52 frames., ppl: 10.432022-06-17 23:19:15,720 IN22022-06-17 23:19:16,042 INFO [train.py:445] Epoch 2, batch 12200, loss[loss=2.253, over 42400.00 frames., ppl: 9.514675040983947] tot_loss[loss=2.342, over 31681910.90 frames., ppl: 10.40289342022-06-17 23:22022-06-17 2022-06-17 23:20:28,976 INFO [train.py:445] Epoch 2, batch 12400, loss[loss=2.252, over 41004.00 frames., ppl: 9.506706705568215] tot_loss[loss=2.342, over 32152553.51 frames., ppl: 10.4013037782022-06-17 23:21:46,306 I202022-06-17 23:21:46,648 INFO [train.py:445] Epoch 2, batch 12600, loss[loss=2.312, over 54757.00 frames., ppl: 10.09108502164987] tot_loss[loss=2.342, over 31736039.67 frames., ppl: 10.398410902022-06-17 23:22:16,845 2022-06-17 23:22:17,085 INFO [train.py:445] Epoch 3, batch 0, loss[loss=2.258, over 32000.00 frames., ppl: 9.564236349661572] tot_loss[loss=2.258, over 32000.00 frames., ppl: 9.56423634962022-06-17 232022-06-17 222022-2022-06-17 23:23:37,571 INFO [train.py:445] Epoch 3, batch 200, loss[loss=2.352, over 13600.00 frames., ppl: 10.50862407306553] tot_loss[loss=2.311, over 3371762.20 frames., ppl: 10.0842022-06-17 23:24:52,616 INF2022-06-17 23:24:52,630 INFO [train.py:445] Epoch 3, batch 400, loss[loss=2.239, over 55600.00 frames., ppl: 9.383180289627106] tot_loss[loss=2.321, over 5710795.27 frames., ppl: 10.18984532022-06-17 232022-06-17 23:26:05,950 INFO [train.py:445] Epoch 3, batch 600, loss[loss=2.233, over 44400.00 frames., ppl: 9.327063107311862] tot_loss[loss=2.325, over 8267580.47 frames., ppl: 10.22284024575766], batc2022-06-17 23:27:15,214 INFO [train.py:445] Epoch 3, batch 800, loss[loss=2.226, over 49600.00 frames., ppl: 9.264932846650808] tot_loss[loss=2.328, over 10338188.14 frames., ppl: 10.253125453758217], batch size: 400
+2022-06-17 23:28:30,558 INFO [train.py:445] Epoch 3, batch 1000, loss[loss=2.239, over 42400.00 frames., ppl: 9.385350555746209] tot_loss[loss=2.328, over 12425025.09 frames., ppl: 10.25928594772582], batch size: 400
+2022-06-17 23:29:40,309202022-06-17 23:29:40,368 INFO [train.py:445] Epoch 3, batch 1200, loss[loss=2.291, over 25600.00 frames., ppl: 9.888227515141073] tot_loss[loss=2.324, over 14569837.45 frames., ppl: 10.2145145692022-06-17 232022-06-17 23:302022-06-17 23:30:54,351 INFO [train.py:445] Epoch 3, batch 1400, loss[loss=2.251, over 54000.00 frames., ppl: 9.49500541298333] tot_loss[loss=2.319, over 16671999.77 frames., ppl: 10.166022022-06-17 23:2022-06-172022-2022-06-17 23:32:07,192 INFO [train.py:445] Epoch 3, batch 1600, loss[loss=2.287, over 23200.00 frames., ppl: 9.841698754164309] tot_loss[loss=2.32, over 18164615.19 frames., ppl: 10.174892022-06-17 23:33:21,653 2022-06-17 23:33:21,885 INFO [train.py:445] Epoch 3, batch 1800, loss[loss=2.231, over 44800.00 frames., ppl: 9.311753120159153] tot_loss[loss=2.328, over 18538970.86 frames., ppl: 10.261250416832022-06-17 232022-06-17 222022-06-17 23:34:36,281 INFO [train.py:445] Epoch 3, batch 2000, loss[loss=2.272, over 43600.00 frames., ppl: 9.694460266560148] tot_loss[loss=2.321, over 20986372.41 frames., ppl: 10.18550892022-06-17 23:35:48,784 2022-202022-06-17 23:35:49,152 INFO [train.py:445] Epoch 3, batch 2200, loss[loss=2.235, over 50400.00 frames., ppl: 9.343625260326986] tot_loss[loss=2.324, over 21298126.81 frames., ppl: 10.2162022-06-17 23:37:02,869 INF2022-06-17 23:37:02,873 INFO [train.py:445] Epoch 3, batch 2400, loss[loss=2.244, over 42400.00 frames., ppl: 9.43388091582533] tot_loss[loss=2.323, over 22953223.43 frames., ppl: 10.206333282022-06-12022-06-17 23:32022-062022-06-17 23:38:15,854 INFO [train.py:445] Epoch 3, batch 2600, loss[loss=2.257, over 28400.00 frames., ppl: 9.558285745753224] tot_loss[loss=2.324, over 23422325.62 frames., ppl: 10.2192022-06-12022-06-17 23:39:31,372022-06-17 23:39:31,440 INFO [train.py:445] Epoch 3, batch 2800, loss[loss=2.256, over 35200.00 frames., ppl: 9.54519436132127] tot_loss[loss=2.324, over 24150608.03 frames., ppl: 10.21432022-06-17 23:40:42,793 I2022-06-17 23:40:42,846 INFO [train.py:445] Epoch 3, batch 3000, loss[loss=2.245, over 27200.00 frames., ppl: 9.442039894521479] tot_loss[loss=2.324, over 25265745.71 frames., ppl: 10.219812402022-06-2022-06-17 23:42022022-2022-06-17 23:41:54,193 INFO [train.py:445] Epoch 3, batch 3200, loss[loss=2.221, over 36400.00 frames., ppl: 9.215559323972855] tot_loss[loss=2.326, over 25466921.54 frames., ppl: 10.2322022-06-17 23:43:05,62022-2022-06-17 23:43:05,763 INFO [train.py:445] Epoch 3, batch 3400, loss[loss=2.352, over 16400.00 frames., ppl: 10.505098480051574] tot_loss[loss=2.326, over 26439205.01 frames., ppl: 10.23582492022-02022-06-17 23:2022022-06-17 23:44:18,999 INFO [train.py:445] Epoch 3, batch 3600, loss[loss=2.288, over 26000.00 frames., ppl: 9.858155957973677] tot_loss[loss=2.325, over 27180629.22 frames., ppl: 10.23035716982022-062022-06-17 23:45:33,525 I2022-06-17 23:45:33,532 INFO [train.py:445] Epoch 3, batch 3800, loss[loss=2.341, over 19600.00 frames., ppl: 10.390886557851173] tot_loss[loss=2.325, over 26805021.72 frames., ppl: 10.2320222022-06-17 23:46:46,979 INFO [train.py:445] Epoch 3, batch 4000, loss[loss=2.265, over 31600.00 frames., ppl: 9.631481774636297] tot_loss[loss=2.328, over 27323339.42 frames., ppl: 10.252942853194442], batch size: 2022-06-17 23:48:042022-02022-06-17 23:48:05,151 INFO [train.py:445] Epoch 3, batch 4200, loss[loss=2.278, over 27200.00 frames., ppl: 9.758105647995844] tot_loss[loss=2.325, over 28478621.51 frames., ppl: 10.2285350982022022-06-17 23:49:142022-06-17 23:49:14,124 INFO [train.py:445] Epoch 3, batch 4400, loss[loss=2.331, over 14000.00 frames., ppl: 10.292838701225449] tot_loss[loss=2.326, over 28883959.67 frames., ppl: 10.2340840492022022-06-17 23:50:26,197 INFO [train.py:445] Epoch 3, batch 4600, loss[loss=2.26, over 62400.00 frames., ppl: 9.585832315440333] tot_loss[loss=2.328, over 28604011.95 frames., ppl: 10.261573496573067], batch size: 40202022-06-17 23:51:38,759 INFO2022-06-17 23:51:38,831 INFO [train.py:445] Epoch 3, batch 4800, loss[loss=2.258, over 34400.00 frames., ppl: 9.56633472982374] tot_loss[loss=2.328, over 28463677.96 frames., ppl: 10.25893202022-06-17 232022-06-172022-06-17 23:52:53,971 INFO [train.py:445] Epoch 3, batch 5000, loss[loss=2.275, over 38000.00 frames., ppl: 9.725187418891021] tot_loss[loss=2.328, over 29358352.90 frames., ppl: 10.2577345132022-06-17 23:54:06,092 INFO [train.py:445] Epoch 3, batch 5200, loss[loss=2.349, over 54994.00 frames., ppl: 10.47037025007274] tot_loss[loss=2.329, over 29564287.83 frames., ppl: 10.265578139654751], batch size: 201
+22022-06-17 23:55:19,077 INFO 2022-06-17 23:55:19,205 INFO [train.py:445] Epoch 3, batch 5400, loss[loss=2.236, over 34000.00 frames., ppl: 9.356757458973654] tot_loss[loss=2.328, over 29333209.81 frames., ppl: 10.2520222022-06-17 23:56:31,859 INFO [train.py:445] Epoch 3, batch 5600, loss[loss=2.273, over 25600.00 frames., ppl: 9.712596142650368] tot_loss[loss=2.327, over 30114570.30 frames., ppl: 10.249841323715575], batch siz2022-06-17 23:57:45,570 INFO [train.py:445] Epoch 3, batch 5800, loss[loss=2.245, over 50000.00 frames., ppl: 9.443631745735411] tot_loss[loss=2.329, over 30139380.59 frames., ppl: 10.269162860926187], batch size: 400
+2022-06-17 23:58:56,811 INFO [train.p2022-06-17 23:58:56,995 INFO [train.py:445] Epoch 3, batch 6000, loss[loss=2.243, over 56000.00 frames., ppl: 9.421418212313021] tot_loss[loss=2.329, over 30280904.72 frames., ppl: 2022-06-18 00:00:07,598 INFO [train2022-06-18 00:00:07,612 INFO [train.py:445] Epoch 3, batch 6200, loss[loss=2.309, over 16000.00 frames., ppl: 10.061019052748406] tot_loss[loss=2.329, over 29808830.45 frames., ppl: 12022-06-18 00:01:202022-06-18 00:012022-06-18 00:01:20,113 INFO [train.py:445] Epoch 3, batch 6400, loss[loss=2.276, over 37200.00 frames., ppl: 9.733833233543002] tot_loss[loss=2.326, over 30986946.12 frames., ppl: 102022-06-2022-06-18 00:02:33,820 INFO [train.py:445] Epoch 3, batch 6600, loss[loss=2.236, over 49200.00 frames., ppl: 9.353450973816097] tot_loss[loss=2.328, over 30665967.61 frames., ppl: 10.260851105737567], batch siz2022-062022-02022-2022-06-18 00:03:47,464 INFO [train.py:445] Epoch 3, batch 6800, loss[loss=2.294, over 22000.00 frames., ppl: 9.913759598029364] tot_loss[loss=2.329, over 30591704.99 frames., ppl: 10.2674528892281622022-06-12022-020222022-06-18 00:05:00,586 INFO [train.py:445] Epoch 3, batch 7000, loss[loss=2.275, over 27200.00 frames., ppl: 9.72529431503427] tot_loss[loss=2.329, over 30712769.93 frames., ppl: 10.2681598340065552022-06-182022-02022022-06-18 00:06:14,925 INFO [train.py:445] Epoch 3, batch 7200, loss[loss=2.325, over 17600.00 frames., ppl: 10.224725999699688] tot_loss[loss=2.33, over 30621461.57 frames., ppl: 10.277645262484912022-06-18 00:07:29,957 INFO [train.py:445] Epoch 3, batch 7400, loss[loss=2.259, over 57600.00 frames., ppl: 9.576775569529676] tot_loss[loss=2.329, over 30932812.39 frames., ppl: 10.263484981691345], batch size: 400
+2022-06-18 02022-06-18 00:08:43,342022-06-18 00:08:43,400 INFO [train.py:445] Epoch 3, batch 7600, loss[loss=2.287, over 17600.00 frames., ppl: 9.847391397396178] tot_loss[loss=2.327, over 31318254.14 frames., ppl: 12022-06-18 00:09:55,899 INFO [train.py:445] Epoch 3, batch 7800, loss[loss=2.243, over 31600.00 frames., ppl: 9.423132059648937] tot_loss[loss=2.328, over 31089523.03 frames., ppl: 10.26142788315377], batch size: 400
+2022-06-18 00:11:22022-06-18 00:11:062022022-06-18 00:11:06,453 INFO [train.py:445] Epoch 3, batch 8000, loss[loss=2.262, over 44800.00 frames., ppl: 9.60332650496082] tot_loss[loss=2.326, over 31524026.10 frames., ppl2022-06-18 00:12:2022-06-18 00:12:15,673 INFO [train.py:445] Epoch 3, batch 8200, loss[loss=2.255, over 38800.00 frames., ppl: 9.536501041967743] tot_loss[loss=2.331, over 30703066.09 frames., ppl: 10.292146099657508],2022-06-18 00:13:28,910 INFO [train.py:445] Epoch 3, batch 8400, loss[loss=2.24, over 38800.00 frames., ppl: 9.393410725583543] tot_loss[loss=2.33, over 31107784.35 frames., ppl: 10.273032087264859], batch size: 400
+2022-06-18 00:142022-2022-06-18 00:14:432022-06-18 00:14:44,099 INFO [train.py:445] Epoch 3, batch 8600, loss[loss=2.24, over 44400.00 frames., ppl: 9.395853836035668] tot_loss[loss=2.326, over 31666761.88 frames., pp2022-06-18 00:15:52022-06-18 00:15:57,060 INFO [train.py:445] Epoch 3, batch 8800, loss[loss=2.243, over 36000.00 frames., ppl: 9.423888825274288] tot_loss[loss=2.326, over 31796367.42 frames., ppl: 10.2414054851798662022-06-18 00:17:02022-06-18 00:17:08,36202022-06-18 00:17:08,726 INFO [train.py:445] Epoch 3, batch 9000, loss[loss=2.267, over 46000.00 frames., ppl: 9.652392792546685] tot_loss[loss=2.326, over 32071456.99 frames.,2022-06-18 00:18:22,003 INFO [train.py:445] Epoch 3, batch 9200, loss[loss=2.276, over 28000.00 frames., ppl: 9.741815098329662] tot_loss[loss=2.329, over 31457844.11 frames., ppl: 10.264684351482765], batch size: 400
+2022-06-18 00:19:3420222022-06-18 00:19:34,237 INFO [train.py:445] Epoch 3, batch 9400, loss[loss=2.251, over 30400.00 frames., ppl: 9.498782678738172] tot_loss[loss=2.332, over 30775793.71 frames., ppl: 10.296924540432022-06-18 00:20:4202022022-06-18 00:20:47,189 INFO [train.py:445] Epoch 3, batch 9600, loss[loss=2.234, over 39200.00 frames., ppl: 9.336558721925567] tot_loss[loss=2.331, over 31041374.12 frames., ppl: 10.2926045962022-06-18 00:21:59,32022-06-18 00:21:59,596 INFO [train.py:445] Epoch 3, batch 9800, loss[loss=2.235, over 32800.00 frames., ppl: 9.346976081862742] tot_loss[loss=2.329, over 31378532.72 frames., ppl: 10.26424560533942022-06-18 00:23:13,512022-06-18 00:23:13,959 INFO [train.py:445] Epoch 3, batch 10000, loss[loss=2.318, over 57486.00 frames., ppl: 10.156704003839488] tot_loss[loss=2.328, over 31717061.52 frames., ppl: 10.26177777604666], batch size: 201
+2022-06-18 00:23:13,959 INFO [train.py:469] Comp2022-06-18 00:23:14,12022-06-18 00:23:14,150 INFO [train.py:480] Epoch 3, validation: loss=2.368, over 211809.00 frames., pp2022-06-18 00:24:28,873 I2022-06-18 00:24:282022-06-18 00:24:29,055 INFO [train.py:445] Epoch 3, batch 10200, loss[loss=2.237, over 38800.00 frames., ppl: 9.366795003756502] tot_loss[loss=2.326, over 31891498.44 frames.2022-06-18 00:25:41,217 INFO [train.py:445] Epoch 3, batch 10400, loss[loss=2.295, over 15600.00 frames., ppl: 9.919889348276136] tot_loss[loss=2.326, over 32149634.78 frames., ppl: 10.240220516064475], batch size: 400
+2022-06-18 00:26:55,810 I2022-06-18 00:26:55,993 INFO [train.py:445] Epoch 3, batch 10600, loss[loss=2.234, over 33600.00 frames., ppl: 9.338872245636681] tot_loss[loss=2.329, over 31418993.19 frames., ppl: 10.26928584262022-06-18 00:28:08,519 INFO [train.p2022-06-18 00:28:08,634 INFO [train.py:445] Epoch 3, batch 10800, loss[loss=2.272, over 56800.00 frames., ppl: 9.695155428848077] tot_loss[loss=2.326, over 31990500.97 frames., ppl: 2022-06-18 00:29:252022-06-18 00:29:26,136 INFO [train.py:445] Epoch 3, batch 11000, loss[loss=2.263, over 26000.00 frames., ppl: 9.613907877053725] tot_loss[loss=2.325, over 32084785.70 frames., ppl: 10.22850062616912022-06-18 00:30:43,22022-06-18 00:30:43,251 INFO [train.py:445] Epoch 3, batch 11200, loss[loss=2.258, over 33600.00 frames., ppl: 9.56516439780676] tot_loss[loss=2.326, over 31960911.44 frames., ppl: 10.238010304302312022-06-18 00:31:5622022-06-18 00:31:56,632 IN2022-06-18 00:31:56,708 INFO [train.py:445] Epoch 3, batch 11400, loss[loss=2.26, over 25200.00 frames., ppl: 9.58589712372044] tot_loss[loss=2.328, over 31619397.91 frames2022-06-18 00:33:11,22022-06-18 00:33:12,131 INFO [train.py:445] Epoch 3, batch 11600, loss[loss=2.338, over 59633.00 frames., ppl: 10.356024622096275] tot_loss[loss=2.325, over 32235320.65 frames., ppl: 10.22598821411522022-06-18 00:34:23,2022-06-18 00:34:2022-06-18 00:34:23,525 INFO [train.py:445] Epoch 3, batch 11800, loss[loss=2.254, over 39200.00 frames., ppl: 9.524858394748504] tot_loss[loss=2.325, over 32048688.92 frames., ppl:2022-06-18 00:35:35,82022-06-18 00:35:35,913 INFO [train.py:445] Epoch 3, batch 12000, loss[loss=2.27, over 25200.00 frames., ppl: 9.67868064735214] tot_loss[loss=2.325, over 31997513.51 frames., ppl: 10.2312655109626492022-06-18 00:36:48,013 INFO [train.py:445] Epoch 3, batch 12200, loss[loss=2.236, over 44000.00 frames., ppl: 9.355618717541732] tot_loss[loss=2.327, over 31485732.99 frames., ppl: 10.246206926903117], batch size: 400
+2022-06-18 00:37:57202022-06-18 00:37:58,138 INFO [train.py:445] Epoch 3, batch 12400, loss[loss=2.24, over 25600.00 frames., ppl: 9.391766244835834] tot_loss[loss=2.327, over 31497598.91 frames., ppl: 10.25021562756722022-06-18 00:39:07,120 INFO2022-06-2022-06-18 00:39:07,183 INFO [train.py:445] Epoch 3, batch 12600, loss[loss=2.384, over 14000.00 frames., ppl: 10.851042525979343] tot_loss[loss=2.323, over 32519525.55 frames., ppl: 2022-06-18 00:39:35202022-06-18 00:39:35,790 INFO [train.py:445] Epoch 4, batch 0, loss[loss=2.147, over 43200.00 frames., ppl: 8.562870205804106] tot_loss[loss=2.147, over 43200.00 frames., ppl: 8.562870205802022-06-18 00:40:52,414 INFO [train.p2022-06-18 00:40:52,508 INFO [train.py:445] Epoch 4, batch 200, loss[loss=2.228, over 29200.00 frames., ppl: 9.277704076690268] tot_loss[loss=2.321, over 2741475.73 frames., ppl: 2022-06-18 00:42:052022-06-18 00:42:05,865 INFO [train.py:445] Epoch 4, batch 400, loss[loss=2.195, over 40400.00 frames., ppl: 8.977953908303341] tot_loss[loss=2.304, over 5846562.95 frames., ppl: 10.018729606095072022-06-18 00:43:19,2022-2022-06-18 00:43:19,800 INFO [train.py:445] Epoch 4, batch 600, loss[loss=2.258, over 32000.00 frames., ppl: 9.560701784719457] tot_loss[loss=2.307, over 8155792.99 frames., ppl: 10.0472420242022-06-18 00:44:31,921 INFO2022-06-18 00:44:32,231 INFO [train.py:445] Epoch 4, batch 800, loss[loss=2.252, over 65600.00 frames., ppl: 9.503764887147893] tot_loss[loss=2.306, over 10730455.75 frames., ppl: 10.0379882022-06-18 00:45:46,979 INF2022-06-18 2022-06-18 00:45:47,008 INFO [train.py:445] Epoch 4, batch 1000, loss[loss=2.284, over 16800.00 frames., ppl: 9.817223075607775] tot_loss[loss=2.309, over 12565022.86 frames., ppl: 2022-06-18 00:46:58,62022022-06-18 00:46:59,406 INFO [train.py:445] Epoch 4, batch 1200, loss[loss=2.319, over 54896.00 frames., ppl: 10.165442273887317] tot_loss[loss=2.312, over 14182509.19 frames., ppl: 10.0939724102022-06-18 00:48:11,12022022-06-18 00:48:11,366 INFO [train.py:445] Epoch 4, batch 1400, loss[loss=2.228, over 33200.00 frames., ppl: 9.281628271677898] tot_loss[loss=2.311, over 15895903.34 frames., ppl: 10.089241433622022-06-18 00:49:21,946 INFO [train.py:445] Epoch 4, batch 1600, loss[loss=2.228, over 37200.00 frames., ppl: 9.281784331750782] tot_loss[loss=2.308, over 17817543.39 frames., ppl: 10.05027293176369], batch size: 400
+2022-06-18 00:50:40,6992022-06-18 002022-06-18 00:50:40,861 INFO [train.py:445] Epoch 4, batch 1800, loss[loss=2.213, over 26400.00 frames., ppl: 9.142028880589004] tot_loss[loss=2.31, over 18943312.40 frames., ppl: 102022-06-18 00:51:52,522022022-06-18 00:51:52,837 INFO [train.py:445] Epoch 4, batch 2000, loss[loss=2.239, over 36800.00 frames., ppl: 9.386206832404953] tot_loss[loss=2.314, over 19733553.31 frames., ppl: 10.115011722022-06-18 00:53:05,732022-06-18 00:532022-06-18 00:53:05,901 INFO [train.py:445] Epoch 4, batch 2200, loss[loss=2.272, over 24800.00 frames., ppl: 9.697099322932141] tot_loss[loss=2.308, over 21913341.62 frames., ppl:2022-06-18 00:54:18,607 INFO [train.2022-06-18 00:54:18,857 INFO [train.py:445] Epoch 4, batch 2400, loss[loss=2.225, over 46000.00 frames., ppl: 9.249317474193186] tot_loss[loss=2.307, over 22965670.40 frames., ppl: 2022-06-18 00:55:30,200 INFO [train.py:445] Epoch 4, batch 2600, loss[loss=2.237, over 36000.00 frames., ppl: 9.366620358715576] tot_loss[loss=2.31, over 23154243.74 frames., ppl: 10.075988274311298], batch size: 400
+2022-06-18 00:56:43,626 I2022-06-18 00:56:43,758 INFO [train.py:445] Epoch 4, batch 2800, loss[loss=2.381, over 19200.00 frames., ppl: 10.82076397130468] tot_loss[loss=2.312, over 24021927.76 frames., ppl: 10.099139722022-06-18 00:57:56,477 IN2022-06-18 00:57:56,792 INFO [train.py:445] Epoch 4, batch 3000, loss[loss=2.245, over 46800.00 frames., ppl: 9.438015729178941] tot_loss[loss=2.314, over 24363486.34 frames., ppl: 10.1129980652022-06-18 00:59:09,260 2022-06-18 00:59:092022-06-18 00:59:09,394 INFO [train.py:445] Epoch 4, batch 3200, loss[loss=2.265, over 22400.00 frames., ppl: 9.631450436381362] tot_loss[loss=2.313, over 25579590.31 frames2022-06-18 01:00:19,783 IN22022-06-18 01:00:19,882 INFO [train.py:445] Epoch 4, batch 3400, loss[loss=2.31, over 20400.00 frames., ppl: 10.07431662714213] tot_loss[loss=2.313, over 26013174.17 frames., ppl: 10.105224292022-06-18 01:01:30,135 IN2022-06-18 01:01:30,414 INFO [train.py:445] Epoch 4, batch 3600, loss[loss=2.236, over 37200.00 frames., ppl: 9.351736013580606] tot_loss[loss=2.313, over 26570820.95 frames., ppl: 10.1058682022-06-18 01:02:42,343 INFO2022-06-18 01:02:42,879 INFO [train.py:445] Epoch 4, batch 3800, loss[loss=2.23, over 68000.00 frames., ppl: 9.296757383839386] tot_loss[loss=2.314, over 26901135.26 frames., ppl: 10.1149902022-06-18 01:03:55,133 INFO [train.py:445] Epoch 4, batch 4000, loss[loss=2.224, over 45200.00 frames., ppl: 9.242159595044805] tot_loss[loss=2.314, over 27218179.98 frames., ppl: 10.113727290453435], batch size: 400
+2022-06-18 01:05:10,418 INFO2022-06-18 01:05:102022-06-18 01:05:10,451 INFO [train.py:445] Epoch 4, batch 4200, loss[loss=2.303, over 17200.00 frames., ppl: 10.003746428647437] tot_loss[loss=2.312, over 28218261.93 fra2022-06-18 01:06:21,625 I20222022-06-18 01:06:21,984 INFO [train.py:445] Epoch 4, batch 4400, loss[loss=2.224, over 51600.00 frames., ppl: 9.246754732110258] tot_loss[loss=2.315, over 27946630.82 frames., ppl: 10.1256322022-06-18 01:07:36,489 2022-06-18 01:07:36,520 INFO [train.py:445] Epoch 4, batch 4600, loss[loss=2.265, over 24400.00 frames., ppl: 9.626863820570374] tot_loss[loss=2.314, over 28720332.98 frames., ppl: 10.11030137332022-06-18 01:08:53,439 2022-2022-06-182022-06-2022-06-18 01:08:53,627 INFO [train.py:445] Epoch 4, batch 4800, loss[loss=2.267, over 27600.00 frames., ppl: 9.645810929785293] tot_loss[loss=2.311, over 29617972.09 fram2022-06-18 01:10:09,7482022-06-18 01:10:09,830 INFO [train.py:445] Epoch 4, batch 5000, loss[loss=2.232, over 31600.00 frames., ppl: 9.3212249882357] tot_loss[loss=2.314, over 29035131.05 frames., ppl: 10.11538013395032022-06-18 01:11:21,32022-06-18 01:11:21,432 INFO [train.py:445] Epoch 4, batch 5200, loss[loss=2.295, over 25200.00 frames., ppl: 9.927867206151317] tot_loss[loss=2.315, over 29178030.11 frames., ppl: 10.121629752257632022-06-18 01:12:31,20220222022-06-18 01:12:31,615 INFO [train.py:445] Epoch 4, batch 5400, loss[loss=2.253, over 44000.00 frames., ppl: 9.517759229281372] tot_loss[loss=2.314, over 29685093.39 frames., ppl: 10.119682022-06-18 01:13:44,568 INFO [train.py:442022-06-18 01:13:44,584 INFO [train.py:445] Epoch 4, batch 5600, loss[loss=2.291, over 32000.00 frames., ppl: 9.884957529521564] tot_loss[loss=2.313, over 30269205.05 frames., p2022-06-18 01:14:56,1620222022-02022-06-18 01:2022-06-18 01:14:56,497 INFO [train.py:445] Epoch 4, batch 5800, loss[loss=2.259, over 39200.00 frames., ppl: 9.572466492944336] tot_loss[loss=2.316, over 29801488.97 fra2022-06-18 01:16:06,697 2022-02022-06-182022-06-2022-06-18 01:16:06,932 INFO [train.py:445] Epoch 4, batch 6000, loss[loss=2.213, over 37200.00 frames., ppl: 9.146984564258421] tot_loss[loss=2.315, over 30194250.57 fr2022-06-18 01:17:21,041 INFO [train.py:445] Epoch 4, batch 6200, loss[loss=2.249, over 38000.00 frames., ppl: 9.47673885961414] tot_loss[loss=2.318, over 29870877.23 frames., ppl: 10.15328600805422], batch size: 400
+2022-06-18 01:18:31,714 INFO [tra2022-06-18 01:18:31,731 INFO [train.py:445] Epoch 4, batch 6400, loss[loss=2.262, over 30400.00 frames., ppl: 9.601590011310822] tot_loss[loss=2.315, over 30802540.58 frames., ppl: 10.2022-06-18 01:19:47,732 INFO 2022-20222022-06-18 01:19:48,003 INFO [train.py:445] Epoch 4, batch 6600, loss[loss=2.225, over 38400.00 frames., ppl: 9.250053295200582] tot_loss[loss=2.317, over 30418447.98 frames., ppl:2022-06-18 01:21:01,943 INFO [trai20222022-06-18 01:21:01,987 INFO [train.py:445] Epoch 4, batch 6800, loss[loss=2.269, over 20000.00 frames., ppl: 9.67226110944103] tot_loss[loss=2.317, over 30750800.05 frames., ppl: 12022-06-18 01:22:14,020 IN202022-06-20222022-06-18 01:22:14,491 INFO [train.py:445] Epoch 4, batch 7000, loss[loss=2.237, over 57200.00 frames., ppl: 9.36747165247757] tot_loss[loss=2.315, over 30798200.69 frames., pp2022-06-18 01:23:29,043 INF2022022-06-182022-06-18 01:23:29,211 INFO [train.py:445] Epoch 4, batch 7200, loss[loss=2.285, over 32000.00 frames., ppl: 9.82713523575323] tot_loss[loss=2.315, over 30893312.52 frames., ppl:2022-06-18 01:24:41,744 INFO [tr2022-06-18 01:242022-06-18 01:24:41,834 INFO [train.py:445] Epoch 4, batch 7400, loss[loss=2.202, over 19600.00 frames., ppl: 9.040253904962901] tot_loss[loss=2.317, over 30878022.09 fram2022-06-18 01:25:54,078 I2022-06-12022-06-18 01:25:54,347 INFO [train.py:445] Epoch 4, batch 7600, loss[loss=2.274, over 43600.00 frames., ppl: 9.716605873730026] tot_loss[loss=2.317, over 30703796.80 frames., ppl: 10.12022-06-18 01:27:10,015 INFO [train.py:445] Epoch 4, batch 7800, loss[loss=2.216, over 46800.00 frames., ppl: 9.169173843758113] tot_loss[loss=2.317, over 31071597.91 frames., ppl: 10.143373851833113], batch size: 400
+2022-06-18 01:28:23,350 INFO [t2022-06-18 01:28:23,867 INFO [train.py:445] Epoch 4, batch 8000, loss[loss=2.268, over 79600.00 frames., ppl: 9.663973656054495] tot_loss[loss=2.313, over 31923211.38 frames., ppl: 10.1042022-06-18 01:29:34,189 INFO [train.py:445] Epoch 4, batch 8200, loss[loss=2.245, over 32400.00 frames., ppl: 9.43977365187959] tot_loss[loss=2.317, over 31131664.31 frames., ppl: 10.141028122003172], batch size: 400
+2022-06-18 01:30:46,529 INFO [train.py:445] Epoch 4, batch 8400, loss[loss=2.237, over 33600.00 frames., ppl: 9.360975256822748] tot_loss[loss=2.316, over 31320601.53 frames., ppl: 10.13996192955062], batch size: 400
+2022-06-18 01:31:59,329 I2022-06-182022-06-18 01:31:59,696 INFO [train.py:445] Epoch 4, batch 8600, loss[loss=2.239, over 55600.00 frames., ppl: 9.387653554802114] tot_loss[loss=2.315, over 31502978.50 frames., ppl: 102022-06-18 01:33:14,5862022-062022-06-18 01:33:14,684 INFO [train.py:445] Epoch 4, batch 8800, loss[loss=2.258, over 27600.00 frames., ppl: 9.566729865422703] tot_loss[loss=2.314, over 32121193.64 frames., ppl: 10.112842022-06-18 01:34:26,92022-06-18 02022-06-18 01:34:27,244 INFO [train.py:445] Epoch 4, batch 9000, loss[loss=2.197, over 59600.00 frames., ppl: 8.997097298593951] tot_loss[loss=2.317, over 31248527.30 frames., ppl: 102022-06-18 01:35:39,52022-06-18 01:35:39,629 INFO [train.py:445] Epoch 4, batch 9200, loss[loss=2.237, over 29200.00 frames., ppl: 9.369641641091805] tot_loss[loss=2.315, over 31515140.49 frames., ppl: 10.124634193370112022-06-18 01:36:51,127 INFO 2022-06-18 01:32022-06-18 01:36:51,158 INFO [train.py:445] Epoch 4, batch 9400, loss[loss=2.321, over 18400.00 frames., ppl: 10.189959301488619] tot_loss[loss=2.314, over 31980672.67 frame2022-06-18 01:38:03,2022-06-18 01:38:03,945 IN2022-06-18 01:38:04,241 INFO [train.py:445] Epoch 4, batch 9600, loss[loss=2.251, over 63200.00 frames., ppl: 9.501965903407488] tot_loss[loss=2.314, over 32107889.32 frame2022-06-18 01:39:17,484 INFO [trai2022-06-18 01:39:17,562 INFO [train.py:445] Epoch 4, batch 9800, loss[loss=2.319, over 21200.00 frames., ppl: 10.168019931502135] tot_loss[loss=2.316, over 31522529.57 frames., ppl: 102022-06-18 01:40:28,620222022-06-18 01:40:29,094 INFO [train.py:445] Epoch 4, batch 10000, loss[loss=2.214, over 57600.00 frames., ppl: 9.153664802862398] tot_loss[loss=2.317, over 31052414.53 frames., ppl: 10.142334771159643], batch size: 400
+2022-06-18 01:40:29,094 INFO [train.py:469] C2022-06-18 01:40:29,283 I2022-06-18 01:40:29,283 INFO [train.py:480] Epoch 4, validation: loss=2.361, over 211809.00 frames.2022-06-18 01:41:40,82022-06-18 01:41:40,917 INFO [train.py:445] Epoch 4, batch 10200, loss[loss=2.242, over 26000.00 frames., ppl: 9.412651489514223] tot_loss[loss=2.313, over 32156920.71 frames., ppl: 10.10359011205962022-06-18 01:42:54,475 IN2022-06-2022-06-18 01:42:54,651 INFO [train.py:445] Epoch 4, batch 10400, loss[loss=2.239, over 39200.00 frames., ppl: 9.385133299547261] tot_loss[loss=2.314, over 32169032.05 frames., ppl: 10.2022-06-18 01:44:11,507 INFO [train.py:445]2022-06-18 01:44:11,788 INFO [train.py:445] Epoch 4, batch 10600, loss[loss=2.243, over 70800.00 frames., ppl: 9.419647934471806] tot_loss[loss=2.311, over 32866656.01 frames2022-06-18 01:45:24,583 INFO [t2022-06-18 01:45:24,771 INFO [train.py:445] Epoch 4, batch 10800, loss[loss=2.237, over 44800.00 frames., ppl: 9.368666590260199] tot_loss[loss=2.314, over 31927812.08 frames., ppl: 10.1172022-06-18 01:46:38,101 INFO [train.p2022-06-18 01:46:38,333 INFO [train.py:445] Epoch 4, batch 11000, loss[loss=2.229, over 45200.00 frames., ppl: 9.29123344237147] tot_loss[loss=2.316, over 31699333.74 frames., ppl: 2022-06-18 01:47:51,064 IN2022-06-18 01:47:51,187 INFO [train.py:445] Epoch 4, batch 11200, loss[loss=2.275, over 19600.00 frames., ppl: 9.731789546059046] tot_loss[loss=2.312, over 32336100.79 frames., ppl: 10.097806022022-06-18 01:49:04,8020222022-06-18 01:49:05,146 INFO [train.py:445] Epoch 4, batch 11400, loss[loss=2.241, over 49200.00 frames., ppl: 9.40149523000899] tot_loss[loss=2.312, over 32355540.18 frames., ppl: 10.09105561872022-06-18 01:50:17,02022-06-18 01:52022-06-18 01:50:17,194 INFO [train.py:445] Epoch 4, batch 11600, loss[loss=2.296, over 22400.00 frames., ppl: 9.935903223484805] tot_loss[loss=2.314, over 31979607.79 frames., ppl: 2022-06-18 01:51:26,715 I2022-06-18 02022022-06-18 01:51:26,965 INFO [train.py:445] Epoch 4, batch 11800, loss[loss=2.21, over 39200.00 frames., ppl: 9.11520408440936] tot_loss[loss=2.316, over 31771223.04 frames., ppl: 2022-06-18 01:52:39,115 INFO [train.py:445]2022-06-18 01:52:39,176 INFO [train.py:445] Epoch 4, batch 12000, loss[loss=2.275, over 25600.00 frames., ppl: 9.730062668369941] tot_loss[loss=2.315, over 31925103.82 frames.,2022-06-18 01:53:49,2022-06-2022-06-18 01:52022-06-18 01:53:49,813 INFO [train.py:445] Epoch 4, batch 12200, loss[loss=2.232, over 38000.00 frames., ppl: 9.321209099356507] tot_loss[loss=2.314, over 32032479.22 frames.2022-06-18 01:55:02,407 I2022-2022-06-18 01:55:02,519 INFO [train.py:445] Epoch 4, batch 12400, loss[loss=2.277, over 23200.00 frames., ppl: 9.751888294610456] tot_loss[loss=2.315, over 31603193.47 frames., ppl: 10.129282022-06-18 01:56:13,260 INFO 2022-06-18 01:56:13,361 INFO [train.py:445] Epoch 4, batch 12600, loss[loss=2.219, over 24400.00 frames., ppl: 9.200714885328322] tot_loss[loss=2.316, over 31537696.53 frames., ppl: 10.135422022-06-18 01:56:43,2022-06-18 01:56:43,958 INFO [train.py:445] Epoch 5, batch 0, loss[loss=2.195, over 24400.00 frames., ppl: 8.976207957203338] tot_loss[loss=2.195, over 24400.00 frames., ppl: 8.976207957203332022-06-18 01:58:03,2022-2022-06-18 01:58:03,186 INFO [train.py:445] Epoch 5, batch 200, loss[loss=2.305, over 15200.00 frames., ppl: 10.025510192303484] tot_loss[loss=2.288, over 3121687.18 frames., ppl: 9.851790502022-06-18 01:59:182022-06-18 01:59:18,742 INFO [train.py:445] Epoch 5, batch 400, loss[loss=2.25, over 40400.00 frames., ppl: 9.484841080195354] tot_loss[loss=2.297, over 5762862.77 frames., ppl: 9.943889988801027]2022-06-18 02:00:30,321 I2022-06-18 02:00:30,354 INFO [train.py:445] Epoch 5, batch 600, loss[loss=2.246, over 26000.00 frames., ppl: 9.452107006355543] tot_loss[loss=2.297, over 8087192.04 frames., ppl: 9.94877642022-06-18 02:01:45,490 INFO 2022-06-18 022022-06-18 02:01:45,812 INFO [train.py:445] Epoch 5, batch 800, loss[loss=2.156, over 41808.00 frames., ppl: 8.636696452245724] tot_loss[loss=2.295, over 10833183.22 frames.2022-06-18 02:02:59,62022-06-182022-06-18 02022-06-18 02:03:00,201 INFO [train.py:445] Epoch 5, batch 1000, loss[loss=2.208, over 62400.00 frames., ppl: 9.099310284956541] tot_loss[loss=2.295, over 12779948.97 frames.2022-06-18 02:04:10,920222022-06-18 02:04:12022-06-18 02:04:11,145 INFO [train.py:445] Epoch 5, batch 1200, loss[loss=2.301, over 20000.00 frames., ppl: 9.987455772652494] tot_loss[loss=2.296, over 14556346.92 frames2022-06-18 02:05:23,967 IN2022-06-18 02:05:24,043 INFO [train.py:445] Epoch 5, batch 1400, loss[loss=2.262, over 22800.00 frames., ppl: 9.606275317937765] tot_loss[loss=2.297, over 16308937.35 frames., ppl: 9.946534632022-06-18 02:06:33,012 INFO [t2022-06-18 02:2022-06-18 02:06:33,072 INFO [train.py:445] Epoch 5, batch 1600, loss[loss=2.239, over 21200.00 frames., ppl: 9.383503826639515] tot_loss[loss=2.304, over 17199994.24 fra2022-06-18 02:07:45,832022-2022-06-18 02:07:46,238 INFO [train.py:445] Epoch 5, batch 1800, loss[loss=2.267, over 47235.00 frames., ppl: 9.649716041849981] tot_loss[loss=2.3, over 19146301.38 frames., ppl: 9.9703488842022-06-18 02:09:01,009 2022-06-18 02:09:01,120 I2022-06-18 02:09:01,184 INFO [train.py:445] Epoch 5, batch 2000, loss[loss=2.206, over 29600.00 frames., ppl: 9.078037685458632] tot_loss[loss=2.3, over 20464692.64 frame2022-06-18 02:10:17,2022-06-12022-06-18 02:10:18,201 INFO [train.py:445] Epoch 5, batch 2200, loss[loss=2.274, over 63060.00 frames., ppl: 9.720122140310368] tot_loss[loss=2.299, over 21465825.74 frames., ppl: 9.968992022-06-18 02:11:302022-06-18 02022-06-18 22022-06-18 02:11:30,577 INFO [train.py:445] Epoch 5, batch 2400, loss[loss=2.219, over 20000.00 frames., ppl: 9.202589951807727] tot_loss[loss=2.299, over 22768598.40 frames.2022-06-18 02:12:48,061 INFO [train.py:445] Epoch 5, batch 2600, loss[loss=2.244, over 39600.00 frames., ppl: 9.426888928313552] tot_loss[loss=2.301, over 23284409.60 frames., ppl: 9.987166190779645], batch size: 400
+2022-06-18 02:142022-06-18 02:2022-06-18 02:14:01,396 INFO [train.py:445] Epoch 5, batch 2800, loss[loss=2.209, over 36000.00 frames., ppl: 9.103443766178184] tot_loss[loss=2.302, over 23814331.81 frames., ppl: 9.9952022-06-18 02:152022-06-18 2022-06-18 02:15:13,745 INFO [train.py:445] Epoch 5, batch 3000, loss[loss=2.214, over 75200.00 frames., ppl: 9.151928626880695] tot_loss[loss=2.297, over 25507353.08 frames., ppl: 9.94872022-06-18 02:162022-06-18 02:16:26,094 INFO [train.py:445] Epoch 5, batch 3200, loss[loss=2.23, over 45600.00 frames., ppl: 9.299260639160927] tot_loss[loss=2.299, over 25964305.48 frames., ppl: 9.966515507554528], b2022-06-18 02:12022-06-18 02:17:37,109 INFO [tra2022-06-18 02:17:37,226 INFO [train.py:445] Epoch 5, batch 3400, loss[loss=2.205, over 39200.00 frames., ppl: 9.06836092406278] tot_loss[loss=2.302, over 26165117.08 fra2022-06-18 02:18:50,823 INFO [train.py:445] Epoch 5, batch 3600, loss[loss=2.323, over 67064.00 frames., ppl: 10.210007325265083] tot_loss[loss=2.303, over 26530934.12 frames., ppl: 10.001761335668489], batch size: 201
+2022-06-182022022-06-18 02:20:05,420 INFO [train.py:445] Epoch 5, batch 3800, loss[loss=2.279, over 19200.00 frames., ppl: 9.770995309908729] tot_loss[loss=2.302, over 27188233.82 frames., ppl: 9.995567244454127], batc2022-06-18 22022-06-18 02:21:15,429 INFO2022-06-18 02:21:15,578 INFO [train.py:445] Epoch 5, batch 4000, loss[loss=2.21, over 29600.00 frames., ppl: 9.114865924442785] tot_loss[loss=2.303, over 27360567.10 frames., p2022-06-18 02:22:26,273 2022-06-18 02:22:26,389 INFO [train.py:445] Epoch 5, batch 4200, loss[loss=2.223, over 33600.00 frames., ppl: 9.238096805403991] tot_loss[loss=2.302, over 28313155.97 frames., ppl: 9.992206399552022-06-18 02:23:38,388 INFO [train.py:445] Epoch 5, batch 4400, loss[loss=2.26, over 20800.00 frames., ppl: 9.579029883793055] tot_loss[loss=2.303, over 28252421.01 frames., ppl: 10.004459996882957], batch size: 400
+2022-06-18 0222022-06-18 02:24:48,615 INFO [train.py:445] Epoch 5, batch 4600, loss[loss=2.23, over 58000.00 frames., ppl: 9.299580473757977] tot_loss[loss=2.305, over 28226128.37 frames., ppl: 10.026028294465757], bat2022-06-18 022022-06-18 02:26:02,148 INFO [train.py:4452022-06-18 02:26:02,186 INFO [train.py:445] Epoch 5, batch 4800, loss[loss=2.213, over 38400.00 frames., ppl: 9.147649010703416] tot_loss[loss=2.306, over 283810412022-06-18 202022-06-2022-06-12022-06-18 02:27:16,657 INFO [train.py:445] Epoch 5, batch 5000, loss[loss=2.23, over 41600.00 frames., ppl: 9.304020229959525] tot_loss[loss=2.305, over 29124135.87 frames., ppl: 10.028792022-06-18 22022-06-18 02:28:31,777 INFO 2022-06-18 02:28:31,799 INFO [train.py:445] Epoch 5, batch 5200, loss[loss=2.274, over 23600.00 frames., ppl: 9.719512801453682] tot_loss[loss=2.303, over 29786519.76 frames., 2022-06-18 022022-06-18 02:29:2022-06-18 02:29:45,265 INFO [train.py:445] Epoch 5, batch 5400, loss[loss=2.327, over 12000.00 frames., ppl: 10.248398190459477] tot_loss[loss=2.305, over 29749786.87 frames., ppl: 10.02642022-06-18 202022-06-18 02:30:59,688 INF2022-06-18 02:30:59,921 INFO [train.py:445] Epoch 5, batch 5600, loss[loss=2.191, over 46000.00 frames., ppl: 8.942794871367397] tot_loss[loss=2.303, over 30189193.23 frames., pp2022-06-18 02:32:10,282 INFO [train.py:445] Epoch 5, batch 5800, loss[loss=2.255, over 20000.00 frames., ppl: 9.536932329533633] tot_loss[loss=2.303, over 30171080.31 frames., ppl: 10.002980097435996], batch size: 400
+2022-06-18 2022-06-18 02:33:21,572 INFO [train.py:445] Epoch 5, batch 6000, loss[loss=2.199, over 24400.00 frames., ppl: 9.013182288249364] tot_loss[loss=2.306, over 29978066.32 frames., ppl: 10.03185805322657], batc2022-06-18 0202022-06-18 02:34:32,294 INFO [train.py:445] Epoch 5, batch 6200, loss[loss=2.216, over 44400.00 frames., ppl: 9.171701162246471] tot_loss[loss=2.304, over 30502661.37 frames., ppl: 10.018692116177995], b2022-06-18 02:32022-062022-06-18 02:35:46,570 INFO [train.py:445] Epoch 5, batch 6400, loss[loss=2.215, over 35200.00 frames., ppl: 9.157505933746274] tot_loss[loss=2.303, over 31024285.42 frames., ppl: 10.0029017269542022-06-18 02:37:01,239 INFO [train.py:445] Epoch 5, batch 6600, loss[loss=2.261, over 32000.00 frames., ppl: 9.590588246268643] tot_loss[loss=2.304, over 30818840.99 frames., ppl: 10.015709489291224], batch size: 400
+2022-06-18 02:38:12,875 INFO [train.py:445] Epoch 5, batch 6800, loss[loss=2.224, over 31200.00 frames., ppl: 9.244941364668446] tot_loss[loss=2.305, over 30851787.57 frames., ppl: 10.019711017039736], batch size: 400
+2022-06-18 02:39:24,2022-06-18 02:39:24,260 INFO [train.py:445] Epoch 5, batch 7000, loss[loss=2.261, over 28800.00 frames., ppl: 9.596432990417753] tot_loss[loss=2.303, over 31388894.02 frames., ppl: 10.008111165970592022-06-182022-2022-06-18 02:40:37,843 INFO [train.py:445] Epoch 5, batch 7200, loss[loss=2.195, over 38800.00 frames., ppl: 8.983829728412122] tot_loss[loss=2.305, over 31247046.63 frames., ppl: 10.023969658627955], b2022-06-182022-06-18 02:41:47,480 INFO [train.py:445] Epoch 5, batch 7400, loss[loss=2.434, over 8800.00 frames., ppl: 11.4082707969672] tot_loss[loss=2.307, over 30915358.11 frames., ppl: 10.040600233164996], batch si2022-06-18 02:43:00,2022-06-18 02:43:00,587 INFO [train.py:445] Epoch 5, batch 7600, loss[loss=2.225, over 31200.00 frames., ppl: 9.249493655648468] tot_loss[loss=2.303, over 31630839.35 frames., ppl: 10.007492275931242022-06-12022-06-18 02:442022-06-18 02:44:13,543 INFO [train.py:445] Epoch 5, batch 7800, loss[loss=2.391, over 19600.00 frames., ppl: 10.926536761167196] tot_loss[loss=2.306, over 31086649.04 frames., ppl: 10.035845732022-06-18 02:2022-06-18 02:45:28,417 INFO [train.py:445] Epoch 5, batch 8000, loss[loss=2.215, over 44000.00 frames., ppl: 9.161929661005612] tot_loss[loss=2.304, over 32070472.62 frames., ppl: 10.010397901499273], b2022-06-18 02:46:37,156 IN2022-06-18 02:462022-06-18 02:42022-06-18 02:46:37,197 INFO [train.py:445] Epoch 5, batch 8200, loss[loss=2.319, over 13200.00 frames., ppl: 10.1633115362535] tot_loss[loss=2.309, over 30530972022-06-12022-06-18 02:47:52022-06-18 02:47:50,887 INFO [train.py:445] Epoch 5, batch 8400, loss[loss=2.26, over 23600.00 frames., ppl: 9.586046269863761] tot_loss[loss=2.307, over 31339172.54 frames., ppl: 10.040564852022-06-182022-06-18 02:42022-06-18 02:49:03,674 INFO [train.py:445] Epoch 5, batch 8600, loss[loss=2.24, over 46800.00 frames., ppl: 9.390009147187877] tot_loss[loss=2.305, over 31825672.23 frames., ppl: 10.0264177542022-06-182022-2022-06-18 02:50:15,077 INFO [train.py:445] Epoch 5, batch 8800, loss[loss=2.181, over 34000.00 frames., ppl: 8.857423972038958] tot_loss[loss=2.307, over 31645238.16 frames., ppl: 10.04071913078311], 2022-06-18 2022-06-18 02022-06-18 02:51:27,832 INFO [train.py:445] Epoch 5, batch 9000, loss[loss=2.214, over 36400.00 frames., ppl: 9.15364549753632] tot_loss[loss=2.304, over 31893010.71 frames., ppl: 10.013054743742022-06-18 02022-2022-06-18 02:52:39,685 INFO [train.py:445] Epoch 5, batch 9200, loss[loss=2.255, over 27200.00 frames., ppl: 9.53494275433679] tot_loss[loss=2.307, over 31473650.96 frames., ppl: 10.046755628914115],2022-06-18 02:53:2022-06-2022-06-18 02:53:52,347 I2022-06-2022-06-18 02:53:52,557 INFO [train.py:445] Epoch 5, batch 9400, loss[loss=2.222, over 36000.00 frames., ppl: 9.223714012570417] tot_loss[loss=2.308, over 311662022-06-18 02:55:06,434 INF2022-06-18 02:55:06,492022-06-18 02:55:06,691 INFO [train.py:445] Epoch 5, batch 9600, loss[loss=2.202, over 37600.00 frames., ppl: 9.04536369120788] tot_loss[loss=2.306, over 31498898.06 fram2022-06-12022-06-18 02:56:18,151 INFO [train.py:445] Epoch 5, batch 9800, loss[loss=2.22, over 30800.00 frames., ppl: 9.206796060298736] tot_loss[loss=2.306, over 31668784.66 frames., ppl: 10.035048053983859], batch si2022-06-2022-06-18 02:2022-06-18 02:57:31,480 INFO [train2022-06-18 02:57:31,605 INFO [train.py:445] Epoch 5, batch 10000, loss[loss=2.27, over 29200.00 frames., ppl: 9.67744317093249] tot_loss[loss=2.306, over 31657877.42 frames., ppl: 10.035225105588257], batch size: 400
+2022-06-18 02:2022-06-12022-06-18 02:57:31,790 INFO [train.py:480] Epoch 5, validation: loss=2.355, over 211809.00 frames., ppl: 10.53785172022-06-18 02:58:47,4642022-2022-06-18 02:58:47,580 INFO [train.py:445] Epoch 5, batch 10200, loss[loss=2.307, over 24400.00 frames., ppl: 10.041663259421876] tot_loss[loss=2.307, over 31722992.21 frames., ppl: 10.046742022-06-18 03:00:02,589 INFO [train.py:445] Epoch 5, batch 10400, loss[loss=2.224, over 41600.00 frames., ppl: 9.240140848728048] tot_loss[loss=2.307, over 31574894.63 frames., ppl: 10.043007742367786], batch size: 400
+2022-06-18 03:2022-06-18 03:01:16,381 INFO [train.py:445]2022-06-18 03:01:16,381 INFO [train.py:445] Epoch 5, batch 10600, loss[loss=2.249, over 25200.00 frames., ppl: 9.481331659238654] tot_loss[loss=2.306, over 317242022-06-182022-06-18 03:02:26,2022-06-18 03:02:26,511 INFO [train.py:445] Epoch 5, batch 10800, loss[loss=2.21, over 41205.00 frames., ppl: 9.117952456805503] tot_loss[loss=2.305, over 32242049.38 frames., ppl: 10.028202022-06-18 03:03:42,068 INFO [train.py:445] Epoch 5, batch 11000, loss[loss=2.228, over 36000.00 frames., ppl: 9.2774708848555] tot_loss[loss=2.305, over 31989369.74 frames., ppl: 10.02536878924561], batch size: 400
+2022-06-18 03:04:2022-06-182022-06-18 03:04:54,6592022-06-18 03:04:54,675 INFO [train.py:445] Epoch 5, batch 11200, loss[loss=2.272, over 16800.00 frames., ppl: 9.702652687460938] tot_loss[loss=2.306, over 31745400.58 f2022-06-18 02022-2022-06-18 03:06:11,349 INFO [train.py:445] Epoch 5, batch 11400, loss[loss=2.27, over 25200.00 frames., ppl: 9.680931346280989] tot_loss[loss=2.309, over 31177214.57 frames., ppl: 10.0666839008268], bat2022-06-182022022-06-18 03:07:28,423 INFO [train.py:445] Epoch 5, batch 11600, loss[loss=2.232, over 20400.00 frames., ppl: 9.315378779942202] tot_loss[loss=2.309, over 31157597.37 frames., ppl: 10.066492671399986], ba2022-06-18 03:08:40,556 INFO2022-06-18 03:08:40,729 INFO [train.py:445] Epoch 5, batch 11800, loss[loss=2.231, over 29200.00 frames., ppl: 9.30860996155813] tot_loss[loss=2.303, over 32461225.97 frames., ppl: 10.005468502022-06-18202022-06-18 03:09:2022-06-18 03:09:50,899 INFO [train.py:445] Epoch 5, batch 12000, loss[loss=2.235, over 55200.00 frames., ppl: 9.345198809693976] tot_loss[loss=2.308, over 31739099.82 frames., ppl: 10.056052022-06-182022-06-18 03:11:06,894 INFO [train.py:445] Epoch 2022-06-18 03:11:06,902 INFO [train.py:445] Epoch 5, batch 12200, loss[loss=2.283, over 17600.00 frames., ppl: 9.801152688155035] tot_loss[loss=2.306, over 312022-06-18 03:12:18,975 INF2022022-06-18 03:12:19,115 INFO [train.py:445] Epoch 5, batch 12400, loss[loss=2.221, over 33600.00 frames., ppl: 9.212050489628892] tot_loss[loss=2.307, over 31781707.63 frames., ppl: 10.042022-06-18 0322022-06-18 03:12022022-06-18 03:13:33,92022-06-18 03:13:33,934 INFO [train.py:445] Epoch 5, batch 12600, loss[loss=2.217, over 64000.00 frames., ppl: 9.181702514054939] tot_loss[loss=2.309, over 31204814.22022-06-18 03:13:59,808 INFO 2022-06-18 03:13:59,831 INFO [train.py:445] Epoch 6, batch 0, loss[loss=2.228, over 20800.00 frames., ppl: 9.283669008325345] tot_loss[loss=2.228, over 20800.00 frames., ppl: 9.283662022-06-12022-02022-06-18 03:2022-06-18 03:15:18,992 INFO [train.p2022-06-18 03:15:19,123 INFO [train.py:445] Epoch 6, batch 200, loss[loss=2.196, over 44400.00 frames., ppl: 8.99028704725054] tot_loss[loss=2.287, o2022-06-18 03:16:32,826 INFO 2022-06-18 03:16:32,842 INFO [train.2022-06-18 03:16:32,874 INFO [train.py:445] Epoch 6, batch 400, loss[loss=2.216, over 34000.00 frames., ppl: 9.172535017054583] tot_loss[loss=2.287, o2022-06-18 03:17:46,665 INFO 2022-06-18 03:17:46,734 INFO [train.py:445] Epoch 6, batch 600, loss[loss=2.258, over 20800.00 frames., ppl: 9.567435500889214] tot_loss[loss=2.288, over 8258939.89 frames., ppl: 9.85952022-06-18 03:18:59,511 INFO [train.py:445] Epoch 6, 2022-06-18 02022-06-18 03:18:59,555 INFO [train.py:445] Epoch 6, batch 800, loss[loss=2.284, over 20800.00 frames., ppl: 9.811795643503904] tot_loss[loss=2.291, o2022-06-12022-06-18 03:20:13,931 INFO [train.py:445] Epoch 6, batch 1000, loss[loss=2.189, over 49200.00 frames., ppl: 8.92449604267055] tot_loss[loss=2.293, over 12491715.22 frames., ppl: 9.899904182506965], batch si2022-06-18 03:21:25,536 INFO [train.py:445] Epoch 6, b2022-06-18 03:21:25,685 INFO [train.py:445] Epoch 6, batch 1200, loss[loss=2.201, over 41600.00 frames., ppl: 9.037172434173314] tot_loss[loss=2.293, over 141622022-06-182022-06-18 03:22:40,434 INFO [train.py:445] Epoch 6, batch 1400, loss[loss=2.223, over 50800.00 frames., ppl: 9.23053981824481] tot_loss[loss=2.293, over 15914880.82 frames., ppl: 9.900298300622756], batch s2022-06-12022-06-18 03:23:53,527 INFO [train.py:445] Epoch 6, batch 1600, loss[loss=2.224, over 29600.00 frames., ppl: 9.247623078844356] tot_loss[loss=2.294, over 17307626.74 frames., ppl: 9.91280562300377], batch s2022-06-18 03:25:03,781 INFO [train.py:445] Epoch 6, batch 1800, lo2022-06-18 03:25:03,805 INFO [train.py:445] Epoch 6, batch 1800, loss[loss=2.321, over 15200.00 frames., ppl: 10.185317939374725] tot_loss[loss=2.2952022-06-18 03:26:18,687 INFO [train.py:445] Epoch 6, batch 2000, loss[loss=2.225, over 27200.00 frames., ppl: 9.258099267831696] tot_loss[loss=2.292, over 20278317.81 frames., ppl: 9.893863292668145], batch size: 400
+2022-06-18 03:27:34,012 2022-06-18 03:272022-06-18 03:27:34,221 INFO [train.py:445] Epoch 6, batch 2200, loss[loss=2.22, over 40000.00 frames., ppl: 9.210151042777474] tot_loss[loss=2.292, over 21280533.86 frames., 2022-06-18 02022-06-18 03:28:50,110 INFO 2022-06-18 03:28:50,217 INFO [train.py:445] Epoch 6, batch 2400, loss[loss=2.184, over 45200.00 frames., ppl: 8.880054812247282] tot_loss[loss=2.292, over 22347183.99 frames., 2022-06-18 02022-06-18 032022-06-18 03:30:03,357 INFO [train.py:445] Epoch 6, batch 2600, loss[loss=2.226, over 28000.00 frames., ppl: 9.263785099774697] tot_loss[loss=2.294, over 23412598.67 frames., ppl: 9.9098690012022-06-18 03:31:15,435 INFO [tr2022-06-2022-06-18 03:31:15,826 INFO [train.py:445] Epoch 6, batch 2800, loss[loss=2.208, over 60800.00 frames., ppl: 9.093119142408034] tot_loss[loss=2.294, over 23644367.52 frames., p2022-06-18 2022-06-18 03:2022-06-18 03:32:31,105 INFO [train.py:445] Epoch 6, batch 3000, loss[loss=2.224, over 25600.00 frames., ppl: 9.245804877331615] tot_loss[loss=2.293, over 25147088.27 frames., ppl: 9.907518762022-06-18 2022-06-18 03:32022-06-18 03:33:44,053 INFO [train.py:445] Epoch2022-06-18 03:33:44,132 INFO [train.py:445] Epoch 6, batch 3200, loss[loss=2.218, over 24800.00 frames., ppl: 9.18825379840323] tot_loss[loss=2022-06-18 2022-06-18 03:34:53,82022-06-18 03:34:53,948 INFO [train.py:445] Epoch 6, batch 3400, loss[loss=2.209, over 41600.00 frames., ppl: 9.104799072645834] tot_loss[loss=2.293, over 26069585.42 frames., ppl: 9.902022-06-18 03:36:05,227 I2022-06-18 03:36:06,967 INFO [train.py:445] Epoch 6, batch 3600, loss[loss=2.266, over 80024.00 frames., ppl: 9.643719039490993] tot_loss[loss=2.294, over 26855407.03 frames., ppl: 9.912229832022-06-18 03:37:19,190 I2022-06-2022-06-18 03:37:19,252 INFO [train.py:445] Epoch 6, batch 3800, loss[loss=2.288, over 20400.00 frames., ppl: 9.859786340713052] tot_loss[loss=2.294, over 27063262.31 frames., ppl: 9.92022-06-18 03:38:30,683 INFO [tra2022-06-18 03:38:30,688 INFO [train.py:442022-06-18 03:38:30,738 INFO [train.py:445] Epoch 6, batch 4000, loss[loss=2.251, over 24800.00 frames., ppl: 9.496127691436712] tot_loss[loss=2022-06-18 02022-06-18 03:39:41,620 INFO [t2022-06-18 03:39:42,057 INFO [train.py:445] Epoch 6, batch 4200, loss[loss=2.217, over 66400.00 frames., ppl: 9.1818821430223] tot_loss[loss=2.294, over 28249495.84 frames2022-06-18 03:40:54,422 INFO [train2022-06-18 03:40:54,483 INFO [train.py:4452022-06-18 03:40:54,512 INFO [train.py:445] Epoch 6, batch 4400, loss[loss=2.3, over 19600.00 frames., ppl: 9.972779139000187] tot_loss[loss=2022-06-18 03:42022-06-12022-06-18 2022-06-18 03:42:05,426 INFO [train.py:445] Epoch 6, batch 4600, loss[loss=2.257, over 20800.00 frames., ppl: 9.55325513852972] tot_loss[loss=2.297, over 28500850.90 frames., ppl: 9.2022-06-18 03:43:21,154 INFO [train.py:445] Epoch 6, batch 4800, loss[loss=2.235, over 31600.00 frames., ppl: 9.342147908762708] tot_loss[loss=2.296, over 29118501.45 frames., ppl: 9.935730529423937], batch size: 400
+2022-06-18 03:42022-06-2022-06-18 2022-06-18 03:44:33,172 INFO [train.py:445] Epoch 6, batch 5000, loss[loss=2.209, over 58800.00 frames., ppl: 9.107819382754862] tot_loss[loss=2.297, over 28973770.76 frames., ppl: 9.2022-06-18 03:45:42,9462022-06-18 03:45:422022-06-18 03:45:43,096 INFO [train.py:445] Epoch 6, batch 5200, loss[loss=2.25, over 24800.00 frames., ppl: 9.484535341424262] tot_loss[loss=2.297, over 29521525.53 frames., 2022-06-18 03:2022-06-18 03:46:58,192 INFO [train.py:445] Epoch 6, batch 5400, loss[loss=2.216, over 25600.00 frames., ppl: 9.173881449364142] tot_loss[loss=2.296, over 29738142.41 frames., ppl: 9.938005105622198], ba2022-06-18 03:2022-06-18 03:48:11,817 INFO [train.py:445] Epoch 6, batch 5600, loss[loss=2.192, over 27600.00 frames., ppl: 8.953092299873617] tot_loss[loss=2.296, over 30022596.23 frames., ppl: 9.934078625171455], b2022-06-18 03:49:27,656 INFO [train.py:445] Epoch 6, ba2022-06-18 03:49:27,694 INFO [train.py:445] Epoch 6, batch 5800, loss[loss=2.279, over 14400.00 frames., ppl: 9.77033865768859] tot_loss[loss=2.295, over 30435155.2022-06-18 03:50:38,006 INFO [train.py:445] Epoch 6, batch 6000, loss[loss=2.213, over 36000.00 frames., ppl: 9.141116671090469] tot_loss[loss=2.296, over 30833644.74 frames., ppl: 9.935564119676817], batch size: 400
+2022-06-18 03:51:48,352 INFO [train.py:445] Epoch 6, batch 6200, loss[loss=2022-06-18 03:51:48,515 INFO [train.py:445] Epoch 6, batch 6200, loss[loss=2.217, over 47600.00 frames., ppl: 9.181510809889787] tot_loss[lo2022-06-18 03:52022-06-18 03:53:01,901 INFO [train.py:445] Epoch 6, batch 6400, loss[loss=2.188, over 33200.00 frames., ppl: 8.914912469989435] tot_loss[loss=2.298, over 30330572.69 frames., ppl: 9.957095983377108], b2022-06-18 03:52022-06-18 03:54:17,786 INFO [train.py:445] Epoch 6, batch 6600, loss[loss=2.222, over 46800.00 frames., ppl: 9.221303696100705] tot_loss[loss=2.299, over 30305677.00 frames., ppl: 9.96252061408439], b2022-06-18 03:52022-06-18 2022-06-18 03:55:29,560 INFO [train.py:445] Epoch 6, batch 6800, loss[loss=2.204, over 30800.00 frames., ppl: 9.061724609314837] tot_loss[loss=2.299, over 30695134.50 frames., ppl: 9.968444602022-06-18 03:2022-06-18 02022-06-18 03:56:41,193 INFO [train.py:445] Epoch 6, batch 7000, loss[loss=2.239, over 20000.00 frames., ppl: 9.387139601171025] tot_loss[loss=2.299, over 31003226.73 frames., ppl: 9.96385042022-06-18 03:57:56,899 INFO [train.2022-06-182022-06-18 03:57:57,047 INFO [train.py:445] Epoch 6, batch 7200, loss[loss=2.263, over 23600.00 frames., ppl: 9.616043153562801] tot_loss[loss=2.296, over 31302311.44 fra2022-06-18 03:59:15,090 INFO [train.py:445] Epoch 6, batch 7400, loss[loss=2.233, over 66400.00 frames., ppl: 9.330558729636019] tot_loss[loss=2.298, over 31450407.35 frames., ppl: 9.955744120874906], batch size: 400
+2022-06-18 02022-06-18 04:00:27,175 INFO [train2022-06-18 04:00:27,182 INFO2022-06-18 04:00:27,417 INFO [train.py:445] Epoch 6, batch 7600, loss[loss=2.234, over 39200.00 frames., ppl: 9.341375615758706] tot_loss[loss=2022-06-182022-06-18 042022-06-18 04:01:39,085 INFO [train.py:445] Epoch 6, batch 7800, loss[loss=2.224, over 51600.00 frames., ppl: 9.24252768269772] tot_loss[loss=2.301, over 30987410.82 frames., ppl: 9.9810160132212022-06-18 04:02:49,279 INFO [train.py:445] Epoch 6, batch 8000, loss[loss=2.241, over 32800.00 frames., ppl: 9.40685914698455] tot_loss[loss=2.298, over 31525298.56 frames., ppl: 9.956608876313227], batch size: 400
+2022-06-18 04:04:02,9792022-06-18 04:04:03,041 2022-06-18 04:04:03,042 INFO [train.py:445] Epoch 6, batch 8200, loss[loss=2.251, over 22400.00 frames., ppl: 9.498784596740332] tot_loss[loss=2.298, over 31440693.08 fram2022-06-182022-06-18 04:05:17,853 INFO [train.py:445] Epoch 6, batch 8400,2022-06-18 04:05:17,974 INFO [train.py:445] Epoch 6, batch 8400, loss[loss=2.195, over 36800.00 frames., ppl: 8.979423430119125] tot_loss[los2022-06-18 02022-06-18 04:06:30,828 INFO [train.py:445] Epoch 6, batch 8600, loss[loss=2.292, over 24800.00 frames., ppl: 9.899606012954958] tot_loss[loss=2.295, over 32089600.99 frames., ppl: 9.921587295677622], bat2022-06-18 04:07:43,812 INFO [train.py:445] Epoch 6, batch 8800, loss[loss=2.215, over 46000.00 frames., ppl: 9.157353658673857] tot_loss[loss=2.299, over 31331213.95 frames., ppl: 9.966438160080198], batch size: 400
+2022-06-18 04:08:56,955 INFO [train.py:445] Epoch 6, batch 9000, loss[loss=2.236, over 40800.00 frames., ppl: 9.35773324708154] tot_loss[loss=2.3, over 31191658.82 frames., ppl: 9.973448293649462], batch size: 400
+2022-06-18 04:10:09,664 I2022-06-18 04:10:09,880 INFO [train.py:445] Epoch 6, batch 9200, loss[loss=2.206, over 42009.00 frames., ppl: 9.079437270991185] tot_loss[loss=2.298, over 31994322.96 frames., ppl: 9.954722022-06-18 04:11:29,557 INFO [train.py:42022-06-18 2022-06-18 04:11:29,643 INFO [t2022-06-18 04:11:29,785 INFO [train.py:445] Epoch 6, batch 9400, loss[loss=2.192, over 38400.00 frames., ppl: 8.956110339886674] tot_lo2022-06-18 04:12:41,589 INFO [train.py:445] Epoch 6, batch 9600, loss[loss=2.26, over 40400.00 frames., ppl: 9.582155216891842] tot_loss[loss=2.298, over 31820487.15 frames., ppl: 9.954956669950834], batch size: 400
+2022-06-18 04:13:52,821 I2022-06-18 04:13:52,912 INFO2022-06-18 04:13:52,981 INFO [train.py:445] Epoch 6, batch 9800, loss[loss=2.238, over 24800.00 frames., ppl: 9.371515625795473] tot_loss[loss=2.298, over 32095987.2022-06-18 04:15:07,377 I2022-06-18 04:15:07,455 INFO [train.py:445] Epoch 6, 2022-06-18 04:15:07,464 INFO [train.py:445] Epoch 6, batch 10000, loss[loss=2.349, over 17600.00 frames., ppl: 10.47025378134195] tot_loss[loss=2.3, over 31420844.57 frames., ppl: 9.97672254100202], batch size2022-06-18 04:15:07,651 INFO [train.py:480] Epoch 6, validation: loss=2.348, over 211809.00 frames., ppl: 10.46682138852792
+2022-06-18 04:16:19,7392022022-06-18 04:12022-06-18 2022-06-18 04:16:19,888 INFO [train.py:445] Epoch 6, batch 10200, loss[loss=2.235, over 20400.00 frames., ppl: 9.343912209340546] tot_loss[loss=2.3, over 31726681.22022-06-18 04:17:33,802 INFO [train.py:445] Epoch 6,2022-06-18 04:17:34,018 I2022-06-18 04:17:34,143 INFO [train.py:445] Epoch 6, batch 10400, loss[loss=2.227, over 46431.00 frames., ppl: 9.268914890498102] tot_loss[l2022-06-18 04:18:48,613 IN2022022-06-18 04:18:48,767 INFO [train.p2022-06-18 04:18:48,959 INFO [train.py:445] Epoch 6, batch 10600, loss[loss=2.19, over 40800.00 frames., ppl: 8.933276540498145] tot_loss[loss=2.2982022-06-18 04:20:00,453 INFO [t202022-06-18 04:202022-06-18 04:20:01,013 INFO [train.py:445] Epoch 6, batch 10800, loss[loss=2.224, over 66000.00 frames., ppl: 9.24100865661027] tot_loss[loss=2.3, over 31804904.59 f2022-06-18 04:21:12,098 INFO [tra2022-06-18 04:21:12,127 INFO [train.py2022-06-18 04:21:12,253 INFO [train.py:445] Epoch 6, batch 11000, loss[loss=2.237, over 26400.00 frames., ppl: 9.360867220622376] tot_loss[loss=2022-06-18 04:22:23,591 INFO [train.2022-06-18 04:22:23,736 INFO [train.py:445] Epo2022-06-18 04:22:23,923 INFO [train.py:445] Epoch 6, batch 11200, loss[loss=2.205, over 40400.00 frames., ppl: 9.070530457122128] tot2022-06-18 04:23:35,921 INFO [train.p2022022-06-18 04:23:36,266 INFO [train.py:445] Epoch 6, batch 11400, loss[loss=2.223, over 43200.00 frames., ppl: 9.23903218523581] tot_loss[loss=2.296, over 32363278.13 frames., 2022-06-18 04:24:46,438 INFO [train.py:445] Epoch 6, batch 11600, loss2022-06-18 04:24:46,466 INFO [train.py:445] Epoch 6, batch 11600, loss[loss=2.254, over 34800.00 frames., ppl: 9.52804997614906] tot_loss[loss=2.3, 2022-06-18 04:26:00,054 INFO [train.py:445] Epoch 6,2022-06-18 04:26:00,356 INFO [train.py:445] Epoch 6, batch 11800, loss[loss=2.211, over 50800.00 frames., ppl: 9.12257145784846] tot_loss[loss=2.3, over 31832132.47 2022-06-18 04:27:09,728 INFO [train.py:445] Epoch 6, batch 2022-06-18 04:27:09,984 INFO [train.py:445] Epoch 6, batch 12000, loss[loss=2.15, over 52400.00 frames., ppl: 8.588595374662445] tot_loss[loss=2.301, over 3162022-06-18 04:28:20,942 INFO [train.py:445] 2022-06-18 04:28:21,072 INFO [train.p2022-06-18 04:28:21,230 INFO [train.py:445] Epoch 6, batch 12200, loss[loss=2.224, over 52400.00 frames., ppl: 9.24805139887964] tot_loss2022-06-18 04:29:32,058 INFO [train.py:2022-2022-02022-06-18 04:29:32,196 INFO [train.py:445] Epoch 6, batch 12400, loss[loss=2.229, over 31600.00 frames., ppl: 9.289814747401282] tot_loss[loss=2.301, over 31569672.75 2022-06-18 04:30:46,218 INFO [train.py:2022-06-18 2022-06-12022-06-18 04:30:46,424 INFO [train.py:445] Epoch 6, batch 12600, loss[loss=2.236, over 31600.00 frames., ppl: 9.353384269832175] tot_loss[loss=2.299, over 3202022-06-18 04:31:16,828 INFO [train.py:445] Epoch 7, batch 2022-06-18 04:31:16,921 INFO [train.py:445] Epoch 7, batch 0, loss[loss=2.222, over 37200.00 frames., ppl: 9.228559852805297] tot_loss[loss=2.222, over 2022-06-18 04:32:34,602 INFO [train.py:445] Epoch 7, batch 2022-06-18 04:32:34,707 INFO [train.py:445] Epoch 7, batch 200, loss[loss=2.23, over 33600.00 frames., ppl: 9.29726728923948] tot_loss[loss=2.282, over 292022-06-18 04:33:48,750 INFO [train.py:4452022-06-182022-06-18 042022-06-18 04:33:48,982 INFO [train.py:445] Epoch 7, batch 400, loss[loss=2.18, over 39600.00 frames., ppl: 8.844669368816836] tot_loss[loss=2.281, o2022-06-18 04:35:03,526 INFO [train.py:445] Epoch 7, batch 600, loss[loss=2.226, over 22800.00 frames., ppl: 9.262596820570977] tot_loss[loss=2.285, over 8259809.91 frames., ppl: 9.826097995224783], batch size: 400
+2022-06-18 04:36:15,082 INFO [train.py:422022-06-18 04:36:2022-06-18 04:36:15,567 INFO [train.py:445] Epoch 7, batch 800, loss[loss=2.232, over 62800.00 frames., ppl: 9.315585831864466] tot_loss[loss=2.285, over 10392022-06-18 04:37:29,521 INFO [train.py:42022-06-18 02022-02022-06-18 04:37:29,798 INFO [train.py:445] Epoch 7, batch 1000, loss[loss=2.203, over 42800.00 frames., ppl: 9.048618340681434] tot_loss[loss=2.286, over 1242022-06-18 04:38:41,357 INFO [train.py:442022-06-18 04:38:41,787 INFO [train.py:445] Epoch 7, batch 1200, loss[loss=2.217, over 52400.00 frames., ppl: 9.182509312620692] tot_loss[loss=2.284, over 14403244.38 frames.,2022-06-18 04:39:54,423 INFO [train.py:2022-06-18 04:39:54,595 INFO [train.py:445] Epoch 7, batch 1400, loss[loss=2.217, over 35200.00 frames., ppl: 9.17968262313996] tot_loss[loss=2.285, over 15988624.29 frames., ppl2022-06-18 04:41:03,512 INFO [train.py:4452022-06-182022-06-18 04:41:03,936 INFO [train.py:445] Epoch 7, batch 1600, loss[loss=2.188, over 61200.00 frames., ppl: 8.914200143265093] tot_loss[loss=2.283, over 17825513.22022-06-18 04:42:17,574 INFO [train.py2022-06-18 04:42:17,791 INFO [train.py:445] Epoch 7, batch 1800, loss[loss=2.226, over 27200.00 frames., ppl: 9.264991691783518] tot_loss[loss=2.286, over 18798106.93 frames., pp2022-06-18 04:43:30,040 INFO [train.py:2022-06-18 042022-06-18 04:43:30,290 INFO [train.py:445] Epoch 7, batch 2000, loss[loss=2.192, over 39200.00 frames., ppl: 8.957347016181895] tot_loss[loss=2.285, over 20373807.362022-06-18 04:44:44,719 INFO [train.py:445] Epoch 7, batch 2200, loss[loss=2.281, over 32800.00 frames., ppl: 9.782496823414077] tot_loss[loss=2.288, over 21093875.54 frames., ppl: 9.851088813305633], batch size: 400
+2022-06-18 04:45:57,418 INFO [train.py:445] Epoch 7, batch 2400, loss[loss=2.211, over 60800.00 frames., ppl: 9.123736464439327] tot_loss[loss=2.287, over 22264935.37 frames., ppl: 9.844398975610531], batch size: 400
+2022-06-18 04:47:07,336 INFO [train.2022-06-18 04:47:07,528 INFO [train.py:445] Epoch2022-06-18 04:47:07,641 INFO [train.py:445] Epoch 7, batch 2600, loss[loss=2.187, over 37600.00 frames., ppl: 8.905849972014245] to2022-06-18 04:48:19,891 INFO [train.py:445] Epoch 2022-06-18 04:48:19,991 INFO [train.2022-06-18 04:48:20,052 INFO [train.py:445] Epoch 7, batch 2800, loss[loss=2.247, over 27200.00 frames., ppl: 9.457687427712766] tot2022-06-18 04:49:32,424 INFO [train.py2022-06-18 04:49:32,843 INFO [train.py:445] Epoch 7, batch 3000, loss[loss=2.221, over 71600.00 frames., ppl: 9.217897707577091] tot_loss[loss=2.288, over 24717603.85 frames., pp2022-06-18 04:50:45,154 INFO [train.p2022-06-18 02022-06-18 04:502022-06-18 04:50:45,438 INFO [train.py:445] Epoch 7, batch 3200, loss[loss=2.243, over 36800.00 frames., ppl: 9.424205778933379] tot_loss[loss=2.289, 2022-06-18 04:51:57,490 INFO [train.py:445] Epoch 7, batch 342022-02022-06-18 04:51:57,575 INFO [train.py:445] Epoch 7, batch 3400, loss[loss=2.261, over 23600.00 frames., ppl: 9.597405643406944] tot_loss[loss=2.288,2022-06-18 04:53:06,975 INFO [train.py:445] Epoch 7, batch 3600, loss[loss=2.221, over 22022-06-18 04:53:07,108 INFO [train.py:445] Epoch 7, batch 3600, loss[loss=2.215, over 40000.00 frames., ppl: 9.15890795896178]2022-06-18 04:54:19,878 INFO [train.py:445] Epoch 7, batch 3800, loss[loss=2.225, over 44000.00 frames., ppl: 9.252889693069793] tot_loss[loss=2.289, over 26993590.14 frames., ppl: 9.868820395058853], batch size: 400
+2022-06-18 04:55:33,085 INFO [train.p2022-06-18 04:55:33,135 INFO2022-06-18 04:55:33,163 INFO [train.py:445] Epoch 7, batch 4000, loss[loss=2.293, over 17200.00 frames., ppl: 9.908900682463809] tot_loss[loss=2.29, ove2022-06-18 04:56:45,243 INFO [train.py2022-06-18 04:56:45,409 INFO [train.py:445] Epoch 7, batch 4200, loss[loss=2.25, over 26000.00 frames., ppl: 9.485882943905407] tot_loss[loss=2.291, over 27883666.98 frames., ppl:2022-06-18 04:57:58,349 INFO [train.p2022-06-18 04:57:58,374 INFO [train.py:445] Epoch 7, batch 4400, loss[loss=2.271, over 24800.00 frames., ppl: 9.686904769048626] tot_loss[loss=2.291, over 28213576.21 frames., pp2022-06-18 04:59:14,490 INFO [train.py:2022-02022-06-18 04:59:14,714 INFO [train.py:445] 2022-06-18 04:59:14,792 INFO [train.py:445] Epoch 7, batch 4600, loss[loss=2.231, over 37600.00 frames., ppl: 9.3138201534836582022-06-18 05:00:31,047 INFO [train.py:445] 2022-06-18 05:00:31,235 INFO [train.py:445] Epoch 7, batch 4800, loss[loss=2.211, over 33200.00 frames., ppl: 9.127189039487293] tot_loss[loss=2.29, over 28888675.72 frames.,2022-06-18 05:01:41,726 INFO [train.py2022-06-18 02022-06-18 05:01:41,870 INFO [train.py:445] Epoch 7, batch 5000, loss[loss=2.235, over 25600.00 frames., ppl: 9.349009349313274] tot_loss[loss=2.288, over 29425127.10 f2022-06-18 05:02:56,438 INFO [train.p2022-06-18 05:02:56,793 INFO [train.py:445] Epoch 7, batch 5200, loss[loss=2.207, over 39600.00 frames., ppl: 9.083922351622286] tot_loss[loss=2.29, over 29585841.92 frames., ppl: 2022-06-18 05:04:08,088 INFO [train.2022022-06-182022-06-18 05:042022-06-18 05:04:08,195 INFO [train.py:445] Epoch 7, batch 5400, loss[loss=2.256, over 16800.00 frames., ppl: 9.54579704825606] tot_loss[loss=2.29, over2022-06-18 05:05:22,670 INFO [train2022-2022-06-18 05:05:22,871 INFO [train.py:445] Epoch 7, batch 5600, loss[loss=2.194, over 31600.00 frames., ppl: 8.968767546686097] tot_loss[loss=2.291, over 29807593.45 frames., 2022-06-18 05:06:37,411 INFO [train.py:445] Epoch 7, batch 5800, loss[loss=2.203, over 53200.00 frames., ppl: 9.056156861370155] tot_loss[loss=2.293, over 29942946.29 frames., ppl: 9.905111767728666], batch size: 400
+2022-06-18 05:07:51,260 INFO [train.2022-06-18 05:07:51,394 IN2022-06-18 05:07:51,502 INFO [train.py:445] Epoch 7, batch 6000, loss[loss=2.189, over 34400.00 frames., ppl: 8.92843391939511] tot_loss[loss=2.291, over 32022-06-18 05:09:04,425 INFO [train.2022-06-18 05:09:04,850 INFO [train.py:445] Epoch 7, batch 6200, loss[loss=2.231, over 51657.00 frames., ppl: 9.311978125321087] tot_loss[loss=2.288, over 31124479.93 frames., ppl: 2022-06-18 05:10:19,948 INFO [train.2022-06-2022-06-18 05:10:20,010 INFO [train.py:445] Epoch 7, batch 6400, loss[loss=2.243, over 26800.00 frames., ppl: 9.419767996107387] tot_loss[loss=2.29, over 30490610.88 frames.2022-06-18 05:11:33,966 INFO [train.py:42022-06-18 05:11:34,119 INFO [train.py:445] Epoch 7, batch 6600, loss[loss=2.19, over 45200.00 frames., ppl: 8.936291162781103] tot_loss[loss=2.292, over 30588967.94 frames., p2022-06-18 05:12:42,830 INFO [train.p2022-06-18 05:12:43,104 INFO [train.py:445] Epoch 7, batch 6800, loss[loss=2.203, over 44000.00 frames., ppl: 9.055038814390786] tot_loss[loss=2.288, over 31754019.26 frames., ppl:2022-06-18 05:13:53,515 INFO [train.py:445] Epoch 7, batch 702022-06-18 05:13:53,675 INFO [train.py:445] Epoch 7, batch 7000, loss[loss=2.248, over 36800.00 frames., ppl: 9.464934220912896] tot_loss[loss=2.292, over 2022-06-18 05:15:08,140 INFO [train.py2022-06-18 05:2022-06-18 05:15:08,365 INFO [train.py:445] Epoch 7, batch 7200, loss[loss=2.194, over 32400.00 frames., ppl: 8.971400210082301] tot_loss[loss=2.293, over 30818511.2022-06-18 05:16:20,219 INFO [train.py:2022-06-18 05:2022-06-18 05:16:20,591 INFO [train.py:445] Epoch 7, batch 7400, loss[loss=2.199, over 44400.00 frames., ppl: 9.020111037511338] tot_loss[loss=2.292, over 31026972022-06-18 05:17:31,743 INFO [train.py:445] 2022-06-18 2022-06-18 05:17:31,893 INFO [train.py:445] Epoch 7, batch 7600, loss[loss=2.232, over 26400.00 frames., ppl: 9.3211889194531] tot_loss[loss=2.292, over 31120987.92022-06-18 05:18:46,637 INFO [train.py:445] Epoch 7, batch 7800, loss[loss=2.245, over 34000.00 frames., ppl: 9.44338784626367] tot_loss[loss=2.289, over 32075323.72 frames., ppl: 9.864960306239414], batch size: 400
+2022-06-18 05:20:00,464 INFO [train2022-06-1822022-02022-06-18 05:20:00,759 INFO [train.py:445] Epoch 7, batch 8000, loss[loss=2.22, over 42000.00 frames., ppl: 9.206126935374826] tot_loss[loss=2.292, over 31423122.58 2022-06-18 05:21:13,454 INFO [train.py:445] E2022-2022-06-18 05:21:13,617 INFO [train.py:445] Epoch 7, batch 8200, loss[loss=2.218, over 32800.00 frames., ppl: 9.191160321151184] tot_loss[loss=2.291, over 31656756.962022-06-18 05:22:25,455 INFO [train.py:445] Epoch 7, batch 8400, loss[loss=2.204, over 56400.00 frames., ppl: 9.064909643654156] tot_loss[loss=2.289, over 32371944.41 frames., ppl: 9.861818768492386], batch size: 400
+2022-06-18 05:23:37,566 INFO [train.2022-06-202022-06-18 05:23:37,750 INFO [train.py:445] Epoch 7, batch 8600, loss[loss=2.158, over 33200.00 frames., ppl: 8.656552489736475] tot_loss[loss=2.291, over 31723107.78 frame2022-06-18 05:24:50,221 INFO [train2022-06-18 05:24:50,488 INFO [train.py:445] Epoch 7, batch 8800, loss[loss=2.204, over 36400.00 frames., ppl: 9.056770686904061] tot_loss[loss=2.291, over 31613934.08 frames., ppl: 92022-06-18 05:26:02,600 INFO [train2022-06-18 05:2022-06-18 05:26:02,651 INFO [train.py:445] Epoch 7, batch 9000, loss[loss=2.285, over 11600.00 frames., ppl: 9.826616028861276] tot_loss[loss=2.292, over 31691100.672022-06-18 05:27:12,342 INFO [train.p2022-06-18 05:2022-06-18 05:27:12,513 INFO [train.py:445] Epoch 7, batch 9200, loss[loss=2.208, over 24800.00 frames., ppl: 9.100876392245894] tot_loss[loss=2.293, over 31430260.672022-06-18 05:28:28,013 INFO [train.py2022-06-18 05:28:28,266 INFO [train.py:445] Epoch 7, batch 9400, loss[loss=2.213, over 60800.00 frames., ppl: 9.143843375638626] tot_loss[loss=2.293, over 31396588.24 frames., ppl2022-06-18 05:29:39,956 INFO [train.py:442022-06-18 05:29:40,039 INFO [train.py:445] Epoch 7, batch 9600, loss[loss=2.229, over 24800.00 frames., ppl: 9.294671804647438] tot_loss[loss=2.291, over 32075596.10 frames., 2022-06-18 05:30:54,174 INFO [train.p2022-06-18 05:30:54,270 INFO [train.py:445] Epoch 7, batch 9800, loss[loss=2.247, over 17600.00 frames., ppl: 9.457473816226988] tot_loss[loss=2.294, over 31286847.90 frames., ppl2022-06-18 05:32:06,545 INFO [train.py:445] Epoch 7, batch 1002022-06-18 05:32:06,52022-06-18 05:32:06,707 INFO [train.py:445] Epoch 7, batch 10000, loss[loss=2.183, over 31200.00 frames., ppl: 8.872043456840244] tot_loss[loss=2.295, over 31439734.77 frames., ppl: 9.921154294487748], bat2022-06-18 05:32:06,889 INFO [train.p2022-06-18 05:32:06,889 INFO [train.py:480] Epoch 7, validation: loss=2.345, over 2118092022-06-18 05:33:18,374 INFO [train.py:442022-06-12022-06-18 05:33:18,587 INFO [train.py:445] Epoch 7, batch 10200, loss[loss=2.205, over 38800.00 frames., ppl: 9.067760805058292] tot_loss[loss=2.294, over 31721595.012022-06-18 05:34:32,045 INFO [train.py:445] Epoch 7, batch 10400, loss[loss=2.452, over 75232.00 frames., ppl: 11.609469678565633] tot_loss[loss=2.292, over 32237851.66 frames., ppl: 9.89168849631456], batch size: 51
+2022-06-18 05:35:43,113 INFO [train.py:445] Epoch 7, batch 10600, loss[loss=2.195, over 51600.00 frames., ppl: 8.976661098122218] tot_loss[loss=2.293, over 32000461.20 frames., ppl: 9.903414211416255], batch size: 400
+2022-06-18 05:36:57,726 INFO [train.py:42022-02022-06-18 05:36:57,828 INFO [train.py:445] Epoch 7, batch 10800, loss[loss=2.221, over 35200.00 frames., ppl: 9.213823406670057] tot_loss[loss=2.295, over 31512757.54 fra2022-06-18 05:38:09,684 INFO [train.py2022-06-2022022-06-18 05:38:2022-06-18 05:38:09,775 INFO [train.py:445] Epoch 7, batch 11000, loss[loss=2.338, over 17200.00 frames., ppl: 10.35642833286986] tot_loss[loss=2.295, o2022-06-18 05:39:22,155 INFO [train.py:4452022-06-18 05:39:22,280 INFO [train.py:445] Epoch 7, batch 11200, loss[loss=2.204, over 26800.00 frames., ppl: 9.064016327610437] tot_loss[loss=2.291, over 32306442.14 frame2022-06-18 05:40:36,502 INFO [train.py:445] Epoch 7, batch 11400, loss[loss=2.256, over 16800.00 frames., ppl: 9.544474294151957] tot_loss[loss=2.291, over 32509863.90 frames., ppl: 9.888230977925403], batch size: 400
+2022-06-18 05:41:49,834 INFO [train.py:445] Epoch 7, batch 11600, loss[loss=2.225, over 66400.00 frames., ppl: 9.256494769592866] tot_loss[loss=2.291, over 32616857.00 frames., ppl: 9.882264552924893], batch size: 400
+2022-06-18 05:43:01,182 INFO [train.py:445202022-2022-06-18 05:43:01,313 INFO [train.py:445] Epoch 7, batch 11800, loss[loss=2.26, over 24800.00 frames., ppl: 9.58654790211974] tot_loss[loss=2.293, over 32007347.48 fram2022-06-18 05:44:15,571 INFO [train.py:445]2022-06-18 05:44:15,659 2022-06-18 05:44:15,691 INFO [train.py:445] Epoch 7, batch 12000, loss[loss=2.213, over 23600.00 frames., ppl: 9.143263810658269] tot_loss[loss=2.292, o2022-06-18 05:45:28,214 INFO [train.py:420222022-06-18 05:45:28,565 INFO [train.py:445] Epoch 7, batch 12200, loss[loss=2.19, over 44000.00 frames., ppl: 8.932150092857857] tot_loss[loss=2.292, over 32429295.21 frames2022-06-18 05:46:41,174 INFO [train.py:442022-06-18 05:46:41,463 INFO [train.py:445] Epoch 7, batch 12400, loss[loss=2.185, over 40000.00 frames., ppl: 8.887431484821448] tot_loss[loss=2.292, over 32431114.90 frames., 2022-06-18 05:47:54,987 INFO [train.py:42022-06-18 05:47:55,058 INFO [train.py:445] Epoch 7, batch 12600, loss[loss=2.251, over 22000.00 frames., ppl: 9.501222310047153] tot_loss[loss=2.293, over 32285159.07 frames., pp2022-06-18 05:48:23,687 INFO [train.py:2022-06-18 05:48:23,708 INFO [train.py:442022-06-18 05:48:23,721 INFO [train.py:445] Epoch 8, batch 0, loss[loss=2.26, over 16400.00 frames., ppl: 9.586947465338929] tot_loss2022-06-18 05:49:43,159 INFO [train.p2022-06-18 05:49:43,358 INFO2022-06-18 05:49:43,459 INFO [train.py:445] Epoch 8, batch 200, loss[loss=2.216, over 34400.00 frames., ppl: 9.168952401304129] tot_loss[loss=2.276,2022-06-18 05:50:55,636 INFO [train.py202022-06-18 05:50:56,060 INFO [train.py:445] Epoch 8, batch 400, loss[loss=2.192, over 44622.00 frames., ppl: 8.955010445971531] tot_loss[loss=2.281, over 5609301.62 frames., 2022-06-18 05:52:06,263 INFO [train.py:445] Epoch 8, batch 600, loss[loss=2.238, over 22000.00 frames., ppl: 9.37590509624075] tot_loss[loss=2.28, over 8205146.91 frames., ppl: 9.776998962339668], batch size: 400
+2022-06-18 05:53:21,215 INFO [train.py:42022-06-18 02022-06-18 05:53:21,642 INFO [train.py:445] Epoch 8, batch 800, loss[loss=2.212, over 50400.00 frames., ppl: 9.134875943386223] tot_loss[loss=2.275, over 10661328.32022-06-18 05:54:32,711 INFO [train.py:442022-06-18 05:54:32,995 INFO [train.py:445] Epoch 8, batch 1000, loss[loss=2.153, over 55600.00 frames., ppl: 8.61249404126865] tot_loss[loss=2.284, over 12256642.14 frames.,2022-06-18 05:55:45,551 INFO [train.py:445]2022-06-18 2022-06-18 05:55:45,651 INFO2022-06-18 05:55:45,666 INFO [train.py:445] Epoch 8, batch 1200, loss[loss=2.217, over 31600.00 frames., ppl: 9.17792666059833] tot_los2022-06-18 05:56:59,749 INFO [train.py:4452022-06-18 05:56:59,866 INFO [train.py:2022-06-18 05:56:59,878 INFO [train.py:445] Epoch 8, batch 1400, loss[loss=2.276, over 18800.00 frames., ppl: 9.736915754966999] tot_lo2022-06-18 05:58:13,475 INFO [train.py:4422022-06-18 05:58:13,645 INFO [train.py:42022-06-18 05:58:13,673 INFO [train.py:445] Epoch 8, batch 1600, loss[loss=2.194, over 28000.00 frames., ppl: 8.967807165553431] tot_2022-06-18 05:59:23,767 INFO [train.py:445]202022-06-18 05:59:23,925 INFO [train.py:445] Epoch 8, batch 1800, loss[loss=2.252, over 20800.00 frames., ppl: 9.506959540241482] tot_loss[loss=2.281, over 18880493.53 fram2022-06-18 06:00:35,609 INFO [train.py:445] 2022-06-18 062022-06-18 06:00:35,763 INFO [train.py:445] Epoch 8, batch 2000, loss[loss=2.206, over 21200.00 frames., ppl: 9.074955707387714] tot_loss[loss=2.28, over 2018942022-06-18 06:01:48,142 INFO [train.py:445] Epoch 8, batch 2200, loss[l2022-06-18 06:01:48,248 INFO [train.py:445] Epoch 8, batch 2200, loss[loss=2.208, over 25200.00 frames., ppl: 9.096392571258516] tot_loss[loss=2.22022-06-18 06:03:02,829 INFO [train.py:445] Epoch 8, batch 2400, loss[loss=2.248, over 45426.00 frames., ppl: 9.473306016087744] tot_loss[loss=2.283, over 21795500.98 frames., ppl: 9.802019643150333], batch size: 201
+2022-06-18 06:04:19,572 INFO [train.py:442022-06-18 06:04:19,793 INFO [train.py:445] Epoch 8, batch 2600, loss[loss=2.21, over 28400.00 frames., ppl: 9.116476234914627] tot_loss[loss=2.282, over 22824816.79 frames., p2022-06-18 06:05:33,965 INFO [train.py:422022022-06-18 06:05:34,123 INFO [train.py:445] Epoch 8, batch 2800, loss[loss=2.23, over 23200.00 frames., ppl: 9.29865106321185] tot_loss[loss=2.284, over 23553377.97 frames2022-06-18 06:06:46,230 INFO [train.py:442022022-06-18 06:06:46,379 INFO [train.py:445] Epoch 8, batch 3000, loss[loss=2.201, over 29600.00 frames., ppl: 9.038484361909221] tot_loss[loss=2.285, over 24368578.05 frame2022-06-18 06:07:58,161 INFO [train.py:4452022-06-18 06:07:58,248 INFO [train.py:445] Epoch 8, batch 3200, loss[loss=2.265, over 20800.00 frames., ppl: 9.626502597075069] tot_loss[loss=2.281, over 25447030.24 frames., 2022-06-18 06:09:12,269 INFO [train.py:442022-06-18 06:09:12,546 INFO 2022-06-18 06:09:12,861 INFO [train.py:445] Epoch 8, batch 3400, loss[loss=2.19, over 66000.00 frames., ppl: 8.93134498801086] tot_loss[loss=2.272022-06-18 06:10:22,456 INFO [train.py:445]2022-06-18 06:2022-06-18 06:10:22,935 INFO [train.py:445] Epoch 8, batch 3600, loss[loss=2.232, over 60000.00 frames., ppl: 9.317870491776578] tot_loss[loss=2.28, over 2697152022-06-18 06:11:36,327 INFO [train.py:445]2022-06-18 06:11:36,773 INFO [train.py:445] Epoch 8, batch 3800, loss[loss=2.173, over 52800.00 frames., ppl: 8.783558302640277] tot_loss[loss=2.285, over 26581114.77 frames.2022-06-18 06:12:48,876 INFO [train.py:445]2022-06-18 06:12:49,054 INFO [train.py:445] Epoch 8, batch 4000, loss[loss=2.274, over 23600.00 frames., ppl: 9.717059744897815] tot_loss[loss=2.285, over 27652371.59 frames.2022-06-18 06:14:00,589 INFO [train.py:445202022-06-18 06:14:00,888 INFO [train.py:445] Epoch 8, batch 4200, loss[loss=2.19, over 41600.00 frames., ppl: 8.937352868447212] tot_loss[loss=2.281, over 28607674.91 frames.2022-06-18 06:15:14,817 INFO [train.py:445] Epoch 8, batch 4400, loss[loss=2.2022-06-18 06:15:15,079 INFO [train.py:445] Epoch 8, batch 4400, loss[loss=2.194, over 52000.00 frames., ppl: 8.969811249350444] tot_loss[lo2022-06-18 06:16:28,028 INFO [train.py:42022022-06-18 06:16:28,409 INFO [train.py:445] Epoch 8, batch 4600, loss[loss=2.223, over 46800.00 frames., ppl: 9.233910320701849] tot_loss[loss=2.28, over 29645081.83 frames.,2022-06-18 06:17:40,839 INFO [train.py:42022-06-18 06:17:41,042 INFO [train.py:445] Epoch 8, batch 4800, loss[loss=2.223, over 47200.00 frames., ppl: 9.2390447023241] tot_loss[loss=2.283, over 29078553.75 frames., pp2022-06-18 06:18:53,094 INFO [train.py:202022-06-18 02022-06-18 06:18:53,384 INFO [train.py:445] Epoch 8, batch 5000, loss[loss=2.208, over 45200.00 frames., ppl: 9.101894803403363] tot_loss[loss=2.285, over 29214911.2022-06-18 06:20:05,979 INFO [train.py:42022-06-18 06:20:06,013 INFO [train.py:445] Epoch 8, batch 5200, loss[loss=2.211, over 35600.00 frames., ppl: 9.121421501717652] tot_loss[loss=2.281, over 30340498.76 frames., p2022-06-18 06:21:19,231 INFO [train.py:2202022-06-12022-06-18 06:21:19,555 INFO [train.py:445] Epoch 8, batch 5400, loss[loss=2.192, over 41600.00 frames., ppl: 8.957198915666575] tot_loss[loss=2.287, over 29473405.912022-06-18 06:22:33,831 INFO [train.py:202022-06-18 06:22:33,944 INFO [train.py:445] Epoch 8, batch 5600, loss[loss=2.271, over 20400.00 frames., ppl: 9.687242182864823] tot_loss[loss=2.285, over 30254634.10 frames.,2022-06-18 06:23:44,928 INFO [train.py:2022-06-18 06:23:45,226 INFO [train.py:445] Epoch 8, batch 5800, loss[loss=2.244, over 55677.00 frames., ppl: 9.42784133773885] tot_loss[loss=2.284, over 30399639.72 frames., pp2022-06-18 06:24:55,147 INFO [train.py:2022-06-18 06:24:55,354 INFO [train.py:445] Epoch 8, batch 6000, loss[loss=2.274, over 23200.00 frames., ppl: 9.722535030604359] tot_loss[loss=2.283, over 30761140.14 frames., 2022-06-18 06:26:08,993 INFO [train.py:44202022-06-18 06:26:09,103 INFO2022-06-18 06:26:09,344 INFO [train.py:445] Epoch 8, batch 6200, loss[loss=2.214, over 57200.00 frames., ppl: 9.153298120252156] tot_loss[loss=2.282022-06-18 06:27:21,176 INFO [train.py:442022-06-18 06:27:21,401 INFO [trai2022-06-18 06:27:21,444 INFO [train.py:445] Epoch 8, batch 6400, loss[loss=2.186, over 34400.00 frames., ppl: 8.897904239927742] tot_loss[loss2022-06-18 06:28:32,774 INFO [train.py:445] Epoch 8, 2022-06-18 06:22022-06-18 06:28:33,103 INFO [train.py:445] Epoch 8, batch 6600, loss[loss=2.18, over 48400.00 frames., ppl: 8.845235376730763] tot_loss[loss=2.286, o2022-06-18 06:29:47,418 INFO [train.py:42022-06-18 06:29:47,754 INFO [train.py:445] Epoch 8, batch 6800, loss[loss=2.202, over 51200.00 frames., ppl: 9.038712336744185] tot_loss[loss=2.286, over 31114279.10 frames., 2022-06-18 06:31:00,716 INFO [train.py:2022-06-18 06:31:00,903 INFO [train.py:445] Epoch 8, batch 7000, loss[loss=2.209, over 64400.00 frames., ppl: 9.103538103380439] tot_loss[loss=2.282, over 32028938.45 frames., pp2022-06-18 06:32:11,290 INFO [train.py22022-06-18 06:32:11,471 INFO [train.py:445] Epoch 8, batch 7200, loss[loss=2.243, over 25200.00 frames., ppl: 9.425895588649562] tot_loss[loss=2.282, over 32178436.00 frames., pp2022-06-18 06:33:24,373 INFO [train.py2022-06-18 062022-06-18 06:33:24,693 INFO [train.py:445] Epoch 8, batch 7400, loss[loss=2.207, over 46800.00 frames., ppl: 9.086855568451893] tot_loss[loss=2.288, over 30736707.422022-06-18 06:34:39,700 INFO [train.py2022-06-18 062022-06-18 06:34:39,974 INFO [train.py:445] Epoch 8, batch 7600, loss[loss=2.207, over 45200.00 frames., ppl: 9.08720513405526] tot_loss[loss=2.289, over 30782975.21 2022-06-18 06:35:53,671 INFO [train.py:445] Epoch 8, batch 7800, loss[loss=2.19, over 38400.00 frames., ppl: 8.938393141657217] tot_loss[loss=2.287, over 31286196.00 frames., ppl: 9.8449101542106], batch size: 400
+2022-06-18 06:37:04,857 INFO [train.py:445] Epoch 8, batch 8000, loss[loss=2.215, over 30800.00 frames., ppl: 9.159499134624753] tot_loss[loss=2.287, over 31284264.21 frames., ppl: 9.848791690902564], batch size: 400
+2022-06-18 06:38:20,068 INFO [train.py:445] Epoch 8, batch 8200, loss[loss=2.211, over 49600.00 frames., ppl: 9.124280755991645] tot_loss[loss=2.287, over 31436524.38 frames., ppl: 9.842343128305641], batch size: 400
+2022-06-18 06:39:32,597 INFO [train.py:22022-06-18 06:39:32,731 INFO [train.py:445] Epoch 8, batch 8400, loss[loss=2.251, over 26400.00 frames., ppl: 9.492835966206403] tot_loss[loss=2.286, over 31606997.22 frames., pp2022-06-18 06:40:45,853 INFO [train.py:445] Epoch 8, batch 8600, loss[loss=2.212, over 62400.00 frames., ppl: 9.135308268207858] tot_loss[loss=2.285, over 31888042.45 frames., ppl: 9.830004663741898], batch size: 400
+2022-06-18 06:41:56,996 INFO [train.p2202022-06-18 06:41:57,153 INFO [train.py:445] Epoch 8, batch 8800, loss[loss=2.227, over 22800.00 frames., ppl: 9.27044275293979] tot_loss[loss=2.289, over 31167083.95 frames., p2022-06-18 06:43:14,486 INFO [train.py:445] Epoch 8, batch 9000, loss[loss=2.224, over 28800.00 frames., ppl: 9.241520806779594] tot_loss[loss=2.288, over 31448923.52 frames., ppl: 9.853028901721027], batch size: 400
+2022-06-18 06:44:28,876 INFO [train.py:445] Epoch 8, batch 9200,2022-06-12022-06-18 06:44:28,912 INFO [train.py:445] Epoch 8, batch 9200, loss[loss=2.214, over 30400.00 frames., ppl: 9.147716793955457] tot_loss[loss=2022-06-18 06:45:40,663 INFO [train.py:2022-06-18 06:45:40,769 INFO [train.py:445] Epoch 8, batch 9400, loss[loss=2.292, over 16400.00 frames., ppl: 9.894031418018258] tot_loss[loss=2.288, over 31767344.94 frames., pp2022-06-18 06:46:51,558 INFO [train.py:4202022-06-18 06:46:51,729 INFO [train.py:445] Epoch 8, batch 9600, loss[loss=2.239, over 29600.00 frames., ppl: 9.379304333907756] tot_loss[loss=2.289, over 31335068.26 frames., 2022-06-18 06:48:01,676 INFO [train.py:445] Epoch 8, batch 9800, loss[loss=2.236, over 37600.00 frames., ppl: 9.35451938100918] tot_loss[loss=2.287, over 32102355.70 frames., ppl: 9.844192344777984], batch size: 400
+2022-06-18 06:49:12,906 INFO [train.py2022-06-18 06:49:13,045 INFO [train.py:445] Epoch 8, batch 10000, loss[loss=2.228, over 29200.00 frames., ppl: 9.2850619363885] tot_loss[loss=2.288, over 31995595.42 frames., ppl: 9.85283462723344], batch size: 400
+2022-06-18 06:49:13,045 INFO [train.2022-06-18 06:49:13,233 INFO [trai2022-06-18 06:49:13,233 INFO [train.py:480] Epoch 8, validation: loss=2.342, over 211809.002022-06-18 06:50:25,508 INFO [trai2022-06-18 2022-06-18 06:50:25,801 INFO [train.py:445] Epoch 8, batch 10200, loss[loss=2.208, over 36800.00 frames., ppl: 9.093905371287908] tot_loss[loss=2.292, over 31005131.86 frame2022-06-18 06:51:37,788 INFO [trai2022-06-2022-06-18 06:51:37,889 INFO [train.py:445] Epoch 8, batch 10400, loss[loss=2.233, over 19600.00 frames., ppl: 9.324415850158507] tot_loss[loss=2.291, over 31071249.78 frames.2022-06-18 06:52:48,135 INFO [train.2022-062022022-06-18 06:52:48,364 2022-06-18 06:52:48,437 INFO [train.py:445] Epoch 8, batch 10600, loss[loss=2.179, over 38400.00 frames., ppl: 8.833519033795072] tot_loss[loss=2.282022-06-18 06:54:06,122 INFO [train.2022-06-182022-06-18 06:54:06,166 INFO [train.py:445] Epoch 8, batch 10800, loss[loss=2.202, over 18400.00 frames., ppl: 9.040738185916029] tot_loss[loss=2.291, over 31389468.16 fra2022-06-18 06:55:22,571 INFO [tra2022-06-182022-06-18 06:552022-06-18 06:55:23,394 INFO [train.py:445] Epoch 8, batch 11000, loss[loss=2.223, over 66540.00 frames., ppl: 9.232017623534018] tot_loss[loss=2.289, over 31652022-06-18 06:56:35,729 INFO [tr2022-06-2022-06-18 06:56:35,914 INFO [train.py:445] Epoch 8, batch 11200, loss[loss=2.191, over 23200.00 frames., ppl: 8.948062802677534] tot_loss[loss=2.289, over 31913663.50 frames., p2022-06-18 06:57:50,502 INFO [tra202022-06-18 06:57:50,654 INFO [train.py:445] Epoch 8, batch 11400, loss[loss=2.256, over 25200.00 frames., ppl: 9.546185475634832] tot_loss[loss=2.287, over 32172909.10 frames., ppl: 92022-06-18 06:59:04,114 INFO [trai2022-06-18 06:59:04,195 INFO [tr2022-06-18 06:59:04,240 INFO [train.py:445] Epoch 8, batch 11600, loss[loss=2.218, over 18400.00 frames., ppl: 9.188774254165434] tot_loss[loss=2.287, 2022-06-18 07:00:17,261 INFO [tra2022-06-18 07:00:17,486 INFO [train.py:445] Epoch 8, batch 11800, loss[loss=2.207, over 36400.00 frames., ppl: 9.087953005667002] tot_loss[loss=2.292, over 31241664.12 frames., ppl: 9.2022-06-18 07:01:27,101 INFO [trai2022-06-18 07:01:27,216 INFO [train.py:445] Epoch 8, batch 12000, loss[loss=2.257, over 20800.00 frames., ppl: 9.554333457033112] tot_loss[loss=2.293, over 31060874.53 frames., ppl: 9.2022-06-18 07:02:40,183 INFO [trai22022-06-18 07:02:40,340 INFO [train.py:445] Epoch 8, batch 12200, loss[loss=2.187, over 23200.00 frames., ppl: 8.912832449008777] tot_loss[loss=2.287, over 32390459.06 frames., ppl:2022-06-18 07:03:54,087 INFO [train.2022-062022-06-18 07:03:54,245 INFO [train.py:445] Epoch 8, batch 12400, loss[loss=2.208, over 42000.00 frames., ppl: 9.098007482196516] tot_loss[loss=2.289, over 31943394.12 frames.2022-06-18 07:05:06,438 INFO [train.py:445]2022-06-18 07:05:06,442 INFO [train.py:445] Epoch 8, batch 12600, loss[loss=2.211, over 26400.00 frames., ppl: 9.12198641268987] tot_loss[loss=2.289, over 31975968.36 fram2022-06-18 07:05:33,109 INFO [train.p2022022-02022-06-18 07:05:33,148 INFO [train.py:445] Epoch 9, batch 0, loss[loss=2.269, over 21200.00 frames., ppl: 9.669976051083102] tot_loss[loss=2.269, over 21200.00 fr2022-06-18 07:06:51,858 INFO [train.py:445] Epoch 9, batch 200, loss[loss=2.2, over 40000.00 frames., ppl: 9.022877362361626] tot_loss[loss=2.271, over 3105564.64 frames., ppl: 9.68774243159985], batch size: 400
+2022-06-18 07:08:04,100 INFO [train.py:445]22022-06-18 07:08:04,509 INFO [train.py:445] Epoch 9, batch 400, loss[loss=2.185, over 52800.00 frames., ppl: 8.893476003903416] tot_loss[loss=2.263, over 6043253.82 frames2022-06-18 07:09:16,010 INFO [train.py:4452022-06-18 07:09:16,587 INFO [train.py:445] Epoch 9, batch 600, loss[loss=2.223, over 75833.00 frames., ppl: 9.236515620698817] tot_loss[loss=2.272, over 8345221.29 frames.,2022-06-18 07:10:27,451 INFO [train.py:445] Epoch2022-06-18 07:102022-06-18 07:10:27,565 INFO [train.py:445] Epoch 9, batch 800, loss[loss=2.175, over 34000.00 frames., ppl: 8.79908306047889] tot_loss[loss=2.27, ove2022-06-18 07:11:42,601 INFO [train.py:445] 2022-02022-06-18 07:11:42,760 INFO [train.py:445] Epoch 9, batch 1000, loss[loss=2.207, over 24000.00 frames., ppl: 9.092551541230037] tot_loss[loss=2.272, over 12861272.73 2022-06-18 07:12:53,047 INFO [train.py:202022-06-18 07:12:53,367 INFO [train.py:445] Epoch 9, batch 1200, loss[loss=2.204, over 49600.00 frames., ppl: 9.059129162191686] tot_loss[loss=2.275, over 14186171.00 frames., 2022-06-18 07:14:06,263 INFO [train.py202202022-06-18 07:14:06,603 INFO [train.py:445] Epoch 9, batch 1400, loss[loss=2.184, over 52400.00 frames., ppl: 8.886001106676964] tot_loss[loss=2.269, over 16507442.88 frames.2022-06-18 07:15:19,079 INFO [train.py:2022-06-18 07:15:19,118 INFO [train.py:445] Epoch 9, batch 1600, loss[loss=2.244, over 24400.00 frames., ppl: 9.431659909883539] tot_loss[loss=2.276, over 17504070.46 frames., pp2022-06-18 07:16:29,341 INFO [train.2022-06-18 07:16:29,652 INFO [train.py:445] Epoch 9, batch 1800, loss[loss=2.197, over 49600.00 frames., ppl: 8.999725846960143] tot_loss[loss=2.277, over 18822703.60 frames., ppl:2022-06-18 07:17:42,694 INFO [train.p2022022-06-18 07:17:42,717 INFO [train2022-06-18 07:17:42,755 INFO [train.py:445] Epoch 9, batch 2000, loss[loss=2.271, over 17600.00 frames., ppl: 9.688819263745593] tot_loss[loss2022-06-18 07:19:00,993 INFO [train.p2020222022-06-18 07:192022-06-18 07:19:01,208 INFO [train.py:445] Epoch 9, batch 2200, loss[loss=2.202, over 34800.00 frames., ppl: 9.045234205273115] tot_loss[loss=2.27, over 220602022-06-18 07:20:15,553 INFO [train.p2022-06-18 07:20:15,852 INFO [train.py:445] Epoch 9, batch 2400, loss[loss=2.237, over 36000.00 frames., ppl: 9.363543376549766] tot_loss[loss=2.275, over 22709608.53 frames., ppl:2022-06-18 07:21:28,628 INFO [train.py:44522022-06-18 07:2022-06-18 07:21:28,761 INFO [train.py:445] Epoch 9, batch 2600, loss[loss=2.233, over 24000.00 frames., ppl: 9.325485015492157] tot_loss[loss=2.271, over 240522022-06-18 07:22:44,092 INFO [train.py:445] Epoch 9, batch 2800, loss[loss=2.178, over 36000.00 frames., ppl: 8.826192677849773] tot_loss[loss=2.275, over 24152130.37 frames., ppl: 9.730891470969475], batch size: 400
+2022-06-18 07:23:54,505 INFO [train.22022-06-18 07:23:54,615 INFO [t2022-06-18 07:23:54,778 INFO [train.py:445] Epoch 9, batch 3000, loss[loss=2.191, over 30800.00 frames., ppl: 8.94394090213084] tot_loss[loss=2.282, 2022-06-18 07:25:06,038 INFO [train.py:445] Epoch 9, batch 3200, loss[loss=2.212, over 42411.00 frames., ppl: 9.131666472549849] tot_loss[loss=2.276, over 25409023.77 frames., ppl: 9.738939783009693], batch size: 201
+2022-06-18 07:26:19,721 INFO [train.py:445] Epoch 9, b2022-06-18 02022-06-18 07:26:19,744 INFO [train.py:445] Epoch 9, batch 3400, loss[loss=2.278, over 11600.00 frames., ppl: 9.756207587725031] tot_loss[loss=2.281, 2022-06-18 07:27:33,730 INFO [trai2022-02022-06-18 07:27:34,011 INF2022-06-18 07:27:34,172 INFO [train.py:445] Epoch 9, batch 3600, loss[loss=2.197, over 53200.00 frames., ppl: 8.993925726664322] tot_loss[loss=2.28, 2022-06-18 07:28:44,627 INFO [train.py:202022-06-18 07:28:44,974 INFO [train.py:445] Epoch 9, batch 3800, loss[loss=2.204, over 43200.00 frames., ppl: 9.059566333897259] tot_loss[loss=2.275, over 27837249.18 frames., 2022-06-18 07:29:56,284 INFO [trai2022-2022-06-18 07:29:56,604 INFO [train.py:445] Epoch 9, batch 4000, loss[loss=2.215, over 40400.00 frames., ppl: 9.159768736972122] tot_loss[loss=2.277, over 28124891.23 frames., pp2022-06-18 07:31:06,237 INFO [tra2022-06-18 07:31:06,392022-06-18 07:31:06,427 INFO [train.py:445] Epoch 9, batch 4200, loss[loss=2.247, over 26800.00 frames., ppl: 9.458718853353618] tot_loss[loss=2.279, over 27918222022-06-18 07:32:21,378 INFO [train.py:445] Epoch 9, ba2022-06-18 07:32:21,486 INFO [train.py:445] Epoch 9, batch 4400, loss[loss=2.214, over 30000.00 frames., ppl: 9.151920995205803] tot_loss[loss=2.278, over 28448502022-06-18 07:33:33,067 INFO [train.py:4422022-06-18 07:33:33,110 INFO [train.py:445] Epoch 9, batch 4600, loss[loss=2.33, over 13200.00 frames., ppl: 10.279819850044323] tot_loss[loss=2.284, over 28044660.39 frames.,2022-06-18 07:34:48,551 INFO [train2022-2022-06-18 07:34:48,672 INFO [train.py:445] Epoch 9, batch 4800, loss[loss=2.205, over 18400.00 frames., ppl: 9.07208490518058] tot_loss[loss=2.275, over 29981204.51 frames., pp2022-06-18 07:36:04,289 INFO [train.py:445] Epoch 9, batch 5000, loss[loss=2.235, over 55600.00 frames., ppl: 9.345109556151597] tot_loss[loss=2.277, over 29717442.34 frames., ppl: 9.750392855664636], batch size: 400
+2022-06-18 07:37:15,687 INFO [train.py:2022-06-18 07:37:15,934 INFO [train.py:445] Epoch 9, batch 5200, loss[loss=2.183, over 41200.00 frames., ppl: 8.87662468741523] tot_loss[loss=2.284, over 28802526.96 frames., ppl2022-06-18 07:38:30,835 INFO [tr2022-06-18 07:38:30,2022-06-18 02022-06-18 07:38:30,886 INFO [train.py:445] Epoch 9, batch 5400, loss[loss=2.357, over 11200.00 frames., ppl: 10.559640897310482] tot_loss[loss=2.282, ov2022-06-18 07:39:40,638 INFO [train.py:445] Epoch 9, batch 5600, loss[loss=2.242, over 27200.00 frames., ppl: 9.414783203302195] tot_loss[loss=2.279, over 30396792.94 frames., ppl: 9.769425935481715], batch size: 400
+2022-06-18 07:40:52,438 INFO [train.py:445] Epoch 9, batch 5800, loss[loss=2.226, over 30800.00 frames., ppl: 9.261840153482042] tot_loss[loss=2.279, over 30550936.22 frames., ppl: 9.771355390403082], batch size: 400
+2022-06-18 07:42:05,682 INFO [train.p2022-06-18 07:42:05,783 INFO [train.py:445] Epoch 9, batch 6000, loss[loss=2.212, over 25600.00 frames., ppl: 9.13033862516327] tot_loss[loss=2.283, over 30131656.85 frames., ppl:2022-06-18 07:43:22,157 INFO [train.p2022-06-18 07:43:22,279 INFO [train.py:445] Epoch 9, batch 6200, loss[loss=2.189, over 38000.00 frames., ppl: 8.924109787088403] tot_loss[loss=2.283, over 30301648.84 frames., pp2022-06-18 07:44:36,905 INFO [train.py:445] Epoch 9, batch 6400, loss[loss=2.184, over 68000.00 frames., ppl: 8.881995008166681] tot_loss[loss=2.279, over 31030613.37 frames., ppl: 9.768029337010462], batch size: 400
+2022-06-18 07:45:49,059 INFO [train2022-06-18 07:45:49,107 INFO [train.py:445] Epoch 9, batch 6600, loss[loss=2.236, over 23200.00 frames., ppl: 9.35779725943154] tot_loss[loss=2.282, over 30819385.80 frames., ppl: 9.2022-06-18 07:46:58,170 INFO [train.py:445] Epoch 9, batc2022-06-18 07:46:58,184 INFO [train.py:445] Epoch 9, batch 6800, loss[loss=2.347, over 18800.00 frames., ppl: 10.449322574792445] tot_loss[loss=2.283, over 30652022-06-18 07:48:09,365 INFO2022-06-12022-06-18 07:48:09,901 INFO [train.py:445] Epoch 9, batch 7000, loss[loss=2.161, over 64400.00 frames., ppl: 8.679051661195109] tot_loss[loss=2.279, over 31913726.93 frames., ppl:2022-06-18 07:49:21,911 INFO [trai2022-06-18 07:49:22,087 INFO [train.py:445] Epoch 9, batch 7200, loss[loss=2.207, over 42800.00 frames., ppl: 9.092514073369328] tot_loss[loss=2.281, over 31550493.64 frames., ppl: 9.2022-06-18 07:50:34,004 INF2022-06-18 07:50:34,045 INFO [train.py:445] Epoch 9, batch 7400, loss[loss=2.23, over 30800.00 frames., ppl: 9.300434600240285] tot_loss[loss=2.284, over 30977190.40 frames., ppl: 9.813002392022-06-18 07:51:46,018 I2022-06-182022-06-18 07:51:46,131 INFO [train.py:445] Epoch 9, batch 7600, loss[loss=2.263, over 25200.00 frames., ppl: 9.607659153611106] tot_loss[loss=2.281, over 31624663.79 frames., ppl: 92022-06-18 07:52:57,172 INFO [train2022-06-18 07:52:5722022-06-18 07:52:57,216 INFO [train.py:445] Epoch 9, batch 7800, loss[loss=2.372, over 10400.00 frames., ppl: 10.718004114161104] tot_loss[loss=2.284, over 3102452022-06-18 07:54:07,616 INFO [train.p2022-06-18 07:54:07,679 INFO [train.py:445] Epoch 9, batch 8000, loss[loss=2.208, over 30000.00 frames., ppl: 9.09981575547434] tot_loss[loss=2.284, over 31025998.51 frames., ppl:2022-06-18 07:55:19,872022-06-18 07:55:20,199 INFO [train.py:445] Epoch 9, batch 8200, loss[loss=2.19, over 42000.00 frames., ppl: 8.934247512335078] tot_loss[loss=2.283, over 31484205.66 frames., ppl: 9.81050736830822022-06-18 07:56:34,42022-06-18 07:56:34,640 INFO [train.py:445] Epoch 9, batch 8400, loss[loss=2.211, over 41200.00 frames., ppl: 9.123150487812248] tot_loss[loss=2.284, over 31364430.79 frames., ppl: 9.81917453773752022-06-18 07:57:49,12022-06-18 07:57:49,330 INFO [train.py:445] Epoch 9, batch 8600, loss[loss=2.21, over 28800.00 frames., ppl: 9.114805213134941] tot_loss[loss=2.284, over 31326497.94 frames., ppl: 9.8203747355782022-06-18 07:59:03,062022-06-18 07:2022-06-18 07:59:03,243 INFO [train.py:445] Epoch 9, batch 8800, loss[loss=2.272, over 24800.00 frames., ppl: 9.694238941733623] tot_loss[loss=2.283, over 31994434.19 frames., ppl:2022-06-18 08:00:14,3982022-06-18 08:00:2022-06-18 08:002022-06-18 08:00:14,490 INFO [train.py:445] Epoch 9, batch 9000, loss[loss=2.298, over 14400.00 frames., ppl: 9.957891405235866] tot_loss[loss=2.287, over 309082022-06-18 08:01:28,728 2022-06-18 082022-06-18 08:01:22022-06-18 08:01:29,122 INFO [train.py:445] Epoch 9, batch 9200, loss[loss=2.186, over 48000.00 frames., ppl: 8.90366121931489] tot_loss[loss=2.287, over 3105912022-06-18 08:02:38,510 INFO [train.py:2022-06-18 08:02:38,673 INFO [train.py:445] Epoch 9, batch 9400, loss[loss=2.222, over 25600.00 frames., ppl: 9.227990986222117] tot_loss[loss=2.284, over 31677258.31 frames., p2022-06-18 08:03:54,822 INF2022-06-18 08:032022-06-18 08:03:55,133 INFO [train.py:445] Epoch 9, batch 9600, loss[loss=2.192, over 43600.00 frames., ppl: 8.949720905436767] tot_loss[loss=2.286, over 31210944.60 frames.2022-06-18 08:05:10,173 INFO [train.py:442022-06-18 08:05:10,336 INFO [train.py:445] Epoch 9, batch 9800, loss[loss=2.223, over 26400.00 frames., ppl: 9.23960423503669] tot_loss[loss=2.285, over 31599360.74 frames., p2022-06-18 08:06:22,558 IN2022-06-18 08:06:22,796 INFO [train.py:445] Epoch 9, batch 10000, loss[loss=2.173, over 32400.00 frames., ppl: 8.782781964218133] tot_loss[loss=2.285, over 31614722.92 frames., ppl: 9.828485997909102], batch size: 400
+2022-06-18 08:06:22,796 INFO [train.py:4692022-06-18 08:06:22,980 INFO [train.py:482022-06-18 08:06:22,980 INFO [train.py:480] Epoch 9, validation: loss=2.34, over 212022-06-18 08:07:34,405 INFO [train.py:442022-06-18 08:07:34,496 INFO [train.py:445] Epoch 9, batch 10200, loss[loss=2.265, over 20400.00 frames., ppl: 9.629468656266676] tot_loss[loss=2.283, over 31962931.43 frames., 2022-06-18 08:08:45,140 INFO2022-06-18 08:08:45,155 INFO [train.py:445] Epoch 9, batch 10400, loss[loss=2.195, over 31200.00 frames., ppl: 8.982947203405145] tot_loss[loss=2.286, over 31413041.07 frames., ppl: 9.836022022-06-18 08:10:01,211 INFO 2022-06-18 082022-06-18 08:10:01,419 INFO [train.py:445] Epoch 9, batch 10600, loss[loss=2.181, over 38000.00 frames., ppl: 8.853107287152039] tot_loss[loss=2.285, over 31586782.73 frames.2022-06-18 08:11:12,911 INFO [train.py:445]20222022-06-18 08:11:13,182 INFO [train.py:445] Epoch 9, batch 10800, loss[loss=2.212, over 44000.00 frames., ppl: 9.135769686793022] tot_loss[loss=2.284, over 31701445.86 fra2022-06-18 08:12:26,112 INFO [train.py:442022-06-18 08:12:26,273 INFO [train.py:445] Epoch 9, batch 11000, loss[loss=2.213, over 29200.00 frames., ppl: 9.141941975218565] tot_loss[loss=2.284, over 31937599.45 frames., p2022-06-18 08:13:38,480 INFO [train.py:445] Ep2022-06-18 08:13:38,617 INFO [train.py:445] Epoch 9, batch 11200, loss[loss=2.229, over 24400.00 frames., ppl: 9.293234191412571] tot_loss[loss=2.285, over 31508811.27 fram2022-06-18 08:14:50,110 INFO [train.py:445] Ep2022-06-18 08:14:50,242 INFO [train.py:445] Epoch 9, batch 11400, loss[loss=2.166, over 50800.00 frames., ppl: 8.720032811828595] tot_loss[loss=2.285, over 31699603.01 fra2022-06-18 08:16:05,022 INFO2022-06-18 08:16:05,177 INFO [train.py:445] Epoch 9, batch 11600, loss[loss=2.19, over 38800.00 frames., ppl: 8.933554468228053] tot_loss[loss=2.287, over 31451432.56 frames., ppl: 9.84166462022-06-18 08:17:15,403 INF2022-06-182022-06-18 08:17:15,569 INFO [train.py:445] Epoch 9, batch 11800, loss[loss=2.265, over 23200.00 frames., ppl: 9.629222628855828] tot_loss[loss=2.284, over 32258780.96 frames., ppl:2022-06-18 08:18:30,377 INF2022-06-18 08:18:30,417 INF2022-06-18 08:18:30,502 INFO [train.py:445] Epoch 9, batch 12000, loss[loss=2.222, over 28800.00 frames., ppl: 9.222531599421508] tot_loss[loss=2.286, over 31772852022-06-18 08:19:42,208 INFO [train.p2022-06-18 08:19:42,380 INFO [train.py:445] Epoch 9, batch 12200, loss[loss=2.213, over 39200.00 frames., ppl: 9.140280984060961] tot_loss[loss=2.284, over 32524273.66 frames., pp2022-06-18 08:20:55,850 INFO 2022-06-18202022-06-18 08:20:56,080 INFO [train.py:445] Epoch 9, batch 12400, loss[loss=2.202, over 34800.00 frames., ppl: 9.04269018485382] tot_loss[loss=2.283, over 32488990.41 frames., pp2022-06-18 08:22:05,301 INFO 2022-06-18 08:22:052022-06-18 08:22:05,462 INFO [train.py:445] Epoch 9, batch 12600, loss[loss=2.257, over 27600.00 frames., ppl: 9.55680758253192] tot_loss[loss=2.286, over 31766213.86 fr2022-06-18 08:22:34,449 INFO 2022-06-18 02022-02022-06-18 08:22:34,62022-06-18 08:22:34,886 INFO [train.py:445] Epoch 10, batch 0, loss[loss=2.171, over 52800.00 frames., ppl: 8.764280976598835] tot_loss[loss=2.12022-06-18 08:23:51,788 INFO [train.py:445] Epoch 10, batch 200, loss[loss=2.245, over 28000.00 frames., ppl: 9.444774589180927] tot_loss[loss=2.267, over 2952715.29 frames., ppl: 9.647917641313981], batch size: 400
+2022-06-18 08:25:04,353 INFO [train.py:4452022-06-18 08:25:04,457 INFO [train.py:445] Epoch 10, batch 400, loss[loss=2.18, over 48000.00 frames., ppl: 8.84710827954535] tot_loss[loss=2.271, over 5842566.94 frames., 2022-06-18 08:26:16,527 INFO [train.py:445] Epoch 10, batch 600, loss[loss=2.174, over 33200.00 frames., ppl: 8.797641868756648] tot_loss[loss=2.263, over 8552771.71 frames., ppl: 9.61212514987543], batch size: 400
+2022-06-18 08:27:26,399 INFO2022-06-18 08:27:26,523 INFO [train.py:445] Epoch 10, batch 800, loss[loss=2.215, over 27200.00 frames., ppl: 9.164077718501378] tot_loss[loss=2.278, over 9857726.44 frames., ppl: 9.7540042022-06-18 08:28:38,022 INFO [train.py:445] 2022-06-18 08:28:38,128 INFO [train.py:445] Epoch 10, batch 1000, loss[loss=2.249, over 23200.00 frames., ppl: 9.48282326913532] tot_loss[loss=2.267, over 12735745.52 frames.2022-06-18 08:29:51,593 INF2022-06-18 08:29:51,654 INFO [train.py:442022-06-18 08:29:51,862 INFO [train.py:445] Epoch 10, batch 1200, loss[loss=2.17, over 36501.00 frames., ppl: 8.755435590279744] tot_loss[loss=2.273, 2022-06-18 08:31:05,191 INFO [trai2022-06-12022-06-18 08:2022-06-18 08:31:05,406 INFO [train.py:445] Epoch 10, batch 1400, loss[loss=2.208, over 33600.00 frames., ppl: 9.09411847541856] tot_loss[loss=2.276, over 154602022-06-18 08:32:17,217 INF2022-06-18 08:32:2022-06-18 082022-06-18 08:32:17,360 INFO [train.py:445] Epoch 10, batch 1600, loss[loss=2.227, over 24800.00 frames., ppl: 9.2682402566973] tot_loss[loss=2.277, over 16859012022-06-18 08:33:30,711 INF2022-06-18 08:33:30,807 INFO [train.py:445] Epoch 10, batch 1800, loss[loss=2.219, over 32800.00 frames., ppl: 9.198032615924207] tot_loss[loss=2.272, over 19068718.53 frames., ppl: 9.69592742022-06-18 08:34:45,771 INFO [train.2022-02022-06-18 08:34:45,956 INFO [train.py:445] Epoch 10, batch 2000, loss[loss=2.245, over 30000.00 frames., ppl: 9.442308746260403] tot_loss[loss=2.27, over 20249524.29 frames., p2022-06-18 08:35:55,822 I2022-06-18 08:32022-06-18 08:35:56,021 INFO [train.py:445] Epoch 10, batch 2200, loss[loss=2.189, over 30800.00 frames., ppl: 8.929123000350003] tot_loss[loss=2.269, over 21527464.54 frames., p2022-06-18 08:37:05,824 2022-06-18 2022-2022-06-18 08:37:06,057 I2022-06-18 08:37:06,083 INFO [train.py:445] Epoch 10, batch 2400, loss[loss=2.182, over 33200.00 frames., ppl: 8.861434255778864] tot_loss[loss=2.276, ov2022-06-18 08:38:18,512 INFO [trai2022-02022-06-18 08:38:18,593 INFO [train.py:445] Epoch 10, batch 2600, loss[loss=2.18, over 34000.00 frames., ppl: 8.84725761302741] tot_loss[loss=2.273, over 23081677.75 frames., ppl2022-06-18 08:39:30,997 INFO [train.p2022-062022-06-18 08:39:31,492022-06-18 08:39:31,603 INFO [train.py:445] Epoch 10, batch 2800, loss[loss=2.22, over 74800.00 frames., ppl: 9.211717086275916] tot_loss[loss=2.276, ov2022-06-18 08:40:43,054 INFO [train.py2022-06-18 08:40:43,357 INFO [train.py:445] Epoch 10, batch 3000, loss[loss=2.157, over 45600.00 frames., ppl: 8.645329709227497] tot_loss[loss=2.276, over 24551028.14 frames., ppl:2022-06-18 08:41:56,902022-06-18 08:41:56,911 INFO [train.py:445] Epoch 10, batch 3200, loss[loss=2.186, over 19200.00 frames., ppl: 8.89543413560374] tot_loss[loss=2.277, over 25301035.20 frames., ppl: 9.75003314506182022-06-18 08:43:12,22022-06-18 08:43:2022-06-18 08:43:12,370 INFO [train.py:445] Epoch 10, batch 3400, loss[loss=2.21, over 29200.00 frames., ppl: 9.111352986037046] tot_loss[loss=2.277, over 25715493.96 frames., ppl:2022-06-18 08:44:25,612 INFO [2022-06-18 2022-062022-06-18 08:44:25,759 INFO [train.py:445] Epoch 10, batch 3600, loss[loss=2.181, over 27600.00 frames., ppl: 8.855049709709569] tot_loss[loss=2.276, over 26423514.73 fr2022-06-18 08:45:40,2022-06-12022-06-18 08:45:40,909 INFO [train.py:445] Epoch 10, batch 3800, loss[loss=2.195, over 56000.00 frames., ppl: 8.98136670210533] tot_loss[loss=2.277, over 26863614.56 frames., ppl: 9.7432792022-06-18 08:46:56,2022-06-18 08:46:56,542 INFO [train.py:445] Epoch 10, batch 4000, loss[loss=2.176, over 27600.00 frames., ppl: 8.810333803157208] tot_loss[loss=2.277, over 27400895.97 frames., ppl: 9.748682549544632022-06-18 08:48:06,004 INFO 2022-06-182022-06-18 08:48:06,290 INFO [train.py:445] Epoch 10, batch 4200, loss[loss=2.201, over 40000.00 frames., ppl: 9.037244327229768] tot_loss[loss=2.272, over 28710400.30 frames., pp2022-06-18 08:49:18,200 INFO [t2022-06-18 08:49:18,257 INFO [train.py:445] Epoch 10, batch 4400, loss[loss=2.237, over 20000.00 frames., ppl: 9.367546089986604] tot_loss[loss=2.28, over 27541010.60 frames., ppl: 9.77692022-06-18 08:50:28,743 INFO [2022-06-18 08:50:29,140 INFO [train.py:445] Epoch 10, batch 4600, loss[loss=2.225, over 69200.00 frames., ppl: 9.251504364738054] tot_loss[loss=2.28, over 27875819.41 frames., ppl: 9.77842022-06-18 08:51:40,602 INFO 22022-06-18 08:51:40,767 INFO [train.py:445] Epoch 10, batch 4800, loss[loss=2.231, over 22800.00 frames., ppl: 9.30857603359205] tot_loss[loss=2.28, over 28415067.61 frames., ppl: 9.7730942022-06-18 08:52:522022-06-18 08:52:52,850 INFO [train.py:445] Epoch 10, batch 5000, loss[loss=2.199, over 32400.00 frames., ppl: 9.018550412762332] tot_loss[loss=2.278, over 29126594.24 frames., ppl: 9.760605455255102022-06-18 08:54:05,2022-06-18 08:54:05,853 INFO [train.py:445] Epoch 10, batch 5200, loss[loss=2.183, over 44800.00 frames., ppl: 8.873201920123176] tot_loss[loss=2.278, over 29420598.19 frames., ppl: 9.75821704087762022-06-18 08:55:19,12022-06-18 08:55:19,200 INFO [train.py:445] Epoch 10, batch 5400, loss[loss=2.265, over 20000.00 frames., ppl: 9.634127271202008] tot_loss[loss=2.277, over 29700967.44 frames., ppl: 9.74985007058632022-06-18 08:56:35,72022-06-182022-06-18 2022-02022-06-18 08:56:35,926 INFO [train.py:445] Epoch 10, batch 5600, loss[loss=2.224, over 20400.00 frames., ppl: 9.242677086725873] tot_loss[loss=2.278, over 29858636.28 f2022-06-18 08:57:48,022022-06-18 08:57:48,157 INFO [train.py:445] Epoch 10, batch 5800, loss[loss=2.266, over 20000.00 frames., ppl: 9.645405043212868] tot_loss[loss=2.278, over 30065295.83 frames., ppl: 9.760841310112022-06-18 08:59:01,2552022-06-18 2022-02022-06-18 08:59:01,547 INFO [train.py:445] Epoch 10, batch 6000, loss[loss=2.173, over 40602.00 frames., ppl: 8.787393387059915] tot_loss[loss=2.275, over 30908508.82 frames., p2022-06-18 09:00:12,122022-062022-2022-02022-06-18 09:00:12,442 INFO [train.py:445] Epoch 10, batch 6200, loss[loss=2.193, over 44000.00 frames., ppl: 8.964108798907743] tot_loss[loss=2.275, over 31196035.13 frames., pp2022-06-18 09:01:24,232022-06-18 09:01:24,303 INFO [train.py:445] Epoch 10, batch 6400, loss[loss=2.304, over 20000.00 frames., ppl: 10.017523781291109] tot_loss[loss=2.278, over 31011735.94 frames., ppl: 9.754417842022-06-18 09:02:36,459 INFO [train.2022-06-18 09:02:36,5952022-06-18 09:02:36,798 INFO [train.py:445] Epoch 10, batch 6600, loss[loss=2.237, over 50400.00 frames., ppl: 9.363050479780783] tot_loss[loss=2.28, over 305312022-06-18 09:03:48,562 2022-06-18 09:03:48,740 INFO [train.py:445] Epoch 10, batch 6800, loss[loss=2.252, over 24000.00 frames., ppl: 9.502700383547351] tot_loss[loss=2.28, over 30818641.28 frames., ppl: 9.780912349472022-06-18 09:05:01,3012022-06-18 09:05:01,406 I2022-06-18 09:05:01,555 INFO [train.py:445] Epoch 10, batch 7000, loss[loss=2.183, over 52400.00 frames., ppl: 8.874568430093044] tot_loss[loss=2.278, over 30863433.34 fr2022-06-18 09:06:17,1682022-06-12022-06-18 09:06:17,373 2022-06-18 09:06:17,450 INFO [train.py:445] Epoch 10, batch 7200, loss[loss=2.177, over 39600.00 frames., ppl: 8.816464122197367] tot_loss[loss=2.281, over 306262022-06-18 09:07:30,494 INFO [train.py:445]2022-06-18 09:2022-06-18 09:07:30,670 INFO [train.py:445] Epoch 10, batch 7400, loss[loss=2.209, over 35200.00 frames., ppl: 9.107815188602713] tot_loss[loss=2.28, over 3091672022-06-18 09:08:41,991 INFO [train.py:445] Epo2022-06-18 09:08:42,184 INFO [train.py:445] Epoch 10, batch 7600, loss[loss=2.209, over 35600.00 frames., ppl: 9.10537586031806] tot_loss[loss=2.28, over 30840958.74 frames2022-06-18 09:09:54,065 INFO [train.py:445] Epoch 10, batch 7800, loss[loss=2.256, over 26400.00 frames., ppl: 9.541631679211525] tot_loss[loss=2.279, over 31396399.46 frames., ppl: 9.770880667329402], batch size: 400
+2022-06-18 09:11:04,789 INFO [train.py:445] Epoch 10, batch 8000, loss[loss=2.189, over 29600.00 frames., ppl: 8.924800117243358] tot_loss[loss=2.28, over 31390476.90 frames., ppl: 9.773913590122385], batch size: 400
+2022-06-18 09:12:182022-06-18 09:12:18,374 INFO [train.py:445] Epoch 10, batch 8200, loss[loss=2.229, over 21600.00 frames., ppl: 9.293971440435469] tot_loss[loss=2.281, over 31264687.77 frames., ppl: 9.782130874725103]2022-06-18 09:13:22022-06-18 09:13:28,772 INFO [train.py:445] Epoch 10, batch 8400, loss[loss=2.235, over 25200.00 frames., ppl: 9.346725253330805] tot_loss[loss=2.28, over 31325866.21 frames., ppl: 9.7812635081061632022-06-18 09:14:2022-06-18 09:14:42,681 INF2022-06-18 09:14:43,086 INFO [train.py:445] Epoch 10, batch 8600, loss[loss=2.182, over 68800.00 frames., ppl: 8.866655317749828] tot_loss[loss=2.28, over 31481445.34 frames2022-06-18 09:152022-2022-06-18 09:15:52022-06-18 09:15:57,987 INFO [train.py:445] Epoch 10, batch 8800, loss[loss=2.216, over 26800.00 frames., ppl: 9.174853688551172] tot_loss[loss=2.28, over 31489046.38 frames., ppl:2022-06-18 09:2022-06-18 09:17:14,4262022-06-18 09:17:14,782 INFO [train.py:445] Epoch 10, batch 9000, loss[loss=2.22, over 66000.00 frames., ppl: 9.208091635585063] tot_loss[loss=2.278, over 31753750.75 frames., ppl: 2022-06-18 092022-02022-06-18 09:18:26,880 INFO [train.py:445] Epoch 10, batch 9200, loss[loss=2.215, over 71600.00 frames., ppl: 9.159374090962038] tot_loss[loss=2.28, over 31688717.22 frames., ppl: 9.775384208601878]2022-06-18 092022-06-18 09:19:40,122 INFO [train.2022-06-18 09:19:40,230 INFO [train.py:445] Epoch 10, batch 9400, loss[loss=2.2, over 28000.00 frames., ppl: 9.024489741527722] tot_loss[loss=2.281, over 31646294.67 fr2022-06-18 09:22022-06-18 09:20:54,758 INF2022-06-18 09:20:54,906 INFO [train.py:445] Epoch 10, batch 9600, loss[loss=2.205, over 42800.00 frames., ppl: 9.071165526514745] tot_loss[loss=2.28, over 31720771.55 frames.2022-06-18 09:22:052022-06-18 09:22:05,801 INFO [train.py:445] Epoch 10, batch 9800, loss[loss=2.201, over 41600.00 frames., ppl: 9.037549217170426] tot_loss[loss=2.281, over 31507307.53 frames., ppl: 9.78332004277432022-06-18 09:2022-06-18 09:23:18,312022022-06-18 09:23:18,447 INFO [train.py:445] Epoch 10, batch 10000, loss[loss=2.266, over 20400.00 frames., ppl: 9.639419672504944] tot_loss[loss=2.278, over 32287996.08 frames., ppl: 9.754144619189521], batch size: 400
+2022-06-18 09:23:18,447 INFO [2022-06-18 09:23:18,632022-06-18 09:23:18,62022-062022-06-18 09:23:18,632 INFO [train.py:480] Epoch 10, validation: loss=2.3392022-06-18 09:24:30,782022-06-18 09:242022-2022-06-18 09:24:30,964 INFO [train.py:445] Epoch 10, batch 10200, loss[loss=2.197, over 35200.00 frames., ppl: 8.999696271812253] tot_loss[loss=2.281, over 31791787.50 frames.2022-06-18 09:25:43,661 INFO [train.py:445] Epoch 10, batch 10400, loss[loss=2.194, over 30800.00 frames., ppl: 8.966542517317947] tot_loss[loss=2.281, over 31854125.46 frames., ppl: 9.784577649109094], batch size: 400
+2022-06-18 09:26:55,742022-06-18 092022-062022-06-18 09:26:56,024 INFO [train.py:445] Epoch 10, batch 10600, loss[loss=2.195, over 38000.00 frames., ppl: 8.982105991229428] tot_loss[loss=2.281, over 31860657.89 frames.2022-06-18 092022-06-18 09:28:09,136 INFO [train.py:445] Epoch 10, batch 10800, loss[loss=2.201, over 47200.00 frames., ppl: 9.030367414615442] tot_loss[loss=2.28, over 31808274.48 frames., ppl: 9.781016610208757], ba2022-06-18 09:2022-06-18 09:29:24,444 INFO [train.py:445] Epoch 10, batch 11000, loss[loss=2.258, over 24000.00 frames., ppl: 9.563286822551749] tot_loss[loss=2.281, over 31805202.30 frames., ppl: 9.78768557354984], batc2022-06-18 09:30:36,881 INFO [train.py:445]2022-06-18 09:30:37,081 INFO [train.py:445] Epoch 10, batch 11200, loss[loss=2.19, over 40000.00 frames., ppl: 8.935429517011857] tot_loss[loss=2.281, over 31960816.37 frames.,2022-06-18 02022-062022-06-18 09:2022-06-12022-06-12022-06-18 09:31:50,866 INFO [train.py:445] Epoch 10, batch 11400, loss[loss=2.193, over 54400.00 frames., ppl: 8.960776410129196] tot_loss[loss=2.279, over 32340032.542022-06-18 092022-02022-06-18 09:22022-06-18 09:33:05,181 INFO [train.py:445] Epoch 10, batch 11600, loss[loss=2.23, over 54519.00 frames., ppl: 9.295744600811004] tot_loss[loss=2.278, over 32424077.17 frames., ppl: 9.2022-06-18 09:34:18,2022-06-18 09:2022-06-12022-06-2022-06-18 09:34:18,473 INFO [train.py:445] Epoch 10, batch 11800, loss[loss=2.22, over 30800.00 frames., ppl: 9.205439334859252] tot_loss[loss=2.28, over 32461456.92022-06-18 092022-06-18 09:35:32,305 INFO [tra2022-06-18 09:35:32,402 INFO [train.py:445] Epoch 10, batch 12000, loss[loss=2.19, over 33600.00 frames., ppl: 8.935740833065262] tot_loss[loss=2.279, over 32262735.63 frames2022-06-18 2022-06-18 09:36:41,607 2022-06-18 09:36:41,749 INFO [train.py:445] Epoch 10, batch 12200, loss[loss=2.222, over 34800.00 frames., ppl: 9.228807401037072] tot_loss[loss=2.28, over 31996819.80 frames., ppl: 2022-06-18 092022-06-18 09:37:54,7962022-06-18 09:37:54,961 INFO [train.py:445] Epoch 10, batch 12400, loss[loss=2.208, over 33600.00 frames., ppl: 9.100343631190777] tot_loss[loss=2.281, over 31710965.07 frames., pp2022-06-18 09:392022-062022-06-18 09:2022-06-18 09:39:07,711 INFO [train.py:445] Epoch 10, batch 12600, loss[loss=2.235, over 19600.00 frames., ppl: 9.343444217754701] tot_loss[loss=2.28, over 32015765.11 frames., ppl:2022-06-18 09:39:2022-062022-06-18 02022-06-18 09:39:37,888 INFO [train.py:445] Epoch 11, batch 0, loss[loss=2.198, over 28800.00 frames., ppl: 9.00515239739639] tot_loss[loss=2.198, over 28800.00 frames., ppl:2022-06-18 09:40:562022-062022-06-18 09:40:56,903 INFO [train.py:445] Epoch 11, batch 200, loss[loss=2.227, over 24400.00 frames., ppl: 9.269633887650047] tot_loss[loss=2.277, over 2783400.56 frames., ppl: 9.748172022-06-18 09:42:07,32022-06-18 09:42:07,902 INFO [train.py:445] Epoch 11, batch 400, loss[loss=2.221, over 73794.00 frames., ppl: 9.213400778065155] tot_loss[loss=2.265, over 5799329.66 frames., ppl: 9.63167217445632022-06-18 09:43:19,431 INFO 2022-06-18 09:43:19,461 INFO [train.py:445] Epoch 11, batch 600, loss[loss=2.166, over 33200.00 frames., ppl: 8.722898844533548] tot_loss[loss=2.265, over 8133259.44 frames., ppl: 9.631002022-06-18 09:44:32,281 INFO 2022-06-18 09:44:32,452 INF2022-06-18 09:44:32,713 INFO [train.py:445] Epoch 11, batch 800, loss[loss=2.199, over 54800.00 frames., ppl: 9.017242962623875] tot_loss[loss=2.265, over 1044962022-06-18 09:45:42022-06-18 09:45:48,2022-06-18 09:45:48,695 INFO [train.py:445] Epoch 11, batch 1000, loss[loss=2.212, over 25200.00 frames., ppl: 9.131439966498947] tot_loss[loss=2.262, over 12765301.37 frames., pp2022-06-18 09:46:59,441 INFO [train.py:445] Epoch 11, batch 1200, loss[loss=2.181, over 40401.00 frames., ppl: 8.854416652412475] tot_loss[loss=2.262, over 14792194.67 frames., ppl: 9.606846463199865], batch size: 201
+2022-06-18 09:48:11,029 INFO [train.py:445] Epoch 11, batch 1400, loss[loss=2.188, over 35200.00 frames., ppl: 8.91880189702603] tot_loss[loss=2.266, over 16105100.55 frames., ppl: 9.639652234189477], batch size: 400
+2022-06-18 09:492022-06-18 09:49:24,976 2022-06-18 09:49:25,124 INFO [train.py:445] Epoch 11, batch 1600, loss[loss=2.191, over 44800.00 frames., ppl: 8.943537658001183] tot_loss[loss=2.268, over 17593481.80 frames., 2022-06-18 09:50:36,255 INFO2022-06-18 22022-06-18 09:2022-06-18 09:50:37,117 INFO [train.py:445] Epoch 11, batch 1800, loss[loss=2.223, over 64230.00 frames., ppl: 9.236498395002837] tot_loss[loss=2.271, over 184095722022-06-18 09:52022-06-18 09:2022-06-18 09:51:44,833 2022-06-18 09:51:44,972 INFO [train.py:445] Epoch 11, batch 2000, loss[loss=2.19, over 40800.00 frames., ppl: 8.93511559165032] tot_loss[loss=2.274, over 19260022.62022-06-18 09:52022-06-18 09:52:55,8582022-06-18 09:52:56,180 INFO [train.py:445] Epoch 11, batch 2200, loss[loss=2.188, over 44400.00 frames., ppl: 8.915801651478763] tot_loss[loss=2.267, over 21497186.59 frames., ppl2022-06-18 09:2022-06-18 09:2022-06-182022-06-18 09:54:09,801 INFO [train.py:445] Epoch 11, batch 2400, loss[loss=2.131, over 46000.00 frames., ppl: 8.42746423197055] tot_loss[loss=2.267, over 22520253.79 frames., pp2022-06-18 09:55:2022-06-18 092022-06-12022-06-18 09:55:25,927 INFO [train.py:445] Epoch 11, batch 2600, loss[loss=2.2, over 57200.00 frames., ppl: 9.026957607165015] tot_loss[loss=2.267, over 23672816.84 frames., ppl:2022-06-18 09:562022-06-18 02022-06-18 02022-06-18 02022-06-18 09:56:39,664 INFO [train.py:445] Epoch 11, batch 2800, loss[loss=2.176, over 28800.00 frames., ppl: 8.807936691716153] tot_loss[loss=2.268, over 24340424.2022-06-18 09:57:56,368 INFO 2022-06-182022-06-18 092022-06-18 09:57:56,502 INFO [train.py:445] Epoch 11, batch 3000, loss[loss=2.239, over 22800.00 frames., ppl: 9.385585563800461] tot_loss[loss=2.276, over 23725579.42022-06-18 09:52022-06-18 09:59:10,560 2022-06-18 092022-06-18 09:59:10,646 INFO [train.py:445] Epoch 11, batch 3200, loss[loss=2.223, over 17600.00 frames., ppl: 9.23637837399604] tot_loss[loss=2.275, over 24564923.662022-06-18 10:00:26,704 INFO [train.py:445] Epoch 11, batch 3400, loss[loss=2.24, over 43818.00 frames., ppl: 9.395671310026705] tot_loss[loss=2.269, over 26655379.28 frames., ppl: 9.666300855183401], batch size: 201
+2022-06-18 10:01:39,432 INFO [trai2022-06-18 10:01:39,707 INFO [train.py:445] Epoch 11, batch 3600, loss[loss=2.176, over 46800.00 frames., ppl: 8.812712186299667] tot_loss[loss=2.268, over 27163141.12 frames., ppl: 9.2022-06-18 10:02:49,324 INFO [train.py:445] Epoch 112022-06-18 10:02:49,827 INFO [train.py:445] Epoch 11, batch 3800, loss[loss=2.192, over 73600.00 frames., ppl: 8.949071306162535] tot_loss[loss=2.27, over 27399237.2022-06-18 10:02022-06-182022-06-18 2022-02022-06-18 2022-06-18 10:04:03,415 INFO [train.py:445] Epoch 11, batch 4000, loss[loss=2.167, over 18800.00 frames., ppl: 8.734992640571768] tot_loss[loss=2.269, over 280596412022-06-18 10:05:17,796 IN2022-06-18 10:05:17,930 INFO [train.py:445] Epoch 11, batch 4200, loss[loss=2.228, over 23200.00 frames., ppl: 9.279760473804807] tot_loss[loss=2.272, over 27932473.67 frames., ppl: 9.702225092022-06-18 10:06:32,041 INFO [train.py:445] Epoch 11, batch 4400, loss[loss=2.265, over 25600.00 frames., ppl: 9.628043357605582] tot_loss[loss=2.273, over 28281911.69 frames., ppl: 9.712237505904394], batch size: 400
+2022-06-18 102022-06-18 10:07:42,861 INFO [train.py:445] Epoch 11, batch 4600, loss[loss=2.246, over 20400.00 frames., ppl: 9.448978791382029] tot_loss[loss=2.272, over 28748525.16 frames., ppl: 9.697573518571902], bat2022-06-18 10:08:59,183 INFO [train.py:445] Epoch 11, batch 4800, loss[loss=2.205, over 38000.00 frames., ppl: 9.071053455310652] tot_loss[loss=2.272, over 29353321.05 frames., ppl: 9.698469868871848], batch size: 400
+2022-06-18 102022-06-18 12022-06-18 10:2022-06-18 10:10:12,447 INFO [train.py:445] Epoch 11, batch 5000, loss[loss=2.212, over 54400.00 frames., ppl: 9.134032444126916] tot_loss[loss=2.273, over 29521965.85 frames., p2022-06-18 10:2022-06-182022-06-18 10:11:22,080 INFO [train.py:445] Epoch 11, batch 5200, loss[loss=2.185, over 32400.00 frames., ppl: 8.89471621786582] tot_loss[loss=2.273, over 29423022.32 frames., ppl: 9.7047550489132022-06-18 12022-06-182022-06-18 10:12:39,303 INFO [train.py:445] Epoch 11, batch 5400, loss[loss=2.222, over 21200.00 frames., ppl: 9.229796347515645] tot_loss[loss=2.274, over 29309922.06 frames., ppl: 9.7225724556962022-06-18 10:13:50,697 INFO [train.py:445] Epoch 11, batch 5600, loss[loss=2.197, over 69600.00 frames., ppl: 8.995287150415662] tot_loss[loss=2.274, over 30295863.04 frames., ppl: 9.715915509195773], batch size: 400
+2022-06-18 2022-06-18 10:15:06,642 INFO [train.py2022-06-18 10:15:06,868 INFO [train.py:445] Epoch 11, batch 5800, loss[loss=2.159, over 52000.00 frames., ppl: 8.6654933558672] tot_loss[loss=2.275, over 30002774.53 f2022-06-18 102022-06-18 10:16:19,132 INFO [train.py:445] Epoch 11, batch 6000, loss[loss=2.21, over 32400.00 frames., ppl: 9.112160656074234] tot_loss[loss=2.274, over 30235693.82 frames., ppl: 9.714473480596862], ba2022-06-18 10:2022-06-18 10:17:34,431 INFO [train.py:445] Epoch 11, batch 6200, loss[loss=2.303, over 14400.00 frames., ppl: 10.00240728077003] tot_loss[loss=2.275, over 30143177.19 frames., ppl: 9.730790916210772], ba2022-06-18 10:2022-06-2022-06-18 10:18:2022-06-18 10:18:47,638 INFO [train.py:445] Epoch 11, batch 6400, loss[loss=2.21, over 22800.00 frames., ppl: 9.118081213611655] tot_loss[loss=2.273, over 31111164.92 frames., p2022-06-18 10:20:01,955 INFO [train.py:445] Epoch 11, batch 6600, loss[loss=2.173, over 42400.00 frames., ppl: 8.78715841017266] tot_loss[loss=2.275, over 31212013.10 frames., ppl: 9.725676217791587], batch size: 400
+2022-06-18 10:21:16,302 2022-06-18 10:2122022-06-18 10:21:16,453 INFO [train.py:445] Epoch 11, batch 6800, loss[loss=2.219, over 26800.00 frames., ppl: 9.198005868066675] tot_loss[loss=2.273, over 31374084.13 frames.,2022-06-18 10:22:31,665 INFO [train.py:4452022-06-18 12022-06-18 10:22:32,075 INFO [train.py:445] Epoch 11, batch 7000, loss[loss=2.176, over 54000.00 frames., ppl: 8.810535365346352] tot_loss[loss=2.275, over 31083602022-06-18 10:23:43,801 INFO [train.py:445] Epoch 11, batch 7200, loss[loss=2.188, over 26400.00 frames., ppl: 8.920192269030935] tot_loss[loss=2.275, over 31489368.71 frames., ppl: 9.7277712900081], batch size: 400
+2022-06-18 10:24:55,92022-06-2022-06-18 10:24:56,190 INFO [train.py:445] Epoch 11, batch 7400, loss[loss=2.222, over 33600.00 frames., ppl: 9.22803892810716] tot_loss[loss=2.276, over 31049577.65 frames., ppl: 9.73672022-06-18 10:26:10,344 INFO [train.py:445] Epo2022-06-12022-06-18 10:26:10,509 INFO [train.py:445] Epoch 11, batch 7600, loss[loss=2.15, over 32400.00 frames., ppl: 8.588191804112311] tot_loss[loss=2.276, over 312950762022-06-18 10:27:22,589 INFO [train.py:445] E2022-06-18 10:27:22,794 INFO [train.py:445] Epoch 11, batch 7800, loss[loss=2.205, over 42400.00 frames., ppl: 9.065880610377446] tot_loss[loss=2.274, over 31506559.43 fram2022-06-18 10:28:32,701 INFO [train.py:445] Ep2022-06-18 10:28:33,150 INFO [train.py:445] Epoch 11, batch 8000, loss[loss=2.195, over 61600.00 frames., ppl: 8.9786481451912] tot_loss[loss=2.275, over 31445405.73 frames.2022-06-18 10:29:43,372 INFO [train.py:445] Epoch 1122022-06-18 10:29:43,637 INFO [train.py:445] Epoch 11, batch 8200, loss[loss=2.188, over 44400.00 frames., ppl: 8.921466816967042] tot_loss[loss=2.277, over 31282592.62022-06-18 10:30:51,841 INFO [train.py:445] Epoch 122022-06-18 10:30:51,924 INFO [train.py:445] Epoch 11, batch 8400, loss[loss=2.193, over 43600.00 frames., ppl: 8.958709134423597] tot_loss[loss=2.277, over 313317802022-06-18 10:32:05,069 INFO [2022-06-18 10:32:05,15522022-06-18 10:32:05,279 INFO [train.py:445] Epoch 11, batch 8600, loss[loss=2.192, over 31200.00 frames., ppl: 8.952798328616723] tot_loss[loss=2.276, over 315298972022-06-18 10:33:18,5942022-06-18 10:33:182022-06-18 10:33:19,136 INFO [train.py:445] Epoch 11, batch 8800, loss[loss=2.183, over 61600.00 frames., ppl: 8.875948212101221] tot_loss[loss=2.277, over 31319735.13 frames.2022-06-18 10:34:32,811 INFO [train.py:445] Epoch 11, batch 9000, loss[loss=2.235, over 24000.00 frames., ppl: 9.350568788819512] tot_loss[loss=2.278, over 31781740.28 frames., ppl: 9.75312588931317], batch size: 400
+2022-06-18 10:35:46,304 I2022-06-18 10:2022-06-18 10:35:46,416 INFO [train.py:445] Epoch 11, batch 9200, loss[loss=2.227, over 20800.00 frames., ppl: 9.275112479380784] tot_loss[loss=2.279, over 31482859.05 frames., pp2022-06-18 10:37:01,421 2022-06-18 10:37:01,458 INFO2022-06-18 10:37:01,752 INFO [train.py:445] Epoch 11, batch 9400, loss[loss=2.188, over 44800.00 frames., ppl: 8.914796303747517] tot_loss[loss=2.276, over 31596889.22022-06-18 10:38:13,517 INF2022-06-18 12022-06-18 10:38:13,984 INFO [train.py:445] Epoch 11, batch 9600, loss[loss=2.212, over 57600.00 frames., ppl: 9.136072892750812] tot_loss[loss=2.278, over 31769115.39 frames., pp2022-06-18 10:39:27,2022-06-18 10:39:28,312 INFO [train.py:445] Epoch 11, batch 9800, loss[loss=2.191, over 43600.00 frames., ppl: 8.948524653002822] tot_loss[loss=2.28, over 31338394.39 frames., ppl: 9.774234735561312022-06-18 10:40:40,613 INFO [train.py2022022-06-18 10:40:40,895 INFO [train.py:445] Epoch 11, batch 10000, loss[loss=2.211, over 38400.00 frames., ppl: 9.125933527028444] tot_loss[loss=2.278, over 31476285.29 frames., ppl: 9.754208908074345], batch size: 400
+2022-06-18 10:40:40,895 INFO 2022-06-18 10:40:41,2022-02022-06-18 10:40:41,173 INF2022-06-18 10:40:41,173 INFO [train.py:480] Epoch 11, validation: loss=2.2022-06-18 10:41:52,2022-06-18 10:41:52,356 INFO [t202022-06-18 10:41:52,480 INFO [train.py:445] Epoch 11, batch 10200, loss[loss=2.167, over 27200.00 frames., ppl: 8.733640820761122] tot_loss[loss=2.274, over 32191532022-06-18 10:43:02,692022-06-18 10:43:02,743 INFO [train.py:445] Epoch 11, batch 10400, loss[loss=2.207, over 26400.00 frames., ppl: 9.091230886993946] tot_loss[loss=2.279, over 31511319.72 frames., ppl: 9.7694287023242022-06-18 10:44:16,942022-06-18 10:44:16,2022-06-18 10:44:16,996 INFO [train.py:445] Epoch 11, batch 10600, loss[loss=2.298, over 13200.00 frames., ppl: 9.953642507559582] tot_loss[loss=2.277, over 31697534.05 frames.2022-06-18 10:45:33,006 INFO [train.py:445] Epoch 11, batch 10800, loss[loss=2.201, over 38800.00 frames., ppl: 9.03048497260757] tot_loss[loss=2.278, over 31949053.10 frames., ppl: 9.753783792321975], batch size: 400
+2022-06-18 10:46:42,958 INFO [train.py:445] Epoch 11,2022-06-18 10:46:42,998 INFO [train.py:445] Epoch 11, batch 11000, loss[loss=2.245, over 25600.00 frames., ppl: 9.443683160179065] tot_loss[loss=2.277, over 31891091.2022-06-18 10:47:54,041 INFO 2022-06-18 10:2022-06-182022-06-18 10:47:54,188 INFO [train.py:445] Epoch 11, batch 11200, loss[loss=2.247, over 23200.00 frames., ppl: 9.455658530482722] tot_loss[loss=2.278, over 31736730.2022-06-18 10:49:05,472022-062022-06-18 10:2022-06-18 10:49:05,744 INFO [train.py:445] Epoch 11, batch 11400, loss[loss=2.194, over 34400.00 frames., ppl: 8.971223580537552] tot_loss[loss=2.278, over 31720526.18 frames2022-06-18 10:50:17,232022-06-18 10:50:17,454 INFO [t2022-06-18 10:50:17,598 INFO [train.py:445] Epoch 11, batch 11600, loss[loss=2.153, over 45600.00 frames., ppl: 8.607341261965207] tot_loss[loss=2.28, over 31457269.2022-06-18 10:51:30,02220222022-06-18 10:51:30,277 INFO [train.py:445] Epoch 11, batch 11800, loss[loss=2.173, over 34800.00 frames., ppl: 8.788164261407253] tot_loss[loss=2.281, over 31130470.72 frames., ppl: 9.784122022-06-18 10:52:47,137 INFO [train.py:445] Ep2022-06-18 10:52:47,255 INFO [train.py:445] Epoch 11, batch 12000, loss[loss=2.168, over 49200.00 frames., ppl: 8.741543111415774] tot_loss[loss=2.278, over 31954523.06 fram2022-06-18 10:54:01,370 I2022-06-18 10:54:01,52022-06-18 10:54:01,666 INFO [train.py:445] Epoch 11, batch 12200, loss[loss=2.176, over 38000.00 frames., ppl: 8.811575022625284] tot_loss[loss=2.277, over 32169934.09 fr2022-06-18 10:55:13,746 INFO [train.py:445] Epoch 11, batch 12400, loss[loss=2.214, over 29600.00 frames., ppl: 9.153585310756425] tot_loss[loss=2.277, over 31972186.86 frames., ppl: 9.750142681999032], batch size: 400
+2022-06-18 10:56:24,661 INFO [train.py:445] Epoch 11, batch 12600, loss[loss=2.184, over 33600.00 frames., ppl: 8.87996750868465] tot_loss[loss=2.277, over 32026111.60 frames., ppl: 9.74952683191081], batch size: 400
+2022-06-18 10:56:53,553 INFO22022-06-18 10:56:54,033 INFO [train.py:445] Epoch 12, batch 0, loss[loss=2.165, over 64870.00 frames., ppl: 8.718271416374545] tot_loss[loss=2.165, over 64870.00 frames., ppl: 8.71827142022-06-18 10:58:10,159 INF2022-06-18 10:58:10,352 INFO [train.py:445] Epoch 12, batch 200, loss[loss=2.172, over 31103.00 frames., ppl: 8.772222193692679] tot_loss[loss=2.264, over 2928801.59 frames., ppl: 9.617924242022-06-18 10:59:22,372 2022-06-18 10:59:22,718 INFO [train.py:445] Epoch 12, batch 400, loss[loss=2.163, over 45200.00 frames., ppl: 8.699690691654475] tot_loss[loss=2.267, over 5595372.71 frames., ppl: 9.65363802582022-06-18 11:00:34,409 IN2022-06-18 11:00:342022-06-18 11:00:34,557 INFO [train.py:445] Epoch 12, batch 600, loss[loss=2.229, over 22800.00 frames., ppl: 9.290365213406519] tot_loss[loss=2.264, over 8151311.99 frame2022-06-18 11:01:46,938 INFO [train.py:42022-2022-06-18 11:01:47,339 INFO [train.py:445] Epoch 12, batch 800, loss[loss=2.154, over 68000.00 frames., ppl: 8.623097797551685] tot_loss[loss=2.263, over 10511100.40 frame2022-06-18 11:03:01,440 INFO [train.py:42022-06-18 11:03:01,576 INFO [train.py:445] Epoch 12, batch 1000, loss[loss=2.211, over 37200.00 frames., ppl: 9.124870013159354] tot_loss[loss=2.264, over 12398561.65 frames., p2022-06-18 11:04:14,126 INFO [train.py:445] Epoch 12, batch 1200, loss[loss=2.164, over 36800.00 frames., ppl: 8.705681714650412] tot_loss[loss=2.264, over 14264057.32 frames., ppl: 9.61836283246935], batch size: 400
+2022-06-18 11:05:23,1692022-06-18 11:052022-06-18 11:05:23,427 INFO [train.py:445] Epoch 12, batch 1400, loss[loss=2.201, over 36000.00 frames., ppl: 9.037709929892493] tot_loss[loss=2.265, over 15882038.16 frames., pp2022-06-18 11:06:35,3022022-06-18 11:062022-06-182022-06-18 11:06:35,690 INFO [train.py:445] Epoch 12, batch 1600, loss[loss=2.205, over 50000.00 frames., ppl: 9.070567614940579] tot_loss[loss=2.268, over 17189440.99 f2022-06-18 11:07:50,5392022-06-18 11:072022-06-18 11:07:50,771 INFO [train.py:445] Epoch 12, batch 1800, loss[loss=2.178, over 41200.00 frames., ppl: 8.826366869973727] tot_loss[loss=2.264, over 19060172.50 frames., p2022-06-18 11:09:05,351 2022-06-18 11:092022-06-12022-06-18 11:09:05,632 INFO [train.py:445] Epoch 12, batch 2000, loss[loss=2.159, over 35200.00 frames., ppl: 8.666708534587812] tot_loss[loss=2.269, over 19846341.71 2022-06-18 11:10:18,003 INFO [train.py:442022-06-18 11:10:18,266 INFO [train.py:445] Epoch 12, batch 2200, loss[loss=2.215, over 46803.00 frames., ppl: 9.162792331294371] tot_loss[loss=2.266, over 21086081.41 frames., 2022-06-18 11:11:32,077 22022-06-18 11:11:32,12022-06-18 11:11:32,263 INFO [train.py:445] Epoch 12, batch 2400, loss[loss=2.237, over 27600.00 frames., ppl: 9.366498928566212] tot_loss[loss=2.263, over 22781691.96 fra2022-06-18 11:12:44,483 22022-06-18 11:12:44,840 INFO [train.py:445] Epoch 12, batch 2600, loss[loss=2.15, over 50400.00 frames., ppl: 8.585755361592488] tot_loss[loss=2.267, over 23386483.00 frames., ppl: 9.65359649582022-06-18 11:13:56,2312022-06-18 11:13:56,243 INFO [train.py:445] Epoch 12, batch 2800, loss[loss=2.187, over 61600.00 frames., ppl: 8.911578261993522] tot_loss[loss=2.267, over 23922662.16 frames., ppl: 9.652673871022022-06-18 11:15:07,382022-06-18 11:15:07,484 2022-06-18 11:15:07,624 INFO [train.py:445] Epoch 12, batch 3000, loss[loss=2.162, over 46400.00 frames., ppl: 8.684405915172077] tot_loss[loss=2.269, over 24453079.95 fram2022-06-18 11:16:19,702022-06-18 11:16:19,828 INFO [train.py:445] Epoch 12, batch 3200, loss[loss=2.225, over 25200.00 frames., ppl: 9.256047833712836] tot_loss[loss=2.268, over 25446697.57 frames., ppl: 9.65838105042352022-06-18 11:17:34,239 INFO [train.py:445] Epoch 12, batch 3400, loss[loss=2.174, over 33200.00 frames., ppl: 8.797766083327089] tot_loss[loss=2.265, over 26615323.33 frames., ppl: 9.63497607943727], batch size: 400
+2022-06-18 11:18:45,902022-06-18 11:182022-06-182022-06-18 11:18:46,440 INFO [train.py:445] Epoch 12, batch 3600, loss[loss=2.182, over 65600.00 frames., ppl: 8.867854032154945] tot_loss[loss=2.268, over 26741447.79 2022-06-18 11:19:57,96622022-06-18 11:19:58,12022-06-18 11:19:58,199 INFO [train.py:445] Epoch 12, batch 3800, loss[loss=2.209, over 28800.00 frames., ppl: 9.107895822089102] tot_loss[loss=2.271, over 26797717.63 fram2022-06-18 11:21:11,480 2022-06-18 11:21:11,556 IN2022-06-18 11:21:12,053 INFO [train.py:445] Epoch 12, batch 4000, loss[loss=2.179, over 70800.00 frames., ppl: 8.838687723648917] tot_loss[loss=2.267, over 27781828.802022-06-18 11:22:25,139 INFO [train.py2022-06-18 112022-06-18 11:22:25,640 INFO [train.py:445] Epoch 12, batch 4200, loss[loss=2.2, over 67200.00 frames., ppl: 9.028002186899533] tot_loss[loss=2.268, over 28163795.06 2022-06-18 11:23:39,548 INFO [train.py:445] Epoch 12, batch 4400, loss[loss=2.205, over 44400.00 frames., ppl: 9.068888706636914] tot_loss[loss=2.267, over 28630178.80 frames., ppl: 9.654570337066888], batch size: 400
+2022-06-18 11:24:50,953202022-06-18 11:24:51,042022-02022-06-18 11:24:51,162 INFO [train.py:445] Epoch 12, batch 4600, loss[loss=2.184, over 30800.00 frames., ppl: 8.882563059046122] tot_loss[loss=2.27, over 28725986.2022-06-18 11:26:00,0102022-06-18 11:26:00,186 INFO [train.py:445] Epoch 12, batch 4800, loss[loss=2.196, over 25200.00 frames., ppl: 8.990870714107771] tot_loss[loss=2.27, over 28878474.23 frames., ppl: 9.679720718092022-06-18 11:27:15,5392022-06-18 11:27:15,843 INFO [train.py:445] Epoch 12, batch 5000, loss[loss=2.188, over 38000.00 frames., ppl: 8.920217346548005] tot_loss[loss=2.268, over 29467192.98 frames., ppl: 9.664804613492022-06-18 11:28:27,473 INFO [train.py:445] Ep2022-06-18 11:28:27,542 INFO [train.py:445] Epoch 12, batch 5200, loss[loss=2.171, over 23600.00 frames., ppl: 8.767941217313146] tot_loss[loss=2.272, over 29410460.27 fra2022-06-18 11:29:43,255 INF2022-06-18 11:29:43,2022022-06-18 11:29:43,595 INFO [train.py:445] Epoch 12, batch 5400, loss[loss=2.187, over 47600.00 frames., ppl: 8.90695698363374] tot_loss[loss=2.27, over 30085634.54 fr2022-06-18 11:30:59,0322022-06-18 11:30:59,174 INFO [train.py:445] Epoch 12, batch 5600, loss[loss=2.247, over 22800.00 frames., ppl: 9.455942371191892] tot_loss[loss=2.27, over 30065084.15 frames., ppl: 9.6796571307302022-06-18 11:32:12,4072022-06-18 11:32:12,409 INFO [train.py:445] Epoch 12, batch 5800, loss[loss=2.224, over 23200.00 frames., ppl: 9.248830122775843] tot_loss[loss=2.269, over 30463494.82 frames., ppl: 9.67287654172022-06-18 11:33:25,690 2022-06-182022-06-18 11:32022-06-18 11:33:25,755 INFO [train.py:445] Epoch 12, batch 6000, loss[loss=2.192, over 24800.00 frames., ppl: 8.949602545564508] tot_loss[loss=2.271, over 30655258.19 f2022-06-18 11:34:40,422022-06-18 12022-06-18 112022-06-18 11:34:40,796 INFO [train.py:445] Epoch 12, batch 6200, loss[loss=2.204, over 48400.00 frames., ppl: 9.065280923906272] tot_loss[loss=2.272, over 30351189.26 fra2022-06-18 11:35:53,612022-06-18 12022-06-18 11:35:54,009 INFO [train.py:445] Epoch 12, batch 6400, loss[loss=2.151, over 49600.00 frames., ppl: 8.59327862747957] tot_loss[loss=2.272, over 30434671.65 frames., ppl: 92022-06-18 11:37:07,82022022-06-18 2022-06-18 12022-06-18 11:37:08,211 INFO [train.py:445] Epoch 12, batch 6600, loss[loss=2.175, over 40800.00 frames., ppl: 8.79934051290707] tot_loss[loss=2.272, over 31032098.52 fra2022-06-18 11:38:20,542022022-06-18 11:38:20,692022022-06-18 11:38:20,887 INFO [train.py:445] Epoch 12, batch 6800, loss[loss=2.19, over 50000.00 frames., ppl: 8.936207212453423] tot_loss[loss=2.274, over 30710398.23 f2022-06-18 11:39:33,442022022-06-18 11:39:33,583 INFO [train.py:445] Epoch 12, batch 7000, loss[loss=2.251, over 20000.00 frames., ppl: 9.49628235178185] tot_loss[loss=2.273, over 30965823.51 frames., ppl: 9.70997365782022-06-18 11:40:47,32022022-06-18 11:40:47,468 IN2022-06-18 11:40:47,652 INFO [train.py:445] Epoch 12, batch 7200, loss[loss=2.19, over 38800.00 frames., ppl: 8.935110564717908] tot_loss[loss=2.271, over 31395153.18 2022-06-18 11:42:02,724 INFO [train.2022-06-18 2022-06-18 11:42:03,048 INFO [train.py:445] Epoch 12, batch 7400, loss[loss=2.219, over 41205.00 frames., ppl: 9.200216676341793] tot_loss[loss=2.272, over 31560184.93 f2022-06-18 11:43:18,903 2022-06-18 112022-06-18 11:42022-06-18 11:43:18,998 INFO [train.py:445] Epoch 12, batch 7600, loss[loss=2.285, over 14800.00 frames., ppl: 9.823113957116457] tot_loss[loss=2.271, over 31743670.42022-06-18 11:44:31,570 2022-06-18 11:44:31,630 IN202022-06-18 11:44:31,704 INFO [train.py:445] Epoch 12, batch 7800, loss[loss=2.188, over 19200.00 frames., ppl: 8.91868576754905] tot_loss[loss=2.271, over 31867477.512022-06-18 11:45:42,958 INFO [train.py:445] Epoch 12, batch 8000, loss[loss=2.188, over 36400.00 frames., ppl: 8.917072314502866] tot_loss[loss=2.275, over 30813986.17 frames., ppl: 9.724519414604483], batch size: 400
+2022-06-18 11:46:52,867 INFO [train.py:445] Epoch 12, batch 8200, loss[loss=2.172, over 49200.00 frames., ppl: 8.773160822190878] tot_loss[loss=2.275, over 30940579.16 frames., ppl: 9.724520576715955], batch size: 400
+2022-06-18 11:48:07,42022-06-18 11:48:07,793 INFO [train.py:445] Epoch 12, batch 8400, loss[loss=2.193, over 49848.00 frames., ppl: 8.966500028252293] tot_loss[loss=2.274, over 31459496.59 frames., ppl: 9.7211171918182022-06-18 11:49:21,92022-06-18 11:49:2022-06-18 11:49:22,132 INFO [train.py:445] Epoch 12, batch 8600, loss[loss=2.227, over 24000.00 frames., ppl: 9.27171250681961] tot_loss[loss=2.276, over 31406492.18 frames., pp2022-06-18 11:50:36,867 INFO [train.py:445] Epoch 12, b2022-06-18 11:50:36,965 INFO [train.py:445] Epoch 12, batch 8800, loss[loss=2.164, over 33600.00 frames., ppl: 8.710060193036409] tot_loss[loss=2.272, over 3194892022-06-18 11:51:46,404 INFO [train.py:445] Epoch 12, batch 9000, loss[loss=2.157, over 42000.00 frames., ppl: 8.647717271820511] tot_loss[loss=2.275, over 31226877.40 frames., ppl: 9.73128910838076], batch size: 400
+2022-06-18 11:52:58,6412022-06-18 11:52:58,694 IN2022-06-18 11:52:58,812 INFO [train.py:445] Epoch 12, batch 9200, loss[loss=2.189, over 27200.00 frames., ppl: 8.930389566297992] tot_loss[loss=2.271, over 32278807.65 f2022-06-18 11:54:14,348 INFO [train.py:445] Epoch 122022-02022-06-18 11:54:14,517 INFO [train.py:445] Epoch 12, batch 9400, loss[loss=2.165, over 34000.00 frames., ppl: 8.718195027751488] tot_loss[loss=2.273, over 3192022-06-18 11:55:23,795 INFO [train.py:42022-06-18 11:55:24,406 INFO [train.py:445] Epoch 12, batch 9600, loss[loss=2.205, over 75600.00 frames., ppl: 9.067020266567582] tot_loss[loss=2.275, over 31628859.09 frames., p2022-06-18 11:56:34,469202022-06-18 11:56:34,514 INFO2022-06-18 11:56:34,684 INFO [train.py:445] Epoch 12, batch 9800, loss[loss=2.196, over 33600.00 frames., ppl: 8.992661066793803] tot_loss[loss=2.275, over 31762173.2022-06-18 11:57:47,006 IN2022-06-18 11:57:47,054 INFO [t2022-06-18 11:57:47,291 INFO [train.py:445] Epoch 12, batch 10000, loss[loss=2.201, over 42411.00 frames., ppl: 9.03435721148511] tot_loss[loss=2.274, over 31834130.46 frames., ppl: 9.719557108690166], batch size: 201
+2022-06-18 11:2022-06-18 11:57:47,474 INF2022-06-18 11:57:47,474 INFO [train.py:480] Epoch 12, validation: loss=2.333, over 211809.00 frames2022-06-18 11:58:58,8292022-06-18 11:58:52022-06-18 11:58:58,871 INFO [train.py:445] Epoch 12, batch 10200, loss[loss=2.276, over 18400.00 frames., ppl: 9.74237375128402] tot_loss[loss=2.277, over 31349191.53 frames., pp2022-06-18 12:00:10,492022-2022-06-18 2022-06-18 12022-06-18 12:00:10,724 INFO [train.py:445] Epoch 12, batch 10400, loss[loss=2.265, over 26400.00 frames., ppl: 9.628846199923082] tot_loss[loss=2.274, over 31945083.2022-06-18 12:01:23,717 INFO [train.py:445] Epoch 12, batch 10600, loss[loss=2.177, over 70000.00 frames., ppl: 8.818153549737476] tot_loss[loss=2.278, over 31025595.18 frames., ppl: 9.754731369231253], batch size: 400
+2022-06-18 12:02:36,607 I2022-06-18 12:02:36,727 INFO [tr2022-06-18 12:02:36,840 INFO [train.py:445] Epoch 12, batch 10800, loss[loss=2.213, over 30000.00 frames., ppl: 9.144769086243947] tot_loss[loss=2.274, over 322192022-06-18 12:03:48,106 INFO [train.py:442022-06-18 12:03:48,390 INFO [train.py:445] Epoch 12, batch 11000, loss[loss=2.18, over 49200.00 frames., ppl: 8.846275355138923] tot_loss[loss=2.278, over 31265189.43 frames., 2022-06-18 12:05:03,501 INFO [train.py:42022-06-18 12:05:03,620 INFO [train.py:445] Epoch 12, batch 11200, loss[loss=2.212, over 27600.00 frames., ppl: 9.132123845740105] tot_loss[loss=2.277, over 31497870.60 frames., p2022-06-18 12:06:15,571 INFO [2022-06-18 12:06:15,64520222022-06-18 12:06:15,689 INFO [train.py:445] Epoch 12, batch 11400, loss[loss=2.24, over 22400.00 frames., ppl: 9.398015688591258] tot_loss[loss=2.275, over 3200132022-06-18 12:07:29,768 I2022-2022-06-18 12:07:22022-06-18 12:07:29,978 INFO [train.py:445] Epoch 12, batch 11600, loss[loss=2.195, over 27600.00 frames., ppl: 8.977871202754187] tot_loss[loss=2.274, over 32167976.26 fr2022-06-18 12:08:41,459 INFO [train.py:42022-06-18 12022-06-18 12:08:41,524 INFO [train.py:445] Epoch 12, batch 11800, loss[loss=2.213, over 25600.00 frames., ppl: 9.14389455905144] tot_loss[loss=2.274, over 32195495.32022-06-18 12:09:56,915 I2022-06-18 12:09:57,053 IN2022-06-18 12:09:57,368 INFO [train.py:445] Epoch 12, batch 12000, loss[loss=2.181, over 57600.00 frames., ppl: 8.85180811376035] tot_loss[loss=2.273, over 32318964.582022-06-18 12:11:07,898 INFO [train.py:445] Epoch2022-06-18 12:11:07,923 INFO [train.py:445] Epoch 12, batch 12200, loss[loss=2.188, over 30000.00 frames., ppl: 8.914943433924057] tot_loss[loss=2.274, over 32274623.12 2022-06-18 12:12:19,691 INFO [tra2022-06-18 12:12:20,008 INFO [train.py:445] Epoch 12, batch 12400, loss[loss=2.182, over 42000.00 frames., ppl: 8.864819583762284] tot_loss[loss=2.277, over 31466808.35 frames., ppl: 9.72022-06-18 12:13:34,266 INFO [train.py:445] Epoch 12, batch 12600, loss[loss=2.206, over 30800.00 frames., ppl: 9.07527767312843] tot_loss[loss=2.28, over 30579862.68 frames., ppl: 9.778163972274804], batch size: 400
+2022-06-18 12:14:02,192 INFO [train.2022-06-2022-06-18 12:14:02,429 INFO [train.py:445] Epoch 13, batch 0, loss[loss=2.166, over 39600.00 frames., ppl: 8.720392599495852] tot_loss[loss=2.166, over 39600.00 frames2022-06-18 12:15:21,491 INF2022-06-18 12:15:21,641 INF2022-2022-06-18 12:15:21,924 INFO [train.py:445] Epoch 13, batch 200, loss[loss=2.201, over 47838.00 frames., ppl: 9.0359493038525] tot_loss[loss=2.252, over 32392022-06-18 12:16:33,923 INFO [train.py:445] 2022-06-18 12:16:33,924 INFO [train.py:445] Epoch 13, batch 400, loss[loss=2.216, over 25200.00 frames., ppl: 9.1749442211561] tot_loss[loss=2.25, over 6353657.24 frames.,2022-06-18 12:17:44,753 INFO [train.2022-06-18 12:17:44,915 INFO [train.py:445] Epoch 13, batch 600, loss[loss=2.19, over 30000.00 frames., ppl: 8.935436497818893] tot_loss[loss=2.259, over 8324157.81 frames., ppl: 92022-06-18 12:18:58,441 INFO [train.py:445] Epoch 2022-06-18 12:18:58,848 INFO [train.py:445] Epoch 13, batch 800, loss[loss=2.153, over 54800.00 frames., ppl: 8.614134711527893] tot_loss[loss=2.258, over 10912205.302022-06-18 12:20:13,569 INFO [train.py:4452022-06-18 12:20:13,620 INFO [train.py:445] Epoch 13, batch 1000, loss[loss=2.17, over 28400.00 frames., ppl: 8.759789982124706] tot_loss[loss=2.259, over 12734304.87 frames.,2022-06-18 12:21:22,885 INF2022-06-18 12:21:23,327 INFO [train.py:445] Epoch 13, batch 1200, loss[loss=2.177, over 62800.00 frames., ppl: 8.823561679876187] tot_loss[loss=2.265, over 14184069.91 frames., ppl: 9.6273342022-06-18 12:22:38,389 INFO [train.py2022-06-18 12:22:38,756 INFO [train.py:445] Epoch 13, batch 1400, loss[loss=2.192, over 52400.00 frames., ppl: 8.953736300124032] tot_loss[loss=2.263, over 16114218.56 frames., ppl2022-06-18 12:23:53,631 INFO [train.py2022-06-18 12:232022-06-18 12:23:53,767 INFO [train.py:445] Epoch 13, batch 1600, loss[loss=2.213, over 25600.00 frames., ppl: 9.144752676577815] tot_loss[loss=2.258, over 18220308.2022-06-18 12:25:10,049 INFO [train2022-06-18 12:20222022-06-18 12:25:10,191 INFO [train.py:445] Epoch 13, batch 1800, loss[loss=2.17, over 34400.00 frames., ppl: 8.760175852978998] tot_loss[loss=2.258, over 19670283.42022-06-18 12:26:24,328 INFO [train.py:442022-06-18 12:26:24,455 INFO [train.py:445] Epoch 13, batch 2000, loss[loss=2.198, over 39600.00 frames., ppl: 9.011342479389912] tot_loss[loss=2.26, over 20422679.98 frames., p2022-06-18 12:27:34,220 IN2022-06-18 12:2022-06-18 12:27:34,640 INFO [train.py:445] Epoch 13, batch 2200, loss[loss=2.177, over 62800.00 frames., ppl: 8.818294406121675] tot_loss[loss=2.262, over 21423039.14 frames., p2022-06-18 12:28:45,935 INFO [train.py:445] Epoch 132022-06-18 12:28:46,272 INFO [train.py:445] Epoch 13, batch 2400, loss[loss=2.158, over 48000.00 frames., ppl: 8.657854638730372] tot_loss[loss=2.261, over 22709866.2022-06-18 12:30:00,907 INF2022-06-18 12:30:01,181 INFO [train.py:445] Epoch 13, batch 2600, loss[loss=2.197, over 35200.00 frames., ppl: 9.00059716540394] tot_loss[loss=2.265, over 23101729.03 frames., ppl: 9.63245202022-06-18 12:31:17,369 INFO [train.py:445] Epoch 2022-06-18 12:31:17,400 INFO [train.py:445] Epoch 13, batch 2800, loss[loss=2.211, over 27200.00 frames., ppl: 9.124479974763233] tot_loss[loss=2.262, over 24551192.532022-06-18 12:32:31,963 INF2022-06-18 12:2022-062022-06-18 12:32:32,423 INFO [train.py:445] Epoch 13, batch 3000, loss[loss=2.177, over 64000.00 frames., ppl: 8.817856458748588] tot_loss[loss=2.262, over 24972752.50 f2022-06-18 12:33:41,046 INFO [train.py2022-06-18 12:33:41,212 INFO [train.py:445] Epoch 13, batch 3200, loss[loss=2.19, over 32000.00 frames., ppl: 8.936801350355038] tot_loss[loss=2.264, over 25398229.37 frames., ppl:2022-06-18 12:34:51,052 INFO [train2022-06-18 12:34:51,095 INFO [train.py:445] Epoch 13, batch 3400, loss[loss=2.188, over 29600.00 frames., ppl: 8.918588274882076] tot_loss[loss=2.265, over 25882052.34 frames., ppl: 92022-06-18 12:36:05,293 INFO [train.py:445] Epoch 13, batch 3600, loss[loss=2.212, over 31200.00 frames., ppl: 9.130832752725254] tot_loss[loss=2.264, over 26895351.95 frames., ppl: 9.623560812965364], batch size: 400
+2022-06-18 12:37:16,825 INFO [train.py:445] Epoch 13, batch 3800, loss[loss=2.167, over 33600.00 frames., ppl: 8.729710745073245] tot_loss[loss=2.265, over 27369060.50 frames., ppl: 9.630300230213722], batch size: 400
+2022-06-18 12:38:27,347 INFO2022-062022-06-18 12:38:27,420 INFO [train.py:445] Epoch 13, batch 4000, loss[loss=2.224, over 24400.00 frames., ppl: 9.24694320687013] tot_loss[loss=2.268, over 26958512.45 frames., ppl: 9.2022-06-18 12:39:40,373 INFO [trai2022-06-18 12:39:40,554 INFO [train.py:445] Epoch 13, batch 4200, loss[loss=2.184, over 30800.00 frames., ppl: 8.882691485830447] tot_loss[loss=2.269, over 27336966.55 frames., ppl: 9.62022-06-18 12:40:54,556 I2022-06-18 12:40:54,872022-06-18 12:40:55,069 INFO [train.py:445] Epoch 13, batch 4400, loss[loss=2.155, over 56400.00 frames., ppl: 8.627198225831215] tot_loss[loss=2.265, over 28523806.73 fr2022-06-18 12:42:08,667 INFO [train.py:42022-06-2022-06-18 12:42:08,935 INFO [train.py:445] Epoch 13, batch 4600, loss[loss=2.179, over 38800.00 frames., ppl: 8.837073615388269] tot_loss[loss=2.265, over 29043168.27 fr2022-06-18 12:43:22,813 INFO [tra2022-06-18 12:43:23,002 INFO [train.py:445] Epoch 13, batch 4800, loss[loss=2.193, over 28400.00 frames., ppl: 8.960283876328605] tot_loss[loss=2.269, over 28356503.00 frames., ppl: 9.62022-06-18 12:44:35,9492022-06-18 12:44:35,92022-06-18 12:44:36,041 INFO [train.py:445] Epoch 13, batch 5000, loss[loss=2.202, over 28800.00 frames., ppl: 9.047418960624999] tot_loss[loss=2.266, over 29350942.39 fram2022-06-18 12:45:48,254 INFO [train.py:445] Epoch 13, 2022-06-18 12:45:48,370 INFO [train.py:445] Epoch 13, batch 5200, loss[loss=2.145, over 33200.00 frames., ppl: 8.539822395833143] tot_loss[loss=2.264, over 30344792022-06-18 12:47:03,632 INFO [train.py:445] Epoch 13, batch 5400, loss[loss=2.141, over 32400.00 frames., ppl: 8.51139837097746] tot_loss[loss=2.267, over 29950989.69 frames., ppl: 9.654314597097445], batch size: 400
+2022-06-18 12:48:15,527 INFO [train.py:445] Epoch 12022-06-18 12:48:15,789 INFO [train.py:445] Epoch 13, batch 5600, loss[loss=2.172, over 46000.00 frames., ppl: 8.77479872284852] tot_loss[loss=2.265, over 30847463.77 2022-06-18 12:49:28,360 INFO [train.py:445] Epoch 13, batch 5800, loss[loss=2.226, over 76000.00 frames., ppl: 9.262059184070443] tot_loss[loss=2.267, over 30613557.45 frames., ppl: 9.648896735816365], batch size: 400
+2022-06-18 12:50:42,414 IN2022-06-18 12:52022-06-18 12:50:42,695 INFO [train.py:445] Epoch 13, batch 6000, loss[loss=2.176, over 43600.00 frames., ppl: 8.814922546402093] tot_loss[loss=2.27, over 30310206.01 frames., 2022-06-18 12:51:56,493 IN2022-06-18 12:51:56,575 INFO [train.py:445] Epoch 13, batch 6200, loss[loss=2.19, over 19200.00 frames., ppl: 8.938393141657217] tot_loss[loss=2.271, over 30101844.53 frames., ppl: 9.68737249172022-06-18 12:53:09,804 INFO [train.py:445] Epoch 2022-06-18 12:53:09,912 INFO [train.py:445] Epoch 13, batch 6400, loss[loss=2.183, over 21200.00 frames., ppl: 8.868899074027723] tot_loss[loss=2.266, over 31055861.88 2022-06-18 12:54:25,004 2022-06-18 12:52022-06-18 12:54:25,133 INFO [train.py:445] Epoch 13, batch 6600, loss[loss=2.24, over 22000.00 frames., ppl: 9.391380105602332] tot_loss[loss=2.27, over 30598221.47 frames., ppl2022-06-18 12:55:34,146 INFO [train.py:445] Epoch 13, batch 6800, loss[loss=2.16, over 38800.00 frames., ppl: 8.66702689646666] tot_loss[loss=2.27, over 30913537.14 frames., ppl: 9.683432771660945], batch size: 400
+2022-06-18 12:56:43,204 INFO [tr2022-06-18 12:56:43,264 INFO [train.py:445] Epoch 13, batch 7000, loss[loss=2.237, over 52662.00 frames., ppl: 9.368539056196992] tot_loss[loss=2.268, over 31171378.01 frames., ppl: 9.662022-06-18 12:57:54,968 INF2022-06-18 12:57:55,354 INFO [train.py:445] Epoch 13, batch 7200, loss[loss=2.222, over 54400.00 frames., ppl: 9.228650902217378] tot_loss[loss=2.272, over 30463543.70 frames., ppl: 9.698828572022-06-18 12:59:08,909 IN20222022-06-18 12:59:09,148 INFO [train.py:445] Epoch 13, batch 7400, loss[loss=2.201, over 30000.00 frames., ppl: 9.0344053369771] tot_loss[loss=2.269, over 31422678.28 frames., ppl: 9.6662212022-06-18 13:00:28,406 IN2022-06-18 13:00:28,449 INFO [train.py:445] Epoch 13, batch 7600, loss[loss=2.206, over 29600.00 frames., ppl: 9.08195841935404] tot_loss[loss=2.272, over 30867264.06 frames., ppl: 9.7023804842022-06-18 13:01:39,435 INFO [train.py2022-06-18 13:01:39,824 INFO [train.py:445] Epoch 13, batch 7800, loss[loss=2.18, over 50800.00 frames., ppl: 8.842943821338434] tot_loss[loss=2.27, over 31239202.98 frames., ppl: 2022-06-18 13:02:52,037 I2022-06-18 13:02:52,044 IN2022-06-18 13:02:52,167 INFO [train.py:445] Epoch 13, batch 8000, loss[loss=2.229, over 28400.00 frames., ppl: 9.286396256763426] tot_loss[loss=2.27, over 31602896.65 2022-06-18 13:04:01,561 I2022-06-18 13:04:01,588 INFO [train.py:445] Epoch 13, batch 8200, loss[loss=2.212, over 31200.00 frames., ppl: 9.134224063691237] tot_loss[loss=2.272, over 30953224.77 frames., ppl: 9.700044642022-06-18 13:05:15,680 INFO [train.py:445] Epoch 13, batch 8400, loss[loss=2.173, over 25200.00 frames., ppl: 8.783174666552632] tot_loss[loss=2.272, over 31288317.53 frames., ppl: 9.699839787557478], batch size: 400
+2022-06-18 13:06:29,073 INFO [train.py:445] Epoch 13, batch 8600, loss[loss=2.201, over 31600.00 frames., ppl: 9.033502983849504] tot_loss[loss=2.273, over 31169079.10 frames., ppl: 9.708455488134163], batch size: 400
+2022-06-18 13:07:41,884 INFO [train.py:445] Epoc2022-06-18 13:07:42,010 INFO [train.py:445] Epoch 13, batch 8800, loss[loss=2.182, over 41200.00 frames., ppl: 8.866983397980153] tot_loss[loss=2.269, over 32030601.54 fr2022-06-18 13:08:56,419 INFO [tr2022-06-18 13:08:56,472 INFO [train.py:445] Epoch 13, batch 9000, loss[loss=2.184, over 26400.00 frames., ppl: 8.878920755017974] tot_loss[loss=2.271, over 31674356.25 frames., ppl: 9.682022-06-18 13:10:07,872 I2022-06-18 13:10:07,873 INFO [train.py:445] Epoch 13, batch 9200, loss[loss=2.206, over 33600.00 frames., ppl: 9.075947570468054] tot_loss[loss=2.272, over 31541457.72 frames., ppl: 9.6942287072022-06-18 13:11:22,062 INFO [train.py:445] Epoch 13, batch 9400, loss[loss=2.229, over 26800.00 frames., ppl: 9.294742317490119] tot_loss[loss=2.271, over 31810301.52 frames., ppl: 9.690819116378593], batch size: 400
+2022-06-18 13:12:32,672022-06-18 13:12:32,977 INFO [train.py:445] Epoch 13, batch 9600, loss[loss=2.22, over 33600.00 frames., ppl: 9.209018003492517] tot_loss[loss=2.271, over 31647687.82 frames., ppl: 9.6897689984442022-06-18 13:13:49,690 INFO [train.py:445] Epoch 13, batch 9800, loss[loss=2.22, over 27600.00 frames., ppl: 9.206248036111962] tot_loss[loss=2.272, over 31776097.82 frames., ppl: 9.70092429356497], batch size: 400
+2022-06-18 13:15:02,505 2022-06-18 132022-06-18 12022-06-18 13:15:02,609 INFO [train.py:445] Epoch 13, batch 10000, loss[loss=2.222, over 26800.00 frames., ppl: 9.226974807954281] tot_loss[loss=2.272, over 32066407.59 frames., ppl: 9.698229797823405], batch size: 400
+2022-06-18 13:15:02,2022-06-18 13:15:02,797202022-06-18 13:15:02,797 INFO [train.py:480] Epoch 13, validation: loss=2.332, over 211809.00 frames.,2022-06-18 13:16:15,1782022-06-2022-06-18 13:16:15,408 INFO [train.py:445] Epoch 13, batch 10200, loss[loss=2.218, over 26000.00 frames., ppl: 9.186850213891685] tot_loss[loss=2.272, over 31736129.48 frames., ppl: 9.2022-06-18 13:17:25,634 I2022-06-18 13:17:25,750 INFO [2022-06-18 13:17:25,920 INFO [train.py:445] Epoch 13, batch 10400, loss[loss=2.196, over 44000.00 frames., ppl: 8.99301170316576] tot_loss[loss=2.272, over 315051282022-06-18 13:18:40,183 INFO [train.py:445] Epoch 1322022-06-18 13:18:40,237 INFO [train.py:445] Epoch 13, batch 10600, loss[loss=2.167, over 32000.00 frames., ppl: 8.729081533577236] tot_loss[loss=2.272, over 316846622022-06-18 13:19:51,633 INFO2022-2022-06-12022-06-182022-06-18 13:19:52,111 INFO [train.py:445] Epoch 13, batch 10800, loss[loss=2.17, over 58400.00 frames., ppl: 8.76228495185365] tot_loss[loss=2.272, over 32069146.67 2022-06-18 13:21:04,726 INFO [train.py:445] Epoch 13, batch 11000, loss[loss=2.169, over 50400.00 frames., ppl: 8.748450886081796] tot_loss[loss=2.275, over 31475461.50 frames., ppl: 9.727266755626736], batch size: 400
+2022-06-18 13:22:14,741 INFO2022-06-18 13:22:14,791 INFO [train.py:445] Epoch 13, batch 11200, loss[loss=2.221, over 23600.00 frames., ppl: 9.218868588244693] tot_loss[loss=2.272, over 32174634.00 frames., ppl: 9.6958752022-06-18 13:23:28,778 INFO [train.py:445] Epoch 13, batch 11400, loss[loss=2.369, over 10800.00 frames., ppl: 10.691740967026139] tot_loss[loss=2.273, over 31998061.90 frames., ppl: 9.71191877481066], batch size: 400
+2022-06-18 13:24:41,072022-06-182022-06-2022-06-18 13:24:41,446 INFO [train.py:445] Epoch 13, batch 11600, loss[loss=2.197, over 45600.00 frames., ppl: 9.001919284085659] tot_loss[loss=2.276, over 30866506.65 frames., p2022-06-18 13:25:53,318 INFO 2022-06-182022-06-18 13:25:53,457 INFO [train.py:445] Epoch 13, batch 11800, loss[loss=2.217, over 26000.00 frames., ppl: 9.180421944849192] tot_loss[loss=2.277, over 30794559.95 frames., ppl2022-06-18 13:27:06,213 INFO [train.py:445] Epoch2022-06-18 13:27:06,291 INFO [train.py:445] Epoch 13, batch 12000, loss[loss=2.261, over 21200.00 frames., ppl: 9.59382741916221] tot_loss[loss=2.273, over 31620258.42 fr2022-06-18 13:28:17,658 INFO [train.py:2022-06-18 13:28:17,930 INFO [train.py:445] Epoch 13, batch 12200, loss[loss=2.175, over 42800.00 frames., ppl: 8.802586808170906] tot_loss[loss=2.275, over 31243010.65 frames., pp2022-06-18 13:29:25,796 IN2022-06-18 13:29:25,885 INFO [train.py:445] Epoch 13, batch 12400, loss[loss=2.205, over 21200.00 frames., ppl: 9.0674025204686] tot_loss[loss=2.277, over 31029197.78 frames., ppl: 9.7448853252022-06-18 13:30:36,623 INFO [train.py:442022-06-18 13:30:36,629 INFO [train.py:445] Epoch 13, batch 12600, loss[loss=2.209, over 43014.00 frames., ppl: 9.110659227011295] tot_loss[loss=2.277, over 31059414.47 frames., 2022-06-18 13:31:2022-06-18 13:31:03,607 2022-06-18 13:31:03,802 INFO [train.py:445] Epoch 14, batch 0, loss[loss=2.132, over 40000.00 frames., ppl: 8.428858285338599] tot_loss[loss=2.132, over 40000.00 frames., 2022-06-18 13:32:2022-06-22022-06-18 13:32:20,531 INFO [train.py:445] Epoch 14, batch 200, loss[loss=2.216, over 77200.00 frames., ppl: 9.171353209366588] tot_loss[loss=2.266, over 2886647.03 frames., ppl: 9.642537302022-06-18 13:33:33,888 INFO [train.py:445] Epoch 2022-06-18 13:33:34,088 INFO [train.py:445] Epoch 14, batch 400, loss[loss=2.162, over 34000.00 frames., ppl: 8.691334688934953] tot_loss[loss=2.255, over 5809495.62 f2022-06-18 13:32022-06-18 13:34:48,145 INFO [train.py:445] Epoch 14, batch 600, loss[loss=2.159, over 41600.00 frames., ppl: 8.66133996613827] tot_loss[loss=2.263, over 7974096.98 frames., ppl: 9.608920513913047], 2022-06-18 13:36:03,4982022-06-18 13:36:032022-06-18 13:36:03,790 INFO [train.py:445] Epoch 14, batch 800, loss[loss=2.189, over 46800.00 frames., ppl: 8.923958713535692] tot_loss[loss=2.257, over 10421928.70 frames.,2022-06-18 13:37:16,5782022-06-18 13:37:162022-06-18 13:37:16,957 INFO [train.py:445] Epoch 14, batch 1000, loss[loss=2.173, over 51600.00 frames., ppl: 8.781661591912133] tot_loss[loss=2.258, over 12477959.41 frames.2022-06-18 13:382022-06-18 13:38:32,887 INFO [train.py:445] Epoch 14, batch 1200, loss[loss=2.148, over 33200.00 frames., ppl: 8.571636562212625] tot_loss[loss=2.262, over 14225030.29 frames., ppl: 9.599526556036103],2022-06-18 13:39:2022-06-18 13:39:44,897 INF2022-06-18 13:39:44,934 INFO [train.py:445] Epoch 14, batch 1400, loss[loss=2.241, over 19600.00 frames., ppl: 9.403449690766728] tot_loss[loss=2.261, over 15654697.03 frames2022-06-18 13:402022-06-18 13:40:58,555 IN2022-06-18 13:40:58,617 INFO [train.py:445] Epoch 14, batch 1600, loss[loss=2.195, over 32400.00 frames., ppl: 8.983769498214437] tot_loss[loss=2.261, over 17335942.46 frames.2022-06-18 13:42:13,364 INFO [train.py:445] Epoch 14, batch 1800, loss[loss=2.175, over 52800.00 frames., ppl: 8.800476530507934] tot_loss[loss=2.256, over 19551390.95 frames., ppl: 9.541614499054342], batch size: 400
+2022-06-18 13:42022-062022-06-18 13:43:26,283 INFO [2022-06-18 13:43:26,496 INFO [train.py:445] Epoch 14, batch 2000, loss[loss=2.17, over 47600.00 frames., ppl: 8.760732412985654] tot_loss[loss=2.257, over 20393412.082022-06-18 13:44:41,905 I2022-06-18 13:44:41,961 IN2022-06-18 13:44:42,000 INFO [train.py:445] Epoch 14, batch 2200, loss[loss=2.205, over 29200.00 frames., ppl: 9.0744641847259] tot_loss[loss=2.259, over 21349817.76 fr2022-06-18 13:2022-02022-06-18 13:45:51,932022-06-18 13:45:52,233 INFO [train.py:445] Epoch 14, batch 2400, loss[loss=2.175, over 56400.00 frames., ppl: 8.803037436194609] tot_loss[loss=2.264, over 21812805.65 frames.,2022-06-18 13:47:06,002 INFO [train.py:442022-06-18 13:47:06,155 INFO [train.py:445] Epoch 14, batch 2600, loss[loss=2.208, over 36800.00 frames., ppl: 9.093496092977173] tot_loss[loss=2.263, over 22809004.87 frames., 2022-06-18 13:48:16,8742022-06-18 13:48:1202022-06-18 13:48:16,965 INFO [train.py:445] Epoch 14, batch 2800, loss[loss=2.263, over 16800.00 frames., ppl: 9.615638317463869] tot_loss[loss=2.264, over 23617060.23 frames.2022-06-12022-06-12022-06-18 13:49:34,162022-02022-06-18 13:49:34,469 INFO [train.py:445] Epoch 14, batch 3000, loss[loss=2.214, over 57200.00 frames., ppl: 9.151581787753791] tot_loss[loss=2.259, over 25290793.07 fra2022-06-18 13:50:46,268 INFO [train.py:445] Epo2022-06-18 13:50:46,301 INFO [train.py:445] Epoch 14, batch 3200, loss[loss=2.251, over 20400.00 frames., ppl: 9.497713157124045] tot_loss[loss=2.26, over 25967643.76 fr2022-06-18 13:51:59,896 INFO [train.py:4452022-06-18 13:51:59,988 INFO [train.py:445] Epoch 14, batch 3400, loss[loss=2.2, over 36800.00 frames., ppl: 9.029141455562625] tot_loss[loss=2.262, over 26175511.81 frames., p2022-06-18 132022-2022-06-2022-06-18 13:53:13,333 INFO [train.py:445] Epoch 14, batch 3600, loss[loss=2.226, over 29600.00 frames., ppl: 9.26648510187338] tot_loss[loss=2.262, over 26522723.94 frames., ppl: 9.606157552022-06-18 13:54:26,025 INFO [train.py:445] Epoch 14, batch 3800, loss[loss=2.173, over 47600.00 frames., ppl: 8.788347247277061] tot_loss[loss=2.262, over 27439246.80 frames., ppl: 9.601713162919964], batch size: 400
+2022-06-18 13:55:40,468 INFO [train.py:2022-06-18 13:55:40,542 INFO [train.py:445] Epoch 14, batch 4000, loss[loss=2.219, over 28000.00 frames., ppl: 9.196208638841478] tot_loss[loss=2.264, over 27528725.65 frames., p2022-06-18 13:56:56,443 I2022-06-18 13:56:56,42022-06-18 13:56:56,550 INFO [train.py:445] Epoch 14, batch 4200, loss[loss=2.244, over 26800.00 frames., ppl: 9.42767970409892] tot_loss[loss=2.265, over 27699397.41 fra2022-06-18 132022-06-18 13:58:09,537 INFO [train.py:445] Epoch 14, batch 4400, loss[loss=2.186, over 71200.00 frames., ppl: 8.895490482999524] tot_loss[loss=2.264, over 28349781.16 frames., ppl: 9.624753708533168], bat2022-06-18 132022-06-18 13:59:21,702 INFO [train.py:445] Epoch 14, batch 4600, loss[loss=2.232, over 20400.00 frames., ppl: 9.320515569394653] tot_loss[loss=2.264, over 28779594.71 frames., ppl: 9.625066252381881], ba2022-06-18 14:00:32,475 INFO [train.py:445] Epoch 14, batch 4800, loss[loss=2.199, over 32400.00 frames., ppl: 9.012161492518334] tot_loss[loss=2.262, over 29700843.48 frames., ppl: 9.598154718930502], batch size: 400
+2022-06-18 14:01:42,654 INFO [train.py:445] Epoch 14, batch 5000, loss[loss=2.177, over 48000.00 frames., ppl: 8.816864792702699] tot_loss[loss=2.262, over 29865389.83 frames., ppl: 9.602512028526249], batch size: 400
+2022-06-18 14:02:53,388 2022-06-18 14:02:53,658 INFO [train.py:445] Epoch 14, batch 5200, loss[loss=2.178, over 38800.00 frames., ppl: 8.82542996438487] tot_loss[loss=2.265, over 29387181.97 frames., ppl: 9.628897133192022-06-18 2022-06-18 14:04:09,228 INFO [t2022-06-18 14:04:09,410 INFO [train.py:445] Epoch 14, batch 5400, loss[loss=2.172, over 34000.00 frames., ppl: 8.773785738104843] tot_loss[loss=2.268, over 29224337.98 frames., 2022-06-182022-2022-06-18 14:05:24,743 INFO [train.py:445] Epoch 14, batch 5600, loss[loss=2.195, over 45200.00 frames., ppl: 8.984321672588864] tot_loss[loss=2.265, over 30472578.26 frames., ppl: 9.635062118894218], 2022-06-182022-06-18 14:06:36,058 INFO [train.py:445] Epoch 14, batch 5800, loss[loss=2.215, over 24400.00 frames., ppl: 9.159904431500825] tot_loss[loss=2.265, over 30300314.46 frames., ppl: 9.630596530006661], batch 2022-06-182022-062022-02022-06-18 14:07:48,743 INFO [train.py:445] Epoch 14, batch 6000, loss[loss=2.184, over 33600.00 frames., ppl: 8.88563078864201] tot_loss[loss=2.267, over 29906206.24 frames., ppl: 9.6524629618232022-06-18 14:09:01,854 INFO [train.py:445] Epoch 14, batch 6200, loss[loss=2.183, over 37600.00 frames., ppl: 8.873666009653551] tot_loss[loss=2.263, over 31283254.27 frames., ppl: 9.611803919729768], batch size: 400
+2022-06-18 14:102022-06-18 14:10:15,419 INFO [train.py:445] Epoch 14, batch 6400, loss[loss=2.156, over 41600.00 frames., ppl: 8.639807768339356] tot_loss[loss=2.267, over 30927892.49 frames., ppl: 9.64694219800448], 2022-06-18 14:11:27,889 INFO [train.py:42022-06-18 14:11:28,088 INFO [train.py:445] Epoch 14, batch 6600, loss[loss=2.188, over 46800.00 frames., ppl: 8.918536327514351] tot_loss[loss=2.266, over 30742239.06 frames., p2022-06-18 14:12022-06-18 14:12:40,231 INFO [train.py:445] Epoch 14, batch 6800, loss[loss=2.193, over 35200.00 frames., ppl: 8.96299233393718] tot_loss[loss=2.266, over 31329030.15 frames., ppl: 9.642163360633942], b2022-06-18 14:13:53,713 INFO [train.py:445] Epoch 14, batch 7000, loss[loss=2.193, over 33600.00 frames., ppl: 8.963787899985704] tot_loss[loss=2.264, over 31714042.20 frames., ppl: 9.619071189282584], batch size: 400
+2022-06-18 14:12022-06-18 14:15:06,323 I2022-06-18 14:15:06,366 INFO [train.py:445] Epoch 14, batch 7200, loss[loss=2.23, over 20800.00 frames., ppl: 9.297012704737591] tot_loss[loss=2.268, over 30827628.28 frames., ppl2022-06-18 14:2022-062022-06-18 14:16:19,620 INFO [train.py:445] Epoch 14, batch 7400, loss[loss=2.237, over 22800.00 frames., ppl: 9.363782302248573] tot_loss[loss=2.27, over 30440995.30 frames., ppl: 9.6825218754222022-06-18 14:17:33,696 INFO [train.py:445] Epoch 14, batch 7600, loss[loss=2.177, over 74400.00 frames., ppl: 8.816863959455688] tot_loss[loss=2.265, over 31829282.30 frames., ppl: 9.633666185415896], batch size: 400
+2022-06-18 14:122022-06-18 14:18:46,288 INFO [train2022-06-18 14:18:46,479 INFO [train.py:445] Epoch 14, batch 7800, loss[loss=2.171, over 36501.00 frames., ppl: 8.767985194211999] tot_loss[loss=2.267, over 31290900.2022-06-18 14:19:52022-06-18 14:19:57,419 INFO [train.py:445] Epoch 14, batch 8000, loss[loss=2.179, over 37200.00 frames., ppl: 8.839356940394575] tot_loss[loss=2.267, over 31667082.49 frames., ppl: 9.65371832586521]2022-06-18 14:21:22022-06-18 14:21:09,487 INFO [train.py:445] Epoch 14, batch 8200, loss[loss=2.269, over 21600.00 frames., ppl: 9.66637558355975] tot_loss[loss=2.269, over 31481709.86 frames., ppl: 9.665359179045527],2022-06-18 14:2202022-06-18 14:22:22,419 INFO [train.py:445] Epoch 14, batch 8400, loss[loss=2.186, over 31600.00 frames., ppl: 8.899066648791539] tot_loss[loss=2.269, over 31416390.31 frames., ppl: 9.674258181414075]2022-06-18 14:23:2022-06-18 14:23:34,092022-06-18 14:23:34,192 INFO [train.py:445] Epoch 14, batch 8600, loss[loss=2.215, over 28800.00 frames., ppl: 9.157889514415814] tot_loss[loss=2.269, over 31297356.62 frames., p2022-06-18 14:24:462022-06-18 14:24:46,42022-06-18 14:24:46,509 INFO [train.py:445] Epoch 14, batch 8800, loss[loss=2.203, over 28000.00 frames., ppl: 9.054410192714744] tot_loss[loss=2.271, over 31158396.00 frames., 2022-06-18 14:25:56,2022-06-18 14:25:56,72022-06-18 14:25:56,853 INFO [train.py:445] Epoch 14, batch 9000, loss[loss=2.258, over 22000.00 frames., ppl: 9.5618910049105] tot_loss[loss=2.271, over 31262108.23 frames., pp2022-06-18 14:27:092022-06-2022-06-18 14:27:09,689 INFO [train.py:445] Epoch 14, batch 9200, loss[loss=2.174, over 34400.00 frames., ppl: 8.793627384743798] tot_loss[loss=2.273, over 30852890.48 frames., ppl: 9.7045712022-06-18 14:28:2022-06-18 14:28:20,516 INFO [train.py:445] Epoch 14, batch 9400, loss[loss=2.281, over 17200.00 frames., ppl: 9.782595011279675] tot_loss[loss=2.268, over 31737472.59 frames., ppl: 9.655739834973202]2022-06-18 14:29:3202022-06-18 14:29:31,2022-06-18 14:22022-06-18 14:29:32,043 INFO [train.py:445] Epoch 14, batch 9600, loss[loss=2.206, over 20400.00 frames., ppl: 9.083318214805775] tot_loss[loss=2.269, over 312022-06-18 14:30:44,3202022-06-18 14:30:44,551 INFO [train.py:445] Epoch 14, batch 9800, loss[loss=2.177, over 28000.00 frames., ppl: 8.817107932254068] tot_loss[loss=2.268, over 31777649.68 frames., ppl: 9.659155817272022-06-18 14:31:57,2022-06-18 142022-06-18 2022-06-18 14:31:57,919 INFO [train.py:445] Epoch 14, batch 10000, loss[loss=2.213, over 28400.00 frames., ppl: 9.145034940459587] tot_loss[loss=2.271, over 31412810.43 frames., ppl: 9.690690844750145], batch size: 400
+2022-06-18 14:31:57,920 IN2022-06-18 14:31:58,2022-06-18 14:31:58,102 INFO [train.py:480] Epoch 14, validation: loss=2.331, over 211809.00 frames., ppl:2022-06-18 14:33:10,202022-06-18 14:33:10,223 INFO [train.py:445] Epoch 14, batch 10200, loss[loss=2.269, over 15600.00 frames., ppl: 9.665092014645172] tot_loss[loss=2.269, over 31867062.47 frames., ppl: 9.6677540762022-06-18 14:34:23,840 INFO [tr2022-06-18 14:32022-06-18 14:34:23,935 INFO [train.py:445] Epoch 14, batch 10400, loss[loss=2.207, over 22400.00 frames., ppl: 9.087524628555622] tot_loss[loss=2.271, over 31439635.20 f2022-06-18 14:35:33,267 I22022-06-18 14:35:33,3572022-06-18 14:35:33,383 INFO [train.py:445] Epoch 14, batch 10600, loss[loss=2.316, over 19200.00 frames., ppl: 10.13961874925128] tot_loss[loss=2.271, over 31617726.29 f2022-06-18 14:36:45,684 2022-06-12022-06-18 14:36:45,843 INFO2022-06-18 14:36:46,003 INFO [train.py:445] Epoch 14, batch 10800, loss[loss=2.151, over 35600.00 frames., ppl: 8.591892239983672] tot_loss[loss=2.271, ove2022-06-18 14:38:01,171 INFO [train.py:445] Epoch 14, batch 11000, loss[loss=2.178, over 49200.00 frames., ppl: 8.824845876180452] tot_loss[loss=2.271, over 31460782.42 frames., ppl: 9.692944316533518], batch size: 400
+2022-06-18 14:39:13,281 INF202022-06-18 14:39:13,37202022-06-18 14:39:13,576 INFO [train.py:445] Epoch 14, batch 11200, loss[loss=2.2, over 33200.00 frames., ppl: 9.024737418436011] tot_loss[loss=2.271, over 31697910.82022-06-18 14:40:27,283 IN2022-06-18 14:40:27,396 I2022-06-18 14:40:27,657 INFO [train.py:445] Epoch 14, batch 11400, loss[loss=2.193, over 51200.00 frames., ppl: 8.96541164980098] tot_loss[loss=2.273, over 31597927.07 f2022-06-18 14:41:38,642 I2022-06-18 14:41:38,837 INFO [train.py:445] Epoch 14, batch 11600, loss[loss=2.21, over 29600.00 frames., ppl: 9.112584366098421] tot_loss[loss=2.272, over 31631684.92 frames., ppl: 9.6981744452022-06-18 14:42:52,931 I2022-06-2022-06-18 14:42:53,027 INFO [t2022-06-18 14:42:53,119 INFO [train.py:445] Epoch 14, batch 11800, loss[loss=2.201, over 25600.00 frames., ppl: 9.032067046163482] tot_loss[loss=2.274, o2022-06-18 14:44:05,613 INFO [20222022-06-18 14:44:05,861 INFO [train.py:445] Epoch 14, batch 12000, loss[loss=2.187, over 38800.00 frames., ppl: 8.910340602849965] tot_loss[loss=2.269, over 32114512.61 frames., ppl: 9.2022-06-18 14:45:19,041 INFO [train.py:445] Epo2022-06-18 14:45:19,177 INFO [train.py:445] Epoch 14, batch 12200, loss[loss=2.195, over 27200.00 frames., ppl: 8.980320892515813] tot_loss[loss=2.271, over 31860249.94 f2022-06-18 14:46:31,480 INFO2022022-06-18 14:46:31,513 INFO [train.p2022-06-18 14:46:31,738 INFO [train.py:445] Epoch 14, batch 12400, loss[loss=2.171, over 38000.00 frames., ppl: 8.769040425807558] tot_loss[loss=2.2732022-06-18 14:47:42,671 INFO [train2022-06-18 14:47:42,734 INFO [train.py:445] Epoch 14, batch 12600, loss[loss=2.235, over 17200.00 frames., ppl: 9.348048504105547] tot_loss[loss=2.27, over 31844633.99 frames., ppl: 9.62022-06-18 14:48:11,897 INFO [train.py:445] Epoch 15, batch 0, loss[loss=2.18, over 32400.00 frames., ppl: 8.845563978286028] tot_loss[loss=2.18, over 32400.00 frames., ppl: 8.845563978286028], batch size: 400
+2022-06-18 14:49:28,543 INFO [train.py:445] Epoch 2022-06-18 14:49:28,2022-06-18 14:49:28,588 INFO [train.py:445] Epoch 15, batch 200, loss[loss=2.215, over 22000.00 frames., ppl: 9.159639464876806] tot_loss[loss=22022-06-18 14:50:42,658 INFO [2022-02022-06-18 14:52022-06-18 14:50:42,807 INFO [train.py:445] Epoch 15, batch 400, loss[loss=2.218, over 25600.00 frames., ppl: 9.19084477312065] tot_loss[loss=2.256, over 5784653.92 2022-06-18 14:51:55,024 INFO [train2022-06-18 14:51:55,192 INFO [train.py:445] Epoch 15, batch 600, loss[loss=2.238, over 24400.00 frames., ppl: 9.37321848337335] tot_loss[loss=2.245, over 8727842.30 frames., ppl: 2022-06-18 14:53:07,440 INFO [train.2022-06-18 14:53:07,744 INFO [train.py:445] Epoch 15, batch 800, loss[loss=2.156, over 44000.00 frames., ppl: 8.639927366593552] tot_loss[loss=2.247, over 10981700.70 frames., ppl: 92022-06-18 14:54:21,956 INFO 2022-06-18 14:54:22,622 INFO [train.py:445] Epoch 15, batch 1000, loss[loss=2.203, over 79200.00 frames., ppl: 9.054203877124763] tot_loss[loss=2.252, over 12955528.46 frames., ppl: 9.50832022-06-18 14:55:38,413 INFO [train.p2022-06-18 14:55:38,571 INFO [train.py:445] Epoch 15, batch 1200, loss[loss=2.185, over 37600.00 frames., ppl: 8.891328382941412] tot_loss[loss=2.252, over 14717256.50 frames., ppl2022-06-18 14:56:52,501 INFO [t2022-06-18 14:56:52,5322022-06-18 14:56:52,708 INFO [train.py:445] Epoch 15, batch 1400, loss[loss=2.227, over 28000.00 frames., ppl: 9.271454675393496] tot_loss[loss=2.258, over 158590592022-06-18 14:58:06,621 INFO [train.py2022-06-18 14:58:06,696 INFO [train.py:445] Epoch 15, batch 1600, loss[loss=2.175, over 32400.00 frames., ppl: 8.800398208714217] tot_loss[loss=2.254, over 17839546.74 frames., pp2022-06-18 14:59:17,852 INFO [t2022-06-18 14:59:18,007 INFO [train.py:445] Epoch 15, batch 1800, loss[loss=2.196, over 36800.00 frames., ppl: 8.988626027674895] tot_loss[loss=2.256, over 19188625.57 frames., ppl: 9.542022-06-18 15:00:31,247 INFO [t2022-06202022-06-2022-06-18 15:00:31,491 INFO [train.py:445] Epoch 15, batch 2000, loss[loss=2.176, over 33600.00 frames., ppl: 8.81196734848292] tot_loss[loss=2.258, over 20169894.89 fr2022-06-18 15:01:41,964 INFO [t2022-06-18 15:01:2022-06-18 15:01:42,152 INF2022-06-18 15:01:42,313 INFO [train.py:445] Epoch 15, batch 2200, loss[loss=2.207, over 40803.00 frames., ppl: 9.09000035558634] tot_loss[loss=2022-06-18 15:02:53,696 INFO [train.py:42022-06-18 15:02:53,956 INFO [trai2022-06-18 15:02:54,228 INFO [train.py:445] Epoch 15, batch 2400, loss[loss=2.179, over 75200.00 frames., ppl: 8.839460227030957] tot_loss[loss=2022-06-18 15:04:04,616 INFO 2022-06-18 2022022-06-18 15:04:04,818 INFO [t2022-06-18 15:04:04,836 INFO [train.py:445] Epoch 15, batch 2600, loss[loss=2.183, over 28400.00 frames., ppl: 8.873559695239004] tot_loss[loss=2022-06-18 15:05:18,336 INFO [train.py:445] Epoch 15, batch 2800, loss[loss=2.172, over 31600.00 frames., ppl: 8.777776251629756] tot_loss[loss=2.262, over 23815839.47 frames., ppl: 9.60182536567565], batch size: 400
+2022-06-18 15:06:31,463 INFO 2022-06-18 12022-06-18 15:06:31,597 INFO [train.py:445] Epoch 15, batch 3000, loss[loss=2.191, over 30000.00 frames., ppl: 8.945350089158238] tot_loss[loss=2.256, over 25498407.43 frames.,2022-06-18 15:07:44,400 INFO2022-06-18 15:2022-06-18 15:07:44,550 INFO [train.py:445] Epoch 15, batch 3200, loss[loss=2.239, over 20855.00 frames., ppl: 9.385276248468237] tot_loss[loss=2.256, over 26024198.34 frames.2022-06-18 15:08:57,448 INFO2022-06-18 15:08:57,531 INFO [train.py:445] Epoch 15, batch 3400, loss[loss=2.278, over 19200.00 frames., ppl: 9.756307723299894] tot_loss[loss=2.261, over 25876783.83 frames., ppl: 9.59087252022-06-18 15:10:09,372 INF2022-06-18 15:10:09,857 INFO [train.py:445] Epoch 15, batch 3600, loss[loss=2.185, over 57200.00 frames., ppl: 8.891450030169967] tot_loss[loss=2.261, over 26507987.22 frames., ppl: 9.59451392022-06-18 15:11:22,801 IN2022-06-18 15:202022-2022-06-18 15:11:23,368 INFO [train.py:445] Epoch 15, batch 3800, loss[loss=2.189, over 68400.00 frames., ppl: 8.928714507360665] tot_loss[loss=2.262, over 26971743.11 fr2022-06-18 15:12:36,293 I2022-06-18 15:12:36,495 INFO [train.py:445] Epoch 2022-06-18 15:12:36,510 INFO [train.py:445] Epoch 15, batch 4000, loss[loss=2.179, over 28400.00 frames., ppl: 8.837836823544759] tot_loss[loss2022-06-18 15:13:46,606 INFO [train.py:445] Epoch 15, batch 4200, loss[loss=2.197, over 44000.00 frames., ppl: 8.99826341755823] tot_loss[loss=2.263, over 27964370.08 frames., ppl: 9.608955550670162], batch size: 400
+2022-06-18 15:14:59,166 INFO [train.py:445] Epoch 15, batch 4400, loss[loss=2022-06-18 15:14:59,321 INFO [train.py:445] Epoch 15, batch 4400, loss[loss=2.182, over 36800.00 frames., ppl: 8.866286689833412] tot_loss[l2022-06-18 15:16:08,906 INFO2022-06-18 1202202022-06-18 15:16:09,077 INFO [train.py:445] Epoch 15, batch 4600, loss[loss=2.203, over 22800.00 frames., ppl: 9.04782127600406] tot_loss[loss=2.26, over 28977428.79 frames.2022-06-18 15:17:23,147 INFO [train.py:445] Epo2022-06-18 15:17:23,394 INFO [t2022-06-18 15:17:23,468 INFO [train.py:445] Epoch 15, batch 4800, loss[loss=2.199, over 57652.00 frames., ppl: 9.012752538153777] tot_loss[2022-06-18 15:18:38,149 INFO [train.py:445] 20222022-06-18 15:18:38,349 INFO [train.py:445] Epoch 15, batch 5000, loss[loss=2.155, over 31200.00 frames., ppl: 8.625786177480386] tot_loss[loss=2.259, over 29759487.44 fr2022-06-18 15:19:48,038 2022-06-18 15:19:48,272 INFO [train.py:445] Epoch 15, batch 5200, loss[loss=2.185, over 30400.00 frames., ppl: 8.88751663208525] tot_loss[loss=2.264, over 29372180.43 frames., ppl: 9.62403777282022-06-18 15:20:58,6832022-06-18 15:20:58,712022-06-18 15:20:59,221 INFO [train.py:445] Epoch 15, batch 5400, loss[loss=2.299, over 63717.00 frames., ppl: 9.961485463206376] tot_loss[loss=2.26, over 30415888.11 fram2022-06-18 15:22:12,745 2022-06-18 15:22:12,822022022-06-18 15:22:12,875 INFO [train.py:445] Epoch 15, batch 5600, loss[loss=2.287, over 18800.00 frames., ppl: 9.848083276986412] tot_loss[loss=2.259, over 30845974.76 f2022-06-18 15:23:25,762 INFO [train.p2022-06-12022-06-18 15:23:25,917 INFO [train.py:445] Epoch 15, batch 5800, loss[loss=2.137, over 36800.00 frames., ppl: 8.478114780032438] tot_loss[loss=2.262, over 30497424.70 fram2022-06-18 15:24:37,949 INFO [train.py:445] Epoch 15, batch 6000, loss[loss=2.196, over 31600.00 frames., ppl: 8.98678835233738] tot_loss[loss=2.266, over 30164021.99 frames., ppl: 9.63704776071634], batch size: 400
+2022-06-18 15:25:51,946 INFO [train.py:2022-06-18 15:25:52,129 INFO [train.py:445] Epoch 15, batch 6200, loss[loss=2.17, over 28000.00 frames., ppl: 8.756566275711123] tot_loss[loss=2.269, over 29757535.87 frames., ppl2022-06-18 15:27:04,680 IN2022-06-18 12022-06-18 15:27:04,722 INFO [train.py:445] Epoch 15, batch 6400, loss[loss=2.345, over 15200.00 frames., ppl: 10.43801425133899] tot_loss[loss=2.27, over 29849748.50 frames., ppl: 2022-06-18 15:28:14,809 IN2022-06-182022-06-12022-06-18 15:28:14,956 INFO [train.py:445] Epoch 15, batch 6600, loss[loss=2.242, over 20400.00 frames., ppl: 9.41376469365611] tot_loss[loss=2.263, over 30938241.19 frames2022-06-18 15:29:25,511 INFO [train.py:42022-06-18 15:29:25,680 INFO [train.py:445] Epoch 15, batch 6800, loss[loss=2.248, over 28000.00 frames., ppl: 9.470817817907081] tot_loss[loss=2.265, over 30851893.86 frames., 2022-06-18 15:30:36,583 INF2022-06-182022-06-18 15:30:36,742 INFO [train.p2022-06-18 15:30:36,790 INFO [train.py:445] Epoch 15, batch 7000, loss[loss=2.201, over 27200.00 frames., ppl: 9.03455265932534] tot_loss[loss=2022-06-18 15:31:49,787 INFO2022-06-120222022-06-18 15:31:49,971 INFO [train.py:445] Epoch 15, batch 7200, loss[loss=2.173, over 33600.00 frames., ppl: 8.780345968359757] tot_loss[loss=2.265, over 30922403.23 frames.,2022-06-18 15:33:02,067 INFO [train.py:445] Epoch 15, batch 7400, loss[loss=2.177, over 43200.00 frames., ppl: 8.822327276704085] tot_loss[loss=2.265, over 31173542.60 frames., ppl: 9.63069836167368], batch size: 400
+2022-06-18 15:34:15,258 INFO2022-06-18 15:32022-06-18 15:34:15,546 INFO [train.py:445] Epoch 15, batch 7600, loss[loss=2.199, over 37600.00 frames., ppl: 9.013724297482884] tot_loss[loss=2.268, over 30656694.85 frames.2022-06-18 15:35:29,181 INFO 2022-06-18 15:2022-06-18 15:35:29,452 INFO [train.py:445] Epoch 15, batch 7800, loss[loss=2.193, over 40000.00 frames., ppl: 8.964565929179477] tot_loss[loss=2.267, over 31172073.96 frames.,2022-06-18 15:36:42,171 INFO2022-06-18 15:2022-06-18 15:36:42,586 INFO [train.py:445] Epoch 15, batch 8000, loss[loss=2.176, over 66400.00 frames., ppl: 8.814096920647508] tot_loss[loss=2.267, over 30932885.01 frames.,2022-06-18 15:37:56,703 INFO2022-062022-02022-06-18 15:37:56,863 INFO [trai2022-06-18 15:37:57,352 INFO [train.py:445] Epoch 15, batch 8200, loss[loss=2.192, over 75200.00 frames., ppl: 8.954223979803835] tot_loss[loss2022-06-18 15:39:12,222 INFO2022-06-18 15:39:12,276 INFO [train.py:445] Epo2022-06-18 15:39:12,326 INFO [train.py:445] Epoch 15, batch 8400, loss[loss=2.216, over 30400.00 frames., ppl: 9.171284039941796] tot_loss[los2022-06-18 15:40:23,498 INFO [train.py:445] Epoch 15, batch 8600, loss[loss=2.25, over 16000.00 frames., ppl: 9.484854744744576] tot_loss[loss=2.268, over 31274773.52 frames., ppl: 9.662142206533614], batch size: 400
+2022-06-18 15:41:34,565 INFO [2022-06-18 15:41:34,760 INFO [train.py:445] Epoch 15, batch 8800, loss[loss=2.176, over 54400.00 frames., ppl: 8.806682676573391] tot_loss[loss=2.267, over 31594132.37 frames., ppl: 9.65342022-06-18 15:42:47,293 INFO [2022-2022-06-18 15:42022-06-18 15:42:47,569 INFO2022-06-18 15:42:47,729 INFO [train.py:445] Epoch 15, batch 9000, loss[loss=2.24, over 52000.00 frames., ppl: 9.397701557623023] tot_loss2022-06-18 15:43:59,893 INFO [train.py:445] Epoch 15, batch 9200, loss[loss=2.218, over 35600.00 frames., ppl: 9.19225966322013] tot_loss[loss=2.267, over 31680966.78 frames., ppl: 9.650524996145613], batch size: 400
+2022-06-18 15:45:13,390 INFO [train.py:445]2022-06-18 15:45:13,506 INFO [train.py:445] Epoch 15, batch 9400, loss[loss=2.208, over 20800.00 frames., ppl: 9.093652317502668] tot_loss[loss=2.27, over 30956778.14 frames.2022-06-18 15:46:27,147 INFO [train.p2022-06-18 15:46:27,159 INFO [train.py:445] E2022-06-18 15:46:27,171 INFO [train.py:445] Epoch 15, batch 9600, loss[loss=2.321, over 11200.00 frames., ppl: 10.188472920914087] tot_l2022-06-18 15:47:37,539 INFO [train.p2022-06-18 15:42022-06-18 15:47:37,710 INFO [train.py:445] Epoch 15, batch 9800, loss[loss=2.236, over 27600.00 frames., ppl: 9.356650567678905] tot_loss[loss=2.268, over 31533271.52022-06-18 15:48:48,247 INFO [train.py:445] Epoch 15, batch 10000, loss[loss=2.197, over 41200.00 frames., ppl: 9.00031652950365] tot_loss[loss=2.267, over 31943926.02 frames., ppl: 9.646226906057251], batch size: 400
+2022-06-18 15:48:48,247 INFO [train.py:469] Computing validation loss
+2022-06-18 15:48:48,430 INFO [train.2022-06-18 15:48:48,430 INFO [train.py:480] Epoch 15, validation: loss=2.33, over 211809.2022-06-18 15:50:03,443 INFO [train.2022-02022-06-18 15:50:03,780 INFO [train.py:445] Epoch 15, batch 10200, loss[loss=2.181, over 46800.00 frames., ppl: 8.857790986325679] tot_loss[loss=2.267, over 31692468.34 frames.2022-06-18 15:51:16,146 INFO [train.py:445]2022-06-18 15:51:16,442 INFO [train.py:445] Epoch 15, batch 10400, loss[loss=2.171, over 62400.00 frames., ppl: 8.76650097722456] tot_loss[loss=2.267, over 31805924.95 frames.,2022-06-18 15:52:30,672 INFO [train2022-06-18 15:52:30,868 INFO [train.py:445] Epoch 15, batch 10600, loss[loss=2.174, over 56800.00 frames., ppl: 8.795740569610052] tot_loss[loss=2.269, over 31853668.09 frames., ppl: 2022-06-18 15:53:45,846 INFO [tra2022-06-18 15:53:46,112 INFO [train.py:445] Epoch 15, batch 10800, loss[loss=2.196, over 38800.00 frames., ppl: 8.991386963077165] tot_loss[loss=2.268, over 31759993.82 frames., ppl: 9.662022-06-18 15:54:56,345 INFO [tra2022-06-18 15:54:56,368 INFO [train.py:445] Epoch 15, batch 11000, loss[loss=2.201, over 26000.00 frames., ppl: 9.032402226745656] tot_loss[loss=2.27, over 31631275.24 frames., ppl: 9.682022-06-18 15:56:09,683 INFO [tr2022-06-18 15:56:09,2022-06-18 15:56:09,759 INFO [t2022-06-18 15:56:09,759 INFO [train.py:445] Epoch 15, batch 11200, loss[loss=2.232, over 21200.00 frames., ppl: 9.317412392718735] tot_l2022-06-18 15:57:24,850 INFO [train.py2022-06-18 15:57:24,863 INFO [train.py:445] 2022-06-18 15:57:24,870 INFO [train.py:445] Epoch 15, batch 11400, loss[loss=2.278, over 16000.00 frames., ppl: 9.752974014483963] tot_l2022-06-18 15:58:33,552 INFO [t202022-2022-06-18 15:58:33,657 INFO [train.py:445] E2022-06-18 15:58:33,899 INFO [train.py:445] Epoch 15, batch 11600, loss[loss=2.207, over 38800.00 frames., ppl: 9.085627616824304] to2022-06-18 15:59:49,698 INFO [tra2022-06-18 15:59:49,72022-06-18 15:59:49,876 INFO [train.py:445] Epoch 15, batch 11800, loss[loss=2.197, over 39600.00 frames., ppl: 8.998782153424154] tot_loss[loss=2.269, over 31641092022-06-18 16:01:05,966 INFO [tra2022-06-2022022-06-18 16:01:06,370 INFO [train.py:445] Epoch 15, batch 12000, loss[loss=2.164, over 48800.00 frames., ppl: 8.704502774853806] tot_loss[loss=2.266, over 32260360.75 fram2022-06-18 16:02:19,503 INFO [train.2022-06-2022-06-18 2022-06-18 16:02:19,714 INFO [train.py:445] Epoch 15, batch 12200, loss[loss=2.184, over 28800.00 frames., ppl: 8.88306444723717] tot_loss[loss=2.27, over 316230362022-06-18 16:03:39,258 INFO [train.p2022-02022-06-18 2022-06-18 16:03:39,746 INFO [train.py:445] Epoch 15, batch 12400, loss[loss=2.21, over 68400.00 frames., ppl: 9.119388323913556] tot_loss[loss=2.271, over 31607641.2022-06-18 16:04:55,064 INFO [train.py:445] Epo2022-06-18 16:04:55,436 INFO [train.py:445] Epoch 15, batch 12600, loss[loss=2.181, over 60800.00 frames., ppl: 8.854198521714023] tot_loss[loss=2.265, over 32598913.90 f2022-06-18 16:05:23,922 INFO [train.py:2022-06-182022-06-18 16:05:24,296 INFO [train.py:445] Epoch 16, batch 0, loss[loss=2.144, over 57200.00 frames., ppl: 8.537676596864118] tot_loss[loss=2.144, over 57200.00 f2022-06-18 16:06:44,481 INFO [train.py:2022-06-18 16:06:44,686 INFO [train.py:445] Epoch 16, batch 200, loss[loss=2.174, over 33600.00 frames., ppl: 8.79389604624587] tot_loss[loss=2.241, over 3300539.52 frames., p2022-06-18 16:07:57,498 INFO [train.py:42022-06-18 16:07:57,756 INFO [train.py:445] Epoch 16, batch 400, loss[loss=2.17, over 36000.00 frames., ppl: 8.7582422261165] tot_loss[loss=2.243, over 6201660.64 frames., ppl2022-06-18 16:09:13,309 INFO [train.py2022-022022-06-18 16:09:13,511 INFO [train.py:445] Epoch 16, batch 600, loss[loss=2.191, over 33200.00 frames., ppl: 8.944372530772073] tot_loss[loss=2.26, over 8117208.04 frames2022-06-18 16:10:25,222 INFO [train.2022-0202022-062022-06-18 16:10:25,641 INFO [train.py:445] Epoch 16, batch 800, loss[loss=2.153, over 49200.00 frames., ppl: 8.607690873246387] tot_loss[loss=2.251, over 10867708.022022-06-18 16:11:36,299 INFO [train.py:445] Epoch 12022-06-18 16:11:36,409 INFO [train.py:442022-06-18 16:11:36,608 INFO [train.py:445] Epoch 16, batch 1000, loss[loss=2.131, over 45200.00 frames., ppl: 8.424192091308942022-06-18 16:12:52,851 INFO [train.py:445] 2022-06-18 16:12:53,012 INFO [train.py:445] Epoch 16, batch 1200, loss[loss=2.183, over 35200.00 frames., ppl: 8.868933911360845] tot_loss[loss=2.256, over 14352383.60 frames2022-06-18 16:14:03,813 INFO [train2022-06-2022-06-18 162022-06-18 16:14:04,200 INFO [train.py:445] Epoch 16, batch 1400, loss[loss=2.194, over 42747.00 frames., ppl: 8.97545753453699] tot_loss[loss=2.258, over 15702112022-06-18 16:15:16,508 INFO [train.py:445] Epoch 16, b2022-06-18 16:15:16,533 INFO [train.py:445] Epoch 16, batch 1600, loss[loss=2.202, over 21200.00 frames., ppl: 9.042820990477988] tot_loss[loss=2.256, over 173622022-06-18 16:16:29,470 INFO [train.py:445] Epoch 16, batch 1800, loss[loss=2.186, over 38000.00 frames., ppl: 8.8990276950801] tot_loss[loss=2.255, over 19031250.54 frames., ppl: 9.533985470000976], batch size: 400
+2022-06-18 16:17:46,047 INFO [train.p2022-06-182022-06-18 12022-06-18 16:17:46,110 INFO [train.py:445] Epoch 16, batch 2000, loss[loss=2.235, over 14400.00 frames., ppl: 9.350014325458563] tot_loss[loss=2.256, over 192022-06-18 16:18:57,597 INFO [train.py:2022-06-2022-06-18 16:18:57,787 INFO [train.py:445] Epoch 16, batch 2200, loss[loss=2.163, over 37200.00 frames., ppl: 8.69685990082725] tot_loss[loss=2.255, over 21445876.62 fr2022-06-18 16:20:13,219 INFO [train.py:445] Epoch 16, batch 2400, loss[loss=2.208, over 22400.00 frames., ppl: 9.09921007337774] tot_loss[loss=2.254, over 22752566.04 frames., ppl: 9.525719918463885], batch size: 400
+2022-06-18 16:21:24,460 INFO [train.py:445] Epoch 16, batch 2600, loss[loss=2.166, over 67600.00 frames., ppl: 8.721449145316425] tot_loss[loss=2.254, over 23769189.15 frames., ppl: 9.529121936061463], batch size: 400
+2022-06-18 16:22:35,066 INFO [train.py:4452022-06-18 16:22:35,144 INFO [train.py:445] Epoch 16, batch 2800, loss[loss=2.304, over 13600.00 frames., ppl: 10.017033217761748] tot_loss[loss=2.256, over 24249145.08 frames.2022-06-18 16:23:46,946 INFO [train.py:445]2022-06-18 16:232022-06-18 16:23:47,118 INFO [train.py:445] Epoch 16, batch 3000, loss[loss=2.182, over 23200.00 frames., ppl: 8.864550295303548] tot_loss[loss=2.258, over 2452022-06-18 16:24:57,999 INFO [train.py:445]2022-06-18 2022-06-18 16:24:58,302 INFO [train.py:445] Epoch 16, batch 3200, loss[loss=2.184, over 33600.00 frames., ppl: 8.878912497097007] tot_loss[loss=2.255, over 25868812022-06-18 16:26:11,290 INFO [train.py:445] Epoch 16, batch 3400, loss[loss=2.251, over 16800.00 frames., ppl: 9.493164246529991] tot_loss[loss=2.258, over 26210932.53 frames., ppl: 9.568094605070307], batch size: 400
+2022-06-18 16:27:23,083 INFO [train.py:445] 2022-06-182022-06-18 16:27:23,304 INFO [train.py:445] Epoch 16, batch 3600, loss[loss=2.178, over 30000.00 frames., ppl: 8.831583894231573] tot_loss[loss=2.258, over 266248122022-06-18 16:28:33,862 INFO [train.py:445] 202022-06-18 16:28:34,102 INFO [train.py:445] Epoch 16, batch 3800, loss[loss=2.194, over 30800.00 frames., ppl: 8.966660786211486] tot_loss[loss=2.261, over 26730863.36 fram2022-06-18 16:29:50,065 INFO [train.py:445] 202022-06-18 16:2022-06-18 16:29:50,499 INFO [train.py:445] Epoch 16, batch 4000, loss[loss=2.179, over 54000.00 frames., ppl: 8.834875652476205] tot_loss[loss=2.263, over 22022-06-18 16:31:04,631 INFO [train.py:445] E2022-06-18 16:31:04,700 INFO [train.py:445] E2022-06-18 16:31:04,951 INFO [train.py:445] Epoch 16, batch 4200, loss[loss=2.191, over 39600.00 frames., ppl: 8.94555254776288]2022-06-18 16:32:16,745 INFO [train.py:445] Ep2022-06-12022-06-18 16:32:16,917 INFO [train.py:445] Epoch 16, batch 4400, loss[loss=2.226, over 27200.00 frames., ppl: 9.267325797130464] tot_loss[loss=2.259, over 2860072022-06-18 16:33:29,991 INFO [train.py:445] E22022-2022-06-18 16:33:30,163 INFO [train.py:445] Epoch 16, batch 4600, loss[loss=2.201, over 28000.00 frames., ppl: 9.033664935957956] tot_loss[loss=2.258, over 28936496.2022-06-18 16:34:42,566 INFO [train.py:445] Epo2022-06-18 16:34:42,690 INFO [train.py:445] Epoch 16, batch 4800, loss[loss=2.252, over 19600.00 frames., ppl: 9.508770703066979] tot_loss[loss=2.26, over 29019359.73 fra2022-06-18 16:35:53,845 INFO [train.py:445] Epoch 16, batch 5000, loss[loss=2.164, over 44000.00 frames., ppl: 8.704579393625455] tot_loss[loss=2.261, over 29444483.00 frames., ppl: 9.590644538516232], batch size: 400
+2022-06-18 16:37:07,022 INFO [train.py:445] Ep2022-06-18 16:37:072022-06-18 16:37:07,232 INFO [train.py:445] Epoch 16, batch 5200, loss[loss=2.184, over 32000.00 frames., ppl: 8.882141826594246] tot_loss[loss=2.263, o2022-06-18 16:38:18,494 INFO [train.py:445] Epo2022-02022022-06-18 16:38:18,908 INFO [train.py:445] Epoch 16, batch 5400, loss[loss=2.155, over 46000.00 frames., ppl: 8.623922965214426] tot_loss[loss=2.258, over 3046492022-06-18 16:39:29,048 INFO [train.py:445] Epoch 16, ba2022-06-12022-06-18 16:39:29,120 INFO [train.py:445] Epoch 16, batch 5600, loss[loss=2.161, over 32000.00 frames., ppl: 8.6802009361849] tot_loss[loss=2.262, ove2022-06-18 16:40:40,747 INFO [train.py:445] Epoc2022-02022-06-18 16:40:40,996 INFO [train.py:445] Epoch 16, batch 5800, loss[loss=2.19, over 39600.00 frames., ppl: 8.937773047595115] tot_loss[loss=2.259, over 30443224.92022-06-18 16:41:50,563 INFO [train.py:445] Epoch 162022-06-18 16:41:50,630 INFO [train.py:445] Epoch 16, batch 6000, loss[loss=2.199, over 25200.00 frames., ppl: 9.01384686692058] tot_loss[loss=2.26, over 30736397.74 2022-06-18 16:43:02,175 INFO [train.py:445] Ep2022-06-18 16:43:02,215 INFO [train.py:445] Epoch 16, batch 6200, loss[loss=2.24, over 16800.00 frames., ppl: 9.394812216719826] tot_loss[loss=2.262, over 30306990.43 frame2022-06-18 16:44:13,354 INFO [train.py:445] E2022-06-18 16:44:13,803 INFO [train.py:445] Epoch 16, batch 6400, loss[loss=2.171, over 64000.00 frames., ppl: 8.771409910198757] tot_loss[loss=2.262, over 30602420.16 fra2022-06-18 16:45:24,441 INFO [train.py:445] Epoch 202022-06-18 16:45:24,688 INFO [train.p2022-06-18 16:45:24,706 INFO [train.py:445] Epoch 16, batch 6600, loss[loss=2.209, over 37600.00 frames., ppl: 9.10671838507602]2022-06-18 16:46:39,442 INFO [train.py:445] Ep2022-020222022-06-18 16:46:39,675 INFO [train.py:445] Epoch 16, batch 6800, loss[loss=2.189, over 29200.00 frames., ppl: 8.923159336268984] tot_loss[loss=2.265, over 3038402022-06-18 16:47:52,671 INFO [train.py:445] 2022-06-18 16:47:52,781 INFO [train.py:445] Epoch 16, batch 7000, loss[loss=2.188, over 29200.00 frames., ppl: 8.91394513828073] tot_loss[loss=2.262, over 31016901.55 frames.2022-06-18 16:49:07,710 INFO [train.py:4452022-06-18 16:2022-06-18 16:49:08,051 INFO [train.py:445] Epoch 16, batch 7200, loss[loss=2.178, over 47600.00 frames., ppl: 8.82598666944326] tot_loss[loss=2.264, over 3082782022-06-18 16:50:20,957 INFO [train.py:445] Epo22022-06-18 16:50:21,041 INFO [train.py:445] Epoch 16, batch 7400, loss[loss=2.218, over 22400.00 frames., ppl: 9.188772442762497] tot_loss[loss=2.264, over 30871632.46 fr2022-06-18 16:51:35,768 INFO [train.py:445] Epoch 16, batch 7600, loss[loss=2.168, over 32400.00 frames., ppl: 8.738290736395907] tot_loss[loss=2.264, over 31299847.20 frames., ppl: 9.6206129385297], batch size: 400
+2022-06-18 16:52:47,815 INFO [train.py:445] Epoc2022-06-18 16:52:47,854 INFO [train.py:445] Epoch 16, batch 7800, loss[loss=2.258, over 16000.00 frames., ppl: 9.567473233112343] tot_loss[loss=2.261, over 31979250.57 fra2022-06-18 16:53:58,782 INFO [train.py:445] Epoch 16, batch2022-06-18 16:53:58,797 INFO [train.py:445] Epoch 16, batch 8000, loss[loss=2.298, over 19600.00 frames., ppl: 9.9586575976771] tot_loss[loss=2.265, over 3132022-06-18 16:55:09,621 INFO [train.py:4452022-06-2022-06-18 16:55:09,708 INFO [train.py:4452022-06-18 16:55:09,766 INFO [train.py:445] Epoch 16, batch 8200, loss[loss=2.212, over 21600.00 frames., ppl: 9.1378004439762022-06-18 16:56:22,877 INFO [train.py:445] Epo2022-06-18 12022-06-18 16:56:23,130 INFO [train.py:445] Epoch 16, batch 8400, loss[loss=2.176, over 39200.00 frames., ppl: 8.81520610502193] tot_loss[loss=2.266, over 313772022-06-18 16:57:35,251 INFO [train.py:445] Epoch 16, batch 8600, loss[loss=2.156, over 32402022-06-18 16:57:35,288 INFO [train.py:445] Epoch 16, batch 8600, loss[loss=2.177, over 36000.00 frames., ppl: 8.81946067761232022-06-18 16:58:48,117 INFO [train.py:445] Epoch 16, b2022-06-18 16:58:48,184 INFO [train.2022-06-18 16:58:48,372 INFO [train.py:445] Epoch 16, batch 8800, loss[loss=2.172, over 52400.00 frames., ppl: 8.778171772425662022-06-18 17:00:00,185 INFO [train.py:4452022-06-18 17:002022-06-18 17:00:00,397 INFO [train.py:445] Epoch 16, batch 9000, loss[loss=2.184, over 30000.00 frames., ppl: 8.885130655419207] tot_loss[loss=2.265, over 312022-06-18 17:01:15,479 INFO [train.py:445] Epoch 16, ba2022-06-18 17:01:15,679 INFO [train.py:445] Epoch 16, batch 9200, loss[loss=2.151, over 34800.00 frames., ppl: 8.590634458449399] tot_loss[loss=2.267, over 3118492022-06-18 17:02:27,231 INFO [train.py:4452022-2022-06-18 17:02:27,388 INFO [train.py:445] Epoch 16, batch 9400, loss[loss=2.202, over 22800.00 frames., ppl: 9.045587808833792] tot_loss[loss=2.267, over 31331205.34 fr2022-06-18 17:03:41,166 INFO [train.py:445] 2022-06-18 17:03:41,589 INFO [train.py:445] Epoch 16, batch 9600, loss[loss=2.172, over 52800.00 frames., ppl: 8.771988641955652] tot_loss[loss=2.262, over 32129717.90 frames2022-06-18 17:04:54,735 INFO [train.py:445]2022-06-18 17:04:55,100 INFO [train.py:445] Epoch 16, batch 9800, loss[loss=2.227, over 58400.00 frames., ppl: 9.273897067412177] tot_loss[loss=2.263, over 32252200.81 frames.2022-06-18 17:06:09,737 INFO [train.py:4452022-06-18 17:06:09,916 INFO [train.py:445] Epoch 16, batch 10000, loss[loss=2.17, over 24400.00 frames., ppl: 8.757981185232989] tot_loss[loss=2.263, over 32205253.40 frames., ppl: 9.61495974072527], batch size: 400
+2022-06-18 17:06:09,917 INFO [2022-06-18 17:06:10,105 INFO [train.py:480] Epoc2022-06-18 17:06:10,105 INFO [train.py:480] Epoch 16, validation: loss=2.328,2022-06-18 17:07:25,888 INFO [train.py:42022-06-2022-06-18 17:07:26,054 INFO [train.py:445] Epoch 16, batch 10200, loss[loss=2.202, over 23600.00 frames., ppl: 9.045166575252114] tot_loss[loss=2.266, over 31757618.92 fr2022-06-18 17:08:36,597 INFO [train.py:445] Ep2022-06-18 17:08:36,795 INFO [train.py:4452022-06-18 17:08:36,798 INFO [train.py:445] Epoch 16, batch 10400, loss[loss=2.198, over 33200.00 frames., ppl: 9.003667662912857] t2022-06-18 17:09:51,244 INFO [train.py:445] Epoch 16, ba2022-06-18 17:09:51,563 INFO [train.py:445] Epoch 16, batch 10600, loss[loss=2.174, over 46800.00 frames., ppl: 8.791500389945305] tot_loss[loss=2.266, over 3183682022-06-18 17:11:04,705 INFO [train.p2022-06-18 17:11:04,714 INFO [train.py:445] Epoch2022-06-18 17:11:04,825 INFO [train.py:445] Epoch 16, batch 10800, loss[loss=2.133, over 27600.00 frames., ppl: 8.438597989328134] to2022-06-18 17:12:19,382 INFO [train.p2022-062022-06-18 12022-06-18 17:12:19,564 INFO [train.py:445] Epoch 16, batch 11000, loss[loss=2.212, over 26800.00 frames., ppl: 9.132885628594796] tot_loss[loss=2.268, over 314202022-06-18 17:13:32,578 INFO [train.py:445] 2022022-06-18 17:13:33,211 INFO [train.py:445] Epoch 16, batch 11200, loss[loss=2.265, over 56432.00 frames., ppl: 9.634579323263674] tot_loss[loss=2.268, over 31655017.76 fra2022-06-18 17:14:45,156 INFO [train.py:445] Epoch 16, batch 11400, loss[loss=2.204, over 31200.00 frames., ppl: 9.057058704774224] tot_loss[loss=2.265, over 32216585.78 frames., ppl: 9.632400777144646], batch size: 400
+2022-06-18 17:15:56,655 INFO [train.p2022-06-18 17:15:56,2022-06-18 17:15:56,776 INFO [train.py:445] Epoch 16, batch 11600, loss[loss=2.245, over 18000.00 frames., ppl: 9.443716591245519] tot_loss[loss=2.268, over 318012022-06-18 17:17:07,032 INFO [train.p2022-06-18 17:2022-06-18 17:17:07,422 INFO [train.py:445] Epoch 16, batch 11800, loss[loss=2.175, over 51600.00 frames., ppl: 8.80070328989382] tot_loss[loss=2.27, over 31156147.53 f2022-06-18 17:18:23,813 INFO [train.p2022-06-18 17:18:24,045 INFO [train.py:445] Epoch 16, batch 12000, loss[loss=2.154, over 38000.00 frames., ppl: 8.622311526350229] tot_loss[loss=2.264, over 32206282.29 frames., ppl:2022-06-18 17:19:35,303 INFO [train.p2022-06-18 17:19:35,361 INFO [train.py:445] Epoch 16, batch 12200, loss[loss=2.231, over 25200.00 frames., ppl: 9.309441598981785] tot_loss[loss=2.264, over 32171016.72 frames., pp2022-06-18 17:20:47,562 INFO [train.py:445] Epoch2022-06-18 17:20:47,588 INFO [train.py:445] Epoch 16, batch 12400, loss[loss=2.216, over 23200.00 frames., ppl: 9.166726221033008] tot_loss[loss=2.268, over 31603624.45 f2022-06-18 17:21:59,219 INFO [train2022-06-18 17:2022-06-18 17:21:59,499 INFO [train.py:445] Epoch 16, batch 12600, loss[loss=2.182, over 45200.00 frames., ppl: 8.861427428005774] tot_loss[loss=2.269, over 31414732.40 fr2022-06-18 17:22:28,168 INFO [train.py:2022022-06-18 17:22:28,241 INFO [train.py:442022-06-18 17:22:28,246 INFO [train.py:445] Epoch 17, batch 0, loss[loss=2.189, over 29200.00 frames., ppl: 8.924566821627005] to2022-06-18 17:23:48,657 INFO [tr2022-06-18 17:23:49,154 INFO [train.py:445] Epoch 17, batch 200, loss[loss=2.2, over 72683.00 frames., ppl: 9.02341224267441] tot_loss[loss=2.241, over 3197101.03 frames., ppl: 9.401662022-06-18 17:25:01,715 INFO [train.py:445] 2022-06-18 17:25:01,794 INFO [train.py:445] Epoch 17, batch 400, loss[loss=2.165, over 33200.00 frames., ppl: 8.711325534163624] tot_loss[loss=2.247, over 5978143.01 frames2022-06-18 17:26:10,947 INFO [train.p2022-06-18 17:26:102022-06-18 17:26:11,088 INFO [train.py:445] Epoch 17, batch 600, loss[loss=2.161, over 40000.00 frames., ppl: 8.679270661767886] tot_loss[loss=2.255, over 792762022-06-18 17:27:27,660 INFO [train.p20222022-06-18 17:22022-06-18 17:27:27,837 INFO [train.py:445] Epoch 17, batch 800, loss[loss=2.176, over 34800.00 frames., ppl: 8.809883284679612] tot_loss[loss=2.256, over 1009902022-06-18 17:28:40,570 IN2022-06-18 17:28:40,639 INFO [train.py:445] Epoch 17, bat2022-06-18 17:28:40,679 INFO [train.py:445] Epoch 17, batch 1000, loss[loss=2.15, over 19600.00 frames., ppl: 8.58075481127003] tot_los2022-06-18 17:29:51,200 INFO [train.py:445] Epoch 17, batch 1200, loss[loss=2.182022-06-18 17:29:51,208 INFO [train.py:445] Epoch 17, batch 1200, loss[loss=2.222, over 32000.00 frames., ppl: 9.228775883213604] tot_loss2022-06-18 17:31:05,407 I2022-06-2022-06-18 17:31:05,481 INFO [train.py:445] Ep2022-06-18 17:31:05,723 INFO [train.py:445] Epoch 17, batch 1400, loss[loss=2.153, over 50000.00 frames., ppl: 8.60859071483322] tot_loss[2022-06-18 17:32:18,935 INFO [trai2022-06-18 17:32:19,02022-06-18 17:32:19,298 INFO [train.py:445] Epoch 17, batch 1600, loss[loss=2.154, over 42860.00 frames., ppl: 8.615100506774596] tot_loss[loss=2.255, over 1730652022-06-18 17:33:28,179 INFO [train.py:445] Epoch 17, b2022-06-18 17:33:28,397 INFO [train.py:445] Epoch 17, batch 1800, loss[loss=2.163, over 38400.00 frames., ppl: 8.697054591957635] tot_loss[loss=2.254, over 1886802022-06-18 17:34:40,361 INFO [train.py:42022-06-18 17:34:40,533 INFO [train.py:445] Epoch 17, batch 2000, loss[loss=2.193, over 45225.00 frames., ppl: 8.964058844997618] tot_loss[loss=2.251, over 20477883.40 frames., p2022-06-18 17:35:56,121 INFO2022022-06-18 17:35:56,278 INFO [train.py:445] Epoch 17, batch 2200, loss[loss=2.238, over 25600.00 frames., ppl: 9.376002823122372] tot_loss[loss=2.259, over 20911415.54 frames., ppl: 9.5692022-06-18 17:37:08,541 INFO2022-06-18 12022-06-18 17:37:08,754 INFO [train.py:445] Epoch 17, batch 2400, loss[loss=2.201, over 33600.00 frames., ppl: 9.038474592397638] tot_loss[loss=2.258, over 21942743.18 frames., 2022-06-18 17:38:20,119 INFO [t2022-06-18 17:38:20,213 I2022-06-18 17:38:20,366 INFO [train.py:445] Epoch 17, batch 2600, loss[loss=2.186, over 36400.00 frames., ppl: 8.902508237486924] tot_loss[loss=2.259, over 224932022-06-18 17:39:31,179 INFO 202022-06-18 17:39:31,264 INFO [train.py:445] Epoch 17, batch 2800, loss[loss=2.264, over 21200.00 frames., ppl: 9.618456951016476] tot_loss[loss=2.259, over 23586479.54 frames., ppl: 9.5752022-06-18 17:40:41,816 INFO [t2022-06-18 17:40:41,927 INFO [train.py:445] Epoch 17, batch 3000, loss[loss=2.156, over 47200.00 frames., ppl: 8.63818849516383] tot_loss[loss=2.26, over 24324291.89 frames., ppl: 9.580372022-06-18 17:41:53,148 INFO [train.py:445] Epoch 17, ba2022-06-18 17:41:53,449 INFO [train.py:445] Epoch 17, batch 3200, loss[loss=2.184, over 50000.00 frames., ppl: 8.885232464791248] tot_loss[loss=2.261, over 2467022022-06-18 17:43:07,125 INFO 2022-06-18 17:43:07,534 INFO [train.py:445] Epoch 17, batch 3400, loss[loss=2.19, over 56400.00 frames., ppl: 8.936882929740154] tot_loss[loss=2.259, over 25911634.19 frames., ppl: 9.57180842022-06-18 17:44:21,101 INFO [train.py:445] Epoch 17, batch 3600, loss[loss=2.253, over 20000.00 frames., ppl: 9.51744997472846] tot_loss[loss=2.255, over 26868026.31 frames., ppl: 9.535292672895627], batch size: 400
+2022-06-18 17:45:34,761 INF22022-062022022-06-18 17:45:34,936 INFO [train.py:445] Epoch 17, batch 3800, loss[loss=2.203, over 26400.00 frames., ppl: 9.053855562771233] tot_loss[loss=2.257, over 27206171.83 frames., ppl2022-06-18 17:46:49,583 INFO [train.py:445] Epoch 17, batch 4000, loss[loss=2.22022-06-18 17:46:49,608 INFO [train.py:445] Epoch 17, batch 4000, loss[loss=2.237, over 23200.00 frames., ppl: 9.365653340411585] tot_loss[2022-06-18 17:48:02,949 INF2022-06-122022-06-18 17:48:03,150 INFO [train.py:445] Epoch 17, batch 4200, loss[loss=2.189, over 27200.00 frames., ppl: 8.929502111892607] tot_loss[loss=2.256, over 28365651.88 frames., ppl:2022-06-18 17:49:13,402 I2022-06-18 12022-06-18 17:49:13,562 INFO [train.py:445] Epoch 17, batch 4400, loss[loss=2.251, over 21600.00 frames., ppl: 9.495309005249396] tot_loss[loss=2.256, over 28772373.62 frames., ppl2022-06-18 17:50:25,334 INF2022-06-18 17:50:25,753 INFO [train.py:445] Epoch 17, batch 4600, loss[loss=2.16, over 48800.00 frames., ppl: 8.671220949760563] tot_loss[loss=2.258, over 28890346.27 frames., ppl: 9.56770932022-06-18 17:51:38,154 INFO [train.py:445] Epoch 17, batch 4800, loss[loss=2.217, over 76400.00 frames., ppl: 9.184138428963003] tot_loss[loss=2.258, over 29303360.27 frames., ppl: 9.560432268450342], batch size: 400
+2022-06-18 17:52:50,656 IN22022-06-18 17:52:50,860 INFO [train.py:445] Epoch 17, batch 5000, loss[loss=2.186, over 23200.00 frames., ppl: 8.90089354718511] tot_loss[loss=2.26, over 29185481.04 frames., ppl: 9.58724852022-06-18 17:54:03,373 INF2022-06-182022022-06-18 17:54:02022-06-18 17:54:03,739 INFO [train.py:445] Epoch 17, batch 5200, loss[loss=2.151, over 54400.00 frames., ppl: 8.591762889421652] tot_loss[loss=2.263, over 2892022-06-18 17:55:17,467 2022-06-18 17:55:17,487 INFO [train2022-06-18 17:55:17,802 INFO [train.py:445] Epoch 17, batch 5400, loss[loss=2.164, over 41004.00 frames., ppl: 8.707270241961208] tot_loss[loss=2.264, over 2912022-06-18 17:56:33,710 I2022-06-18 12022-06-18 17:56:33,840 INFO [train.py:445] Ep2022-06-18 17:56:33,880 INFO [train.py:445] Epoch 17, batch 5600, loss[loss=2.166, over 34400.00 frames., ppl: 8.72285888974923] tot_lo2022-06-18 17:57:46,047 2022-06-18 17:57:2022-06-18 17:57:46,172 INFO [train.py:445] Epoch 17, batch 5800, loss[loss=2.233, over 19600.00 frames., ppl: 9.324978944727736] tot_loss[loss=2.257, over 30295882.23 frames., 2022-06-18 17:58:56,859 INFO [train.py:442022-06-18 17:58:52022-06-18 17:58:56,9742022-06-18 17:58:57,006 INFO [train.py:445] Epoch 17, batch 6000, loss[loss=2.173, over 30000.00 frames., ppl: 8.781422508686088] tot_los2022-06-18 18:00:072022-06-18 18:00:07,781 INFO [train.py:445] Epoch 17, batch 6200, loss[loss=2.17, over 28400.00 frames., ppl: 8.756121961720785] tot_loss[loss=2.26, over 30765600.21 frames., ppl: 9.58123768948577], 2022-06-18 18:012022-06-18 18:01:19,968 INFO [train.py:445] Epoch 17, batch 6400, loss[loss=2.156, over 44000.00 frames., ppl: 8.633854552887678] tot_loss[loss=2.26, over 30879785.59 frames., ppl: 9.584588423863904], ba2022-06-18 18:02:312022-06-18 18:02:31,051 INFO [train.p2022-06-18 18:02:31,257 INFO [train.py:445] Epoch 17, batch 6600, loss[loss=2.145, over 32400.00 frames., ppl: 8.540578966326338] tot_loss[loss=2.265, over 2985342022-06-18 12022-06-18 18:03:42,2022-06-18 18:03:42,898 INFO [train.py:442022-06-18 18:03:42,953 INFO [train.py:445] Epoch 17, batch 6800, loss[loss=2.177, over 23200.00 frames., ppl: 8.822993934506282] tot_loss[loss=2022-06-18 1820222022-06-18 18:042022-06-18 18:04:52,189 INFO [train.py:445] Epoch 17, batch 7000, loss[loss=2.211, over 32400.00 frames., ppl: 9.120289465401228] tot_loss[loss=2.262, over 30638905.93 frames., ppl: 9.2022-06-18 18:02022-06-18 18:06:05,591 INFO [train.py:445] Epoch 17, batch 7200, loss[loss=2.189, over 32000.00 frames., ppl: 8.927954022886295] tot_loss[loss=2.261, over 31205478.42 frames., ppl: 9.592862416981509],2022-06-18 18:07:2022-06-18 18:07:20,126 INFO [train.py:442022-06-18 18:07:20,209 INFO [train.py:445] Epoch 17, batch 7400, loss[loss=2.163, over 61124.00 frames., ppl: 8.693283504904672] tot_loss[loss=2.264, over 30652022-06-18 18:08202022-06-18 18:08:31,419 INFO [train.py:445] Epoch 17, batch 7600, loss[loss=2.196, over 20800.00 frames., ppl: 8.986965415469749] tot_loss[loss=2.262, over 31165429.15 frames., ppl: 9.599431690203132022-06-18 18:09:202022-06-18 18:09:45,898 INFO [train.py:42022-06-18 18:09:46,141 INFO [train.py:445] Epoch 17, batch 7800, loss[loss=2.182, over 40000.00 frames., ppl: 8.866378319645387] tot_loss[loss=2.263, over 32022-06-18 18:10:52022022-06-18 18:10:57,782022-06-18 18:10:57,800 INFO [train.py:445] Epoch 17, batch 8000, loss[loss=2.209, over 36800.00 frames., ppl: 9.103310330034581] tot_loss[loss=2.264, over 30524868.12 frames.2022-06-18 18:12:12022022-06-18 18:12:13,724 INFO [train.py:445] Epoch 17, batch 2022-06-18 18:12:13,802 INFO [train.py:445] Epoch 17, batch 8200, loss[loss=2.216, over 36000.00 frames., ppl: 9.174643855875106] tot_lo2022-06-18 18:13:220222022-06-18 18:13:29,028 INFO [train.py:2022-06-18 18:13:29,02022-06-18 18:13:29,242 INFO [train.py:445] Epoch 17, batch 8400, loss[loss=2.156, over 34400.00 frames., ppl: 8.639121264042984] tot_l2022-06-18 18:14:41,382 INFO [train.2022-06-18 18:14:41,502 INFO [train.py:445] Epoch 17, batch 8600, loss[loss=2.231, over 22800.00 frames., ppl: 9.307654280138744] tot_loss[loss=2.262, over 31607359.68 frames., ppl: 2022-06-18 18:15:2022-06-18 18:15:52,127 INFO [train.py:445] Epoch 17, batch 8800, 2022-06-18 18:15:52,480 INFO [train.py:445] Epoch 17, batch 8800, loss[loss=2.229, over 45426.00 frames., ppl: 9.290374472574804] tot_l2022-06-18 18:17:06,658 INFO [train.py:445] Epoch 17, batch 2022-06-18 18:17:06,671 INFO [train.py:445] Epoch 17, batch 9000, loss[loss=2.194, over 30800.00 frames., ppl: 8.967793517318336] tot_loss[loss=2.262, over 312022-06-18 18:182022-2022-06-18 18:18:20,429 INFO [train.py:445] Epoch 17, batch 9200, loss[loss=2.182, over 32400.00 frames., ppl: 8.861583750699461] tot_loss[loss=2.261, over 31775906.57 frames., ppl: 9.5944745015442022-06-18 18:192022-02022-06-18 18:19:30,618 INFO [train.py:445] Epoch 17, batch 9400, loss[loss=2.182, over 30000.00 frames., ppl: 8.861240080393635] tot_loss[loss=2.262, over 31746366.97 frames., ppl: 9.6006586592892022-06-18 18:20:43,708 INFO [trai2022-06-18 18:20:44,088 INFO [train.py:445] Epoch 17, batch 9600, loss[loss=2.199, over 68800.00 frames., ppl: 9.018194845442343] tot_loss[loss=2.263, over 31762218.38 frames., ppl: 92022-06-18 18:21:55,728 INFO [train.py:445] Epoch 17, batch2022-06-18 18:21:55,775 INFO [train.py:445] Epoch 17, batch 9800, loss[loss=2.284, over 15200.00 frames., ppl: 9.818351010002557] tot_loss[loss=2.265, over 3162022-06-18 18:23:06,870 INFO [train.py:445] Epoch 17, batch 10000, loss[loss=2.166, over 53600.00 frames., ppl: 8.722694318207719] tot_loss[loss=2.264, over 31862548.00 frames., ppl: 9.619346675854933], batch size: 400
+2022-06-18 18:23:06,870 INFO [train.py:469] Computing validation loss
+2022-06-18 18:23:07,052 INFO [train.py:480] Epoch 17, validation: loss=2.328, over 211809.00 frames., ppl: 10.254031547969818
+2022-06-18 18:2022-02022-06-18 18:2022-06-18 18:24:20,015 I2022-06-18 18:24:2022-06-18 18:24:20,272 INFO [train.py:445] Epoch 17, batch 10200, loss[loss=2.156, over 51600.00 frames., ppl: 8.636461188459263] tot_loss[lo2022-06-18 18:22022-06-18 18:25:37,262 INFO [train.py:445] 2022-06-18 18:25:37,275 INFO [train.py:445] Epoch 17, batch 10400, loss[loss=2.184, over 25600.00 frames., ppl: 8.880252187150328] tot_loss[loss=2.263, over 322022-06-18 18:26:52,390 INFO [train.py:445] Epoch 17, batch 10600, loss[loss=2.164, over 37600.00 frames., ppl: 8.70455644048381] tot_loss[loss=2.264, over 31784425.50 frames., ppl: 9.624101354485953], batch size: 400
+2022-06-18 18:28:07,127 INFO [train.p2022-06-18 18:28:07,136 INFO [train.py:445] Epoch 17, batch 10800, loss[loss=2.237, over 23200.00 frames., ppl: 9.36790072342722] tot_loss[loss=2.262, over 32432849.08 frames., ppl: 2022-06-18 18:29:2022-06-18 18:29:18,868 INFO 2022-06-18 18:29:18,929 INFO [train.py:445] Epoch 17, batch 11000, loss[loss=2.186, over 37200.00 frames., ppl: 8.895797792199156] tot_loss[loss=2.265, over 31579593.77 fr2022-06-18 18:30:312022-06-18 18:30:32,078 INFO [train.py:445] Epoch 17, batch 11200, loss[loss=2.195, over 52400.00 frames., ppl: 8.975925153732238] tot_loss[loss=2.268, over 31276809.81 frames., ppl: 9.657706111931302022-06-18 18:31:44,555 2022-06-18 18:31:44,647 INFO [train.py:445] Epoch 17,2022-06-18 18:31:44,859 INFO [train.py:445] Epoch 17, batch 11400, loss[loss=2.176, over 61128.00 frames., ppl: 8.814596416225019] tot_loss[l2022-06-18 18:32:57,20222022-06-18 18:32:57,467 2022-06-18 18:32:57,584 INFO [train.py:445] Epoch 17, batch 11600, loss[loss=2.149, over 32000.00 frames., ppl: 8.573522808118655] tot_loss[loss=2.266, over 31616013.96 2022-06-18 18:34:06,843 INFO [train.py:445] Epoch 17, batch 11800, 2022-06-18 18:34:06,953 INFO [train.py:445] Epoch 17, batch 11800, loss[loss=2.17, over 46000.00 frames., ppl: 8.75639811879005] tot_loss[loss=2.265, ov2022-06-18 18:35:18,12022-06-18 18:35:18,2022-06-18 18:35:18,211 2022-06-18 18:35:18,229 INFO [train.py:445] Epoch 17, batch 12000, loss[loss=2.223, over 20800.00 frames., ppl: 9.233620147039797] tot_loss[loss=2.265, ov2022-06-18 18:36:35,202022-06-18 18:36:352022-06-18 18:36:35,308 INFO [train.py:445] Epoch 17, batch 12200, loss[loss=2.141, over 31600.00 frames., ppl: 8.50776084553278] tot_loss[loss=2.265, over 32018637.42 frames., p2022-06-18 18:37:46,12022-06-18 18:37:42022-06-18 18:37:46,248 INFO [train.py:445] Epoch 17, batch 12400, loss[loss=2.239, over 21200.00 frames., ppl: 9.382900433077053] tot_loss[loss=2.265, over 31966125.60 frames., pp2022-06-18 18:39:00,4202022-06-18 18:39:00,569 INF2022-06-18 18:39:00,676 INFO [train.py:445] Epoch 17, batch 12600, loss[loss=2.187, over 26000.00 frames., ppl: 8.90628771404352] tot_loss[loss=2.268, over 31109509.39 f2022-06-18 18:39:28,822022-06-18 18:39:28,952 INFO [train.py:445] Epoch 18, bat2022-06-18 18:39:29,179 INFO [train.py:445] Epoch 18, batch 0, loss[loss=2.219, over 38400.00 frames., ppl: 9.195121720968347] tot_lo2022-06-18 18:40:2022-06-18 18:40:49,191 INFO [train.py:445] Epoch 18, batch 200, loss[loss=2.164, over 51600.00 frames., ppl: 8.702289741825707] tot_loss[loss=2.244, over 3121495.90 frames., ppl: 9.426402006182327]2022-06-18 18:42:02022-06-18 18:42:00,774 INFO [train.py:445] Epoch 18, batch 400, loss[loss=2.216, over 14000.00 frames., ppl: 9.172389439143373] tot_loss[loss=2.25, over 5634885.62 frames., ppl: 9.490309825453496]2022-06-18 18:43:12022-06-18 18:43:15,498 INFO [train.py:445] Epoch 18, batch 600, loss[loss=2.16, over 32400.00 frames., ppl: 8.669214297673738] tot_loss[loss=2.252, over 8108967.63 frames., ppl: 9.502312706195433]2022-06-18 18:44:26,395 INFO [train.py:445] Epoch 18, batch 800, loss[loss=2.2, ov2022-06-18 18:44:26,462 INFO [train.py:445] Epoch 18, batch 800, loss[loss=2.206, over 29200.00 frames., ppl: 9.079982017091647] tot_2022-06-18 18:45:39,2022-06-18 18:45:39,343 INFO [train.py:42022-06-18 18:45:39,606 INFO [train.py:445] Epoch 18, batch 1000, loss[loss=2.186, over 47235.00 frames., ppl: 8.895414484360998] tot_loss[loss=2.258, over 112022-06-18 18:46:52,202022-06-18 18:46:52,877 INFO [2022-06-18 18:46:52,982 INFO [train.py:445] Epoch 18, batch 1200, loss[loss=2.156, over 26000.00 frames., ppl: 8.637919963735515] tot_loss[loss=2.247, over 144145452022-06-18 18:48:04,52022-06-18 18:48:04,676 INFO [train.py:445] Epoch 18, batch 1400, loss[loss=2.244, over 19200.00 frames., ppl: 9.432701508187359] tot_loss[loss=2.252, over 15977275.93 frames., ppl: 9.502606820218992022-06-18 18:49:19,2022-06-18 18:49:19,965 INFO [tra2022-06-18 18:49:20,012 INFO [train.py:445] Epoch 18, batch 1600, loss[loss=2.196, over 30000.00 frames., ppl: 8.99167094167088] tot_loss[loss=2.246, over 18189934.12022-06-18 18:50:34,2022-06-18 18:50:35,265 INFO [train.py:445] Epoch 18, batch 1800, loss[loss=2.162, over 49600.00 frames., ppl: 8.690018400454273] tot_loss[loss=2.252, over 18845828.91 frames., ppl: 9.51052509241522]2022-06-18 18:51:52,514 INFO [train.py:445] Epoch 18, batch 2000, loss[loss=2.19, over 23200.00 frames., ppl: 8.932779250586727] tot_loss[loss=2.255, over 19909445.22 frames., ppl: 9.539231793959207], batch size: 400
+2022-06-18 18:53:05,92022-06-18 18:53:06,045 INFO [train.py:445] Epoch 18, batch 2200, loss[loss=2.198, over 18400.00 frames., ppl: 9.007979324479798] tot_loss[loss=2.253, over 21519659.65 frames., ppl: 9.5192478908812022-06-18 18:54:16,372022-06-18 18:54:16,488 INFO [train.py:445] Epoch 18, batch 2400, loss[loss=2.167, over 39200.00 frames., ppl: 8.731284262270266] tot_loss[loss=2.253, over 22567998.59 frames., ppl: 9.512690651592022-06-18 18:55:30,261 INFO [train.py:445] Epoch 12022-06-18 18:55:30,274 INFO [train.py:445] Epoch 18, batch 2600, loss[loss=2.16, over 33600.00 frames., ppl: 8.667928512138536] tot_loss[loss=2.257, over 22699724.372022-06-18 18:56:40,892022-06-18 18:56:41,027 INFO 2022-06-18 2022-06-18 18:56:41,256 INFO [train.py:445] Epoch 18, batch 2800, loss[loss=2.152, over 46400.00 frames., ppl: 8.604458006593257] tot_loss[loss=2.255, over 2022-06-18 18:57:55,984 I2022-06-18 182022-06-18 18:57:56,092 INFO [train.py:445] Epoch 18, batch 3000, loss[loss=2.213, over 31200.00 frames., ppl: 9.145003204122409] tot_loss[loss=2.253, over 24812391.82 frames., ppl2022-06-18 18:59:04,397 INFO [train.py:445] Epoch 18, batch 3200, loss[loss=2.166, over 34800.00 frames., ppl: 8.721994385004779] tot_loss[loss=2.257, over 25035604.06 frames., ppl: 9.556472129158673], batch size: 400
+2022-06-18 19:00:19,212 INFO [train.py:445] Epoch 18, batch 342022-06-18 19:00:19,296 INFO [train.py:445] Epoch 18, batch 3400, loss[loss=2.292, over 15600.00 frames., ppl: 9.892509400588425] tot_loss[loss=2.253, over 2022-06-18 19:01:32022-2022-06-18 19:01:32,291 INFO [train.py:2022-06-18 19:01:32,344 INFO [train.py:445] Epoch 18, batch 3600, loss[loss=2.205, over 43818.00 frames., ppl: 9.067573405303955] tot_loss[loss=2.252, over 2022-06-18 19:02:42022-06-18 19:02:41,551 INFO [train.py:445] Epoch 18, batch 3800, loss[loss=2.196, over 22800.00 frames., ppl: 8.989427863683927] tot_loss[loss=2.256, over 27197034.27 frames., ppl: 9.54255509782104],2022-06-18 19:03:20222022-06-18 19:03:56,830 INF2022-06-18 19:03:56,934 INFO [train.py:445] Epoch 18, batch 4000, loss[loss=2.179, over 39200.00 frames., ppl: 8.839225138129837] tot_loss[loss=2.257, over 27466895.01 f2022-06-18 19:05:2022-06-18 19:05:10,849 INFO [train.py:445] Epoch 18, batch 4200, loss[loss=2.168, over 35200.00 frames., ppl: 8.742653768408719] tot_loss[loss=2.256, over 28177003.36 frames., ppl: 9.542033396583227]2022-06-18 19:06:22022-06-18 19:06:23,059 INFO [train.py:445] Epoch 18, batch 4400, loss[loss=2.155, over 42800.00 frames., ppl: 8.628570559283721] tot_loss[loss=2.257, over 28368069.84 frames., ppl: 9.551424602416045]2022-06-18 19:07:32,665 INFO [train.py:445] Epoch 18, batch 46002022-06-18 19:07:32,675 INFO [train.py:445] Epoch 18, batch 4600, loss[loss=2.255, over 15600.00 frames., ppl: 9.530953562022887] tot_loss[loss=2.258, ov2022-06-18 19:08:45,124 INFO [train.py:442022-06-18 19:08:45,218 2022-06-18 19:08:45,334 INFO [train.py:445] Epoch 18, batch 4800, loss[loss=2.195, over 39600.00 frames., ppl: 8.979505016168806] tot_loss[loss=2.258, ov2022-06-18 19:09:57,158 INFO [train.py:445] Epoch 18, batch 5000,2022-06-18 19:09:57,178 INFO [train.py:445] Epoch 18, batch 5000, loss[loss=2.239, over 22800.00 frames., ppl: 9.384411795806471] tot_loss[loss=2.259, ov2022-06-18 19:11:2022-06-18 19:11:09,577 2022-06-18 19:11:09,615 INFO [train.py:445] Epoch 18, batch 5200, loss[loss=2.197, over 32400.00 frames., ppl: 8.999347317224881] tot_loss[loss=2.259, over 29076219.25 frames.,2022-06-18 19:12:24,2022-06-18 19:12:25,062022-06-18 19:12:25,124 INFO [train.py:445] Epoch 18, batch 5400, loss[loss=2.192, over 26400.00 frames., ppl: 8.949345522594482] tot_loss[loss=2.259, over 29380899.59 frames.,2022-06-18 19:132022022-06-18 19:13:38,3492022-06-18 19:13:38,405 INFO [train.py:445] 2022-06-18 19:13:38,425 INFO [train.py:445] Epoch 18, batch 5600, loss[loss=2.231, over 24400.00 frames., ppl: 9.309590282230156] to2022-06-18 19:14:49,103 INFO [train.py:445] Epo2022-06-18 19:14:49,120 INFO [train.py:445] Epoch 18, batch 5800, loss[loss=2.203, over 35200.00 frames., ppl: 9.051410371397397] tot_loss[loss=2.255, over 30792557.40 fra2022-06-18 19:12022-06-18 19:16:02,464 I2022-06-18 19:16:02,507 INFO [train.py:445] Epoch 18, batch 6000, loss[loss=2.237, over 22800.00 frames., ppl: 9.363358785488849] tot_loss[loss=2.26, over 30147940.70 frames., pp2022-06-18 19:2022-06-18 19:17:18,758 I2022-06-18 19:17:19,023 INFO [train.py:445] Epoch 18, batch 6200, loss[loss=2.177, over 44400.00 frames., ppl: 8.815850008745192] tot_loss[loss=2.26, over 30355299.67 frames., ppl2022-06-18 19:12022-06-18 19:18:30,2932022-06-18 19:18:30,337 INFO [train.py:445] Ep2022-06-18 19:18:30,396 INFO [train.py:445] Epoch 18, batch 6400, loss[loss=2.219, over 22800.00 frames., ppl: 9.198006479596199] tot2022-06-18 19:122022-06-18 19:19:44,012022-06-18 19:19:44,190 INFO [train.py:445] Ep2022-06-18 19:19:44,252 INFO [train.py:445] Epoch 18, batch 6600, loss[loss=2.205, over 35600.00 frames., ppl: 9.068937941511688] tot2022-06-18 19:22022-06-18 19:20:55,422 2022-06-18 19:20:55,518 INFO [train.py:445] Epoch 18, batch 6800, loss[loss=2.186, over 31200.00 frames., ppl: 8.895534230961433] tot_loss[loss=2.261, over 30526405.37 frames.,2022-06-18 19:22:02022-06-18 19:22:08,224 2022-06-18 19:222022-06-18 19:22:08,444 INFO [train.py:445] Epoch 18, batch 7000, loss[loss=2.175, over 43600.00 frames., ppl: 8.799520013112621] tot_loss[loss=2.26, over 309332022-06-18 19:23:2022-06-18 19:23:25,048 INFO [train.py:445] Epoch 18, batch 7200, los2022-06-18 19:23:25,205 INFO [train.py:445] Epoch 18, batch 7200, loss[loss=2.207, over 36400.00 frames., ppl: 9.087016794998153] 2022-06-18 19:24:38,085 INFO [train.py:445]2022-06-18 19:24:38,170 INFO [train.py:445] Epoch 18, batch 7400, loss[loss=2.174, over 25200.00 frames., ppl: 8.79401055084611] tot_loss[loss=2.26, over 31248749.16 frames.,2022-06-18 19:25:52,745 INFO [train.py:445] Epoch 18, batch 7600, loss[loss=2.231, over 24000.00 frames., ppl: 9.305327418270934] tot_loss[loss=2.26, over 31053963.83 frames., ppl: 9.587156694462742], batch size: 400
+2022-06-18 19:27:06,2022-06-18 19:27:06,297 INFO [train.py:445] Epoch 18, batch 7800, loss[loss=2.218, over 51849.00 frames., ppl: 9.186147203952082] tot_loss[loss=2.261, over 30949177.65 frames., ppl: 9.594088214199202022-06-18 19:28:17,2022-06-18 19:28:17,860 INFO [train.py:445] Epoch 18, batch 8000, loss[loss=2.18, over 46800.00 frames., ppl: 8.846027157908294] tot_loss[loss=2.262, over 31231169.36 frames., ppl: 9.605958202039005]2022-06-18 19:29:222022-06-18 19:29:25,817 INFO [train.py:445] Epoch 18, batch 8200, loss[loss=2.194, over 22800.00 frames., ppl: 8.967639916245153] tot_loss[loss=2.263, over 30740879.54 frames., ppl: 9.611986787128322022-06-18 19:30:39,2022-06-18 19:30:39,2022-06-18 19:30:39,849 INFO [train.py:445] Epoch 18, batch 8400, loss[loss=2.156, over 40800.00 frames., ppl: 8.636640793546505] tot_loss[loss=2.262, over 31018385.68 frames., pp2022-06-18 19:31:54,551 INFO [train.py:445] Epoch 12022-06-18 19:31:54,695 INFO [train.py:445] Epoch 18, batch 8600, loss[loss=2.183, over 39200.00 frames., ppl: 8.871235659908306] tot_loss[loss=2.258, over 31849430.782022-06-18 19:33:02022-06-18 19:33:07,602 INFO [train.p2022-06-18 19:33:07,635 2022-06-18 19:33:07,663 INFO [train.py:445] Epoch 18, batch 8800, loss[loss=2.179, over 36800.00 frames., ppl: 8.837221227141681] tot_loss[2022-06-18 19:34:20,738 INFO [train.py:445] Epoch 18, batch 9000, loss[loss=2.254, over 25200.00 frames., ppl: 9.52254170849409] tot_loss[loss=2.261, over 31538914.38 frames., ppl: 9.593031032039173], batch size: 400
+2022-06-18 19:35:32022-06-18 19:35:342022-06-18 19:32022-06-18 19:35:34,207 INFO [train.py:445] Epoch 18, batch 9200, loss[loss=2.192, over 26000.00 frames., ppl: 8.952068432706483] tot_loss[loss=2.259, over 31767256.2022-06-18 19:36:45,785 INFO [train.py:445] Epoch 18, 2022-06-18 19:36:45,860 I2022-06-18 19:36:45,915 INFO [train.py:445] Epoch 18, batch 9400, loss[loss=2.192, over 34000.00 frames., ppl: 8.949559559554949] tot_loss[2022-06-18 19:38:01,895 INFO [train.py:445] Epoch 18, batch 9600, loss[loss=2.227, over 19200.00 frames., ppl: 9.273290371440577] tot_loss[loss=2.261, over 31818674.03 frames., ppl: 9.58981647793962], batch size: 400
+2022-06-18 19:32022-06-18 19:39:13,550 INFO [train.py:42022-06-18 19:39:13,574 INFO [train.py:445] Epoch 18, batch 9800, loss[loss=2.185, over 30000.00 frames., ppl: 8.893211930118621] tot_loss[loss=2.262, over 3201182022-06-18 19:40:25,733 INFO [train.py:445] Epoch 18, batch 10000, loss[loss=2.204, over 54000.00 frames., ppl: 9.059738692737747] tot_loss[loss=2.26, over 32257478.95 frames., ppl: 9.580647270298023], batch size: 400
+2022-06-18 19:40:25,734 INFO [train.py:469] Computing validation loss
+2022-06-18 19:40:25,918 INFO [train.py:42022-06-18 1202022-06-18 19:40:25,918 INFO [train.py:480] Epoch 18, validation: loss=22022-06-18 19:41:38,603 INFO [train.py:42022-06-18 12022-06-18 19:41:38,962 INFO [train.py:445] Epoch 18, batch 10200, loss[loss=2.212, over 50400.00 frames., ppl: 9.129982155181256] tot_loss[loss=2.261, over 31957952.2022-06-18 19:42:56,912022-06-18 19:42:57,016 INFO [train.py:445] Epoch 18, batch 10400, loss[loss=2.147, over 36400.00 frames., ppl: 8.557494013215484] tot_loss[loss=2.263, over 31354052.09 frames., ppl: 9.616502464802022-06-18 19:442022-06-18 19:44:09,953 INFO [train.py:445] Epoch 18, batch 10600, loss[loss=2.17, over 41607.00 frames., ppl: 8.760739146642502] tot_loss[loss=2.263, over 31499225.57 frames., ppl: 9.614762344758828],2022-06-18 19:45:2022-06-2022-06-18 19:45:23,2022-06-18 19:45:23,997 INFO [train.py:445] Epoch 18, batch 10800, loss[loss=2.201, over 30000.00 frames., ppl: 9.0365583228717] tot_loss[loss=2.265, over 31261207.59 frames2022-06-18 19:46:36,918 INFO [train.py:445] Epoch 18, batc2022-06-18 19:46:36,973 INFO [train.py:445] Epoch 18, batch 11000, loss[loss=2.214, over 25600.00 frames., ppl: 9.150735224974968] tot_loss[loss=2.262, over 322022-06-18 19:47:50,630 INFO [train.py:445] Epoch 18, batch 11200, loss[loss=2.153, over 21600.00 frames., ppl: 8.611779435551595] tot_loss[loss=2.259, over 32703676.83 frames., ppl: 9.570543254134028], batch size: 400
+2022-06-18 19:49:02,293 IN2022-06-18 19:49:02,410 INFO [2022-06-18 19:49:02,412022-06-18 19:49:02,595 INFO [train.py:445] Epoch 18, batch 11400, loss[loss=2.212, over 36400.00 frames., ppl: 9.129986509760169] tot_loss[2022-06-18 19:50:12022-06-12022-06-18 19:50:12,214 INFO [train.py:445] Epoch 18, batch 11600, loss[loss=2.139, over 42000.00 frames., ppl: 8.494133460106116] tot_loss[loss=2.263, over 31626989.97 frames., ppl: 9.61082902022-06-18 19:51:2022-06-18 19:51:27,479 INFO [train.py:445] Epoch 18, batch 11800, loss[loss=2.236, over 23600.00 frames., ppl: 9.35859823207472] tot_loss[loss=2.262, over 32268796.77 frames., ppl: 9.597844346034414],2022-06-18 19:52:40,230 INFO [train.py:445] Epoch 18, bat2022-06-18 19:52:40,242022-06-18 19:52:40,467 INFO [train.py:445] Epoch 18, batch 12000, loss[loss=2.155, over 44800.00 frames., ppl: 8.624102466498108] tot_loss[2022-06-18 19:53:2022-06-18 19:53:50,459 INFO [train.py:445] Epoch 18, batch 12200, loss[loss=2.204, over 26800.00 frames., ppl: 9.062231655200101] tot_loss[loss=2.264, over 31843653.50 frames., ppl: 9.623128479341707]2022-06-18 19:55:04,731 INFO2022-06-18 19:55:04,2022-06-18 19:55:05,162 INFO [train.py:445] Epoch 18, batch 12400, loss[loss=2.166, over 54800.00 frames., ppl: 8.721706297285541] tot_loss[loss=2.264, over 31915014.41 fr2022-06-18 19:56:16,985 INFO [train.py:445] Epoch 18, batch 12600, loss[loss=2.185, over 30000.00 frames., ppl: 8.892153608238225] tot_loss[loss=2.261, over 32360089.42 frames., ppl: 9.59175668950515], batch size: 400
+2022-06-18 19:56:42022-06-18 19:56:43,598 INFO [t2022-06-18 12022-06-18 19:56:43,2022-06-18 19:56:43,774 INFO [train.py:445] Epoch 19, batch 0, loss[loss=2.151, over 30800.00 frames., ppl: 8.596468342253818] to2022-06-18 19:58:04,938 INFO [t2022-06-18 19:58:05,223 INFO [train.py:445] Epoch 19, batch 200, loss[loss=2.185, over 38400.00 frames., ppl: 8.892077625804975] tot_loss[loss=2.227, over 3446436.67 frames., ppl: 9.2692022-06-18 19:59:20,108 INFO [train.py:445] Epoch 12022-06-182022-06-18 19:59:20,418 INFO [train.py:445] Epoch 19, batch 400, loss[loss=2.208, over 45627.00 frames., ppl: 9.096399991980102] tot_loss[loss=2.248, ove2022-06-18 20:00:33,086 INFO [tr2022-06-18 20:00:33,294 INFO [train.py:445] Epoch 19, batch 600, loss[loss=2.193, over 28400.00 frames., ppl: 8.958482239831742] tot_loss[loss=2.249, over 8034298.26 frames., ppl: 9.472022-06-18 20:01:43,299 INFO [train.py:445] Epoch 19, batch 802022-06-18 20:01:43,352 INFO [train.py:445] Epoch 19, batch 800, loss[loss=2.18, over 18800.00 frames., ppl: 8.842770501652888] tot_loss[loss=2.251, over 2022-06-18 20:02:54,639 INFO [tr2022-06-18 20:02:54,929 INFO [train.py:445] Epoch 19, batch 1000, loss[loss=2.152, over 48000.00 frames., ppl: 8.60066493444382] tot_loss[loss=2.25, over 12214830.13 frames., ppl: 9.49232022-06-18 20:04:06,441 INFO [train.py:445] Epoch 19, batch 1202022-06-18 20:04:06,692 INFO [train.py:445] Epoch 19, batch 1200, loss[loss=2.144, over 36000.00 frames., ppl: 8.531135228593572] tot_loss[loss=2.248, over2022-06-18 20:05:192022-06-18 20:05:19,605 INFO [2022-06-18 20:02022-06-18 20:05:12022-06-18 20:05:19,741 INFO [train.py:445] Epoch 19, batch 1400, loss[loss=2.213, over 27200.00 frames., ppl: 9.145107766264783] tot_lo2022-06-18 20:06:34,2022-06-18 20:06:34,813 INFO [train.py:445] Epoch 19, batch 162022-06-18 20:06:34,856 INFO [train.py:445] Epoch 19, batch 1600, loss[loss=2.17, over 29200.00 frames., ppl: 8.75687817905027] tot_loss2022-06-18 20:07:42022-06-18 20:07:46,882 INFO [tr2022-06-18 20:07:47,035 INFO [train.py:445] Epoch 19, batch 1800, loss[loss=2.204, over 30000.00 frames., ppl: 9.062143932179659] tot_loss[loss=2.25, over 18835064.95 f2022-06-18 20:09:022022-06-18 20:09:02,186 INFO [train.py:445] Epoch 19, batch 2000, loss[loss=2.147, over 34800.00 frames., ppl: 8.558952572867385] tot_loss[loss=2.247, over 20434438.95 frames., ppl: 9.464005005789252022-06-18 20:10:15,236 I2022-06-18 20:10:15,271 I2022-06-18 20:10:15,318 INFO 2022-06-18 20:10:15,353 INFO [train.py:445] Epoch 19, batch 2200, loss[loss=2.234, over 23200.00 frames., ppl: 9.335750705011929] tot_loss[2022-06-18 20:11:27,004 INFO [train.py:445] Epoc2022-06-18 20:11:27,280 INFO [train.py:445] Epoch 19, batch 2400, loss[loss=2.154, over 38000.00 frames., ppl: 8.62051776163225] tot_loss[loss=2.246, over 23037707.73 fra2022-06-18 20:12:38,905 INFO [train.py:445] Epoch 19, batch 2600, loss[loss=2.22022-06-18 20:12:39,149 INFO [train.py:445] Epoch 19, batch 2600, loss[loss=2.13, over 58800.00 frames., ppl: 8.412378407449893] tot_loss[2022-06-18 20:13:51,653 INFO [train.py:445] Epoch 19, batch 2802022-06-18 20:13:51,788 INFO [train.py:445] Epoch 19, batch 2800, loss[loss=2.183, over 27200.00 frames., ppl: 8.869266118182413] tot_loss[loss=2.252, ove2022-06-18 20:15:07,2022-06-2022-06-18 20:15:07,966 INFO [train.py:445] Epoch 19, batch 3000, loss[loss=2.179, over 31200.00 frames., ppl: 8.836171246056589] tot_loss[loss=2.254, over 24564269.83 frames., ppl: 9.528642022-06-18 20:16:18,42022-06-18 20:16:18,767 INFO [train.py:445] Epoch 19, batch 3200, loss[loss=2.166, over 34400.00 frames., ppl: 8.726584016039716] tot_loss[loss=2.248, over 26035415.54 frames., ppl: 9.473384894382022-06-18 20:17:36,088 INFO [2022-06-18 20:17:36,770 INFO [train.py:445] Epoch 19, batch 3400, loss[loss=2.184, over 55821.00 frames., ppl: 8.877524291965285] tot_loss[loss=2.254, over 26247006.69 frames., ppl: 9.521132022-06-18 20:18:46,459 INFO [train.py:445] Epoch 19, batch 3600, loss[loss=2.224, over 16800.00 frames., ppl: 9.246282142412234] tot_loss[loss=2.252, over 26696913.55 frames., ppl: 9.511163474938138], batch size: 400
+2022-06-18 20:19:59,142 INFO [train.py:445] E2022-06-18 20:19:59,437 INFO [train.py:445] Epoch 19, batch 3800, loss[loss=2.171, over 60000.00 frames., ppl: 8.767290998890736] tot_loss[loss=2.253, over 27197581.84 frame2022-06-18 20:21:13,212022-06-18 20:21:13,244 INFO [train.py:445] Epoch 19, batch 4000, loss[loss=2.182, over 26800.00 frames., ppl: 8.868405742945201] tot_loss[loss=2.252, over 27764573.54 frames., ppl: 9.506018072982022-06-18 20:22:26,7312022-06-18 20:22:26,790 INFO [train.py:445] Epoch 19, batch 4200, loss[loss=2.191, over 22800.00 frames., ppl: 8.94414390915251] tot_loss[loss=2.253, over 28031001.10 frames., ppl: 9.51868895116312022-06-18 20:23:39,02022-06-18 20:23:39,180 2022-06-18 20:23:39,192 INFO [train.py:445] Epoch 19, batch 4400, loss[loss=2.182, over 27600.00 frames., ppl: 8.867463192217498] tot_loss[loss=2.257, over 28112011.66 fram2022-06-18 20:24:52022-06-18 20:24:51,827 INFO [train.py:445] Epoch 19, batch 4600, loss[loss=2.151, over 42800.00 frames., ppl: 8.59136093025018] tot_loss[loss=2.253, over 28835623.62 frames., ppl: 9.520345743045354],2022-06-18 20:26:2022-06-18 20:26:04,534 INFO [train.py:42022-06-18 20:26:04,610 INFO [train.py:445] Epoch 19, batch 4800, loss[loss=2.154, over 54000.00 frames., ppl: 8.621793394360122] tot_loss[loss=2.257, over 29092022-06-18 20:27:12022-06-18 20:27:16,566 INFO [train.py:445] Epoch 19, batch 5000, loss[loss=2.244, over 24800.00 frames., ppl: 9.435373133190424] tot_loss[loss=2.253, over 29655021.20 frames., ppl: 9.517254103796054]2022-06-18 20:28:31,305 INFO [train.py:445] Epoch 19, batch 5200, loss[loss=2.17, over 40800.00 frames., ppl: 8.754960736251387] tot_loss[loss=2.256, over 29571296.15 frames., ppl: 9.540195233575956], batch size: 400
+2022-06-18 20:29:432022-06-18 20:29:43,689 INFO [train.py:2022-06-18 20:29:43,781 INFO [train.py:445] Epoch 19, batch 5400, loss[loss=2.219, over 22800.00 frames., ppl: 9.201187124155076] tot_loss[loss=2.256, over 3002022-06-18 20:30:52022-06-18 20:30:56,442 INFO [train.py:445] Epo2022-06-18 20:30:56,480 INFO [train.py:445] Epoch 19, batch 5600, loss[loss=2.24, over 18000.00 frames., ppl: 9.395384264491993] tot_loss[loss=2.258, ove2022-06-18 20:32:09,414 INFO2022-06-18 20:32:09,460 INFO [t2022-06-18 20:32:09,512 INFO [train.py:445] Epoch 19, batch 5800, loss[loss=2.201, over 21200.00 frames., ppl: 9.034616994549998] tot_loss[loss=2.257, over 30492022-06-18 20:32022-06-18 22022-06-18 20:33:21,2022-06-18 20:33:22,051 INFO [train.py:445] Epoch 19, batch 6000, loss[loss=2.146, over 48800.00 frames., ppl: 8.55327864977909] tot_loss[loss=2.256, over 30500745.76 fram2022-06-18 20:32022-06-18 20:34:34,479 INFO [t2022-06-18 20:34:34,580 INFO [train.py:445] Epoch 19, batch 6200, loss[loss=2.177, over 33600.00 frames., ppl: 8.817089071273267] tot_loss[loss=2.257, over 30530003.35 fram2022-06-18 20:2022-06-18 202022-06-18 20:35:51,440 INFO [train.py:445] Epoch 19, batch 6400, loss[loss=2.185, over 30400.00 frames., ppl: 8.894542720395194] tot_loss[loss=2.257, over 30719739.82 frames., ppl: 9.55142612022-06-18 20:37:04,470 INFO [train.py:445] Epoch 19, batch 6600, loss[loss=2.196, over 27600.00 frames., ppl: 8.99071644176175] tot_loss[loss=2.256, over 31021395.89 frames., ppl: 9.54640672307041], batch size: 400
+2022-06-18 202022-06-18 20:38:17,990 INFO [train.py:445] Epoch 19, batch 6800, loss[loss=2.166, over 40400.00 frames., ppl: 8.719733232083774] tot_loss[loss=2.259, over 30367511.46 frames., ppl: 9.574914020201046], batc2022-06-18 22022-06-18 20:39:30,442 INFO [train.py:445] Epoch 19, batch 7000, loss[loss=2.148, over 36000.00 frames., ppl: 8.567123894030205] tot_loss[loss=2.259, over 30463005.55 frames., ppl: 9.576239760798261], batc2022-06-18 20:40:42,807 INF2022-06-18 20:40:42,892 INFO [train.py:445] Epoch 19, batch 7200, loss[loss=2.201, over 26400.00 frames., ppl: 9.030361934057785] tot_loss[loss=2.258, over 31219093.26 frames., ppl: 9.5607322022-06-18 22022-06-18 20:41:58,469 INFO [train.p2022-06-2022-06-18 20:41:58,600 INFO [train.py:445] Epoch 19, batch 7400, loss[loss=2.196, over 27200.00 frames., ppl: 8.989043383106536] tot_loss[loss=2.257, over 315192022-062022-06-18 20:43:12,42022-06-18 20:43:12,600 INFO [train.py:442022-06-18 20:43:12,601 INFO [train.py:445] Epoch 19, batch 7600, loss[loss=2.215, over 20000.00 frames., ppl: 9.160809703083341] tot_loss[loss=2.2552022-06-18 20:44:25,439 INFO2022-06-18 20:44:25,588 INFO2022-06-18 202022-06-18 20:44:25,878 INFO [train.py:445] Epoch 19, batch 7800, loss[loss=2.162, over 58800.00 frames., ppl: 8.686568725483365] tot_loss[loss=2.256,2022-06-18 20:45:36,856 IN2022-06-18 20:45:36,941 INFO2022-06-18 20:45:36,991 INFO [train.py:445] Epoch 19, batch 8000, loss[loss=2.216, over 22400.00 frames., ppl: 9.168048041722273] tot_loss[loss=2.258, over 317698992022-06-18 20:46:49,806 INFO [train.py:445] Epoch 19, batch 8200, loss[loss=2.162, over 31600.00 frames., ppl: 8.69242784833078] tot_loss[loss=2.259, over 31706261.53 frames., ppl: 9.571614071620724], batch size: 400
+2022-062022-06-18 20:47:59,971 INFO [train.py:445] Epoch 19, batch 842022-06-18 20:48:00,013 INFO [train.py:445] Epoch 19, batch 8400, loss[loss=2.134, over 23200.00 frames., ppl: 8.451859271987733] tot_loss[loss=2.2572022-2022-06-18 20:49:15,426 INFO [train.p2022-06-18 20:49:15,574 INFO [train.py:445] Epoch 19, batch 8600, loss[loss=2.164, over 31200.00 frames., ppl: 8.706919801026736] tot_loss[loss=2.262, over 30819881.24 frames2022-02022-06-18 20:50:25,733 INFO [train.py:445] Epoch 19, batch 8800, loss[loss=2.249, over 16800.00 frames., ppl: 9.482769135254088] tot_loss[loss=2.26, over 31470812.04 frames., ppl: 9.584539862396197], batch size2022-06-18 20:51:35,114 I2022-06-18 20:51:35,2022-06-18 20:51:35,252 INFO [train.py:445] Epoch 19, batch 9000, loss[loss=2.195, over 30000.00 frames., ppl: 8.978916038912821] tot_loss[loss=2.262, over 30976556.36 fram2022-06-18 20:52:49,091 INFO [train.py:445] Ep2022-06-18 22022-06-18 20:52:49,288 INFO [train.py:445] Epoch 19, batch 9200, loss[loss=2.17, over 35600.00 frames., ppl: 8.76068497913717] tot_loss[loss=2.259, over 320532022-2022-06-18 20:54:00,112022-06-18 20:54:00,2022-06-182022-06-18 20:54:00,389 INFO [train.py:445] Epoch 19, batch 9400, loss[loss=2.172, over 36000.00 frames., ppl: 8.780060426657334] tot_loss[loss=2.258, over 321502022-06-18 20:55:13,519 INF2022-06-18 20:55:13,556 INFO [train.py:445] Epoch 19, batch 9600, loss[loss=2.179, over 36400.00 frames., ppl: 8.841048492235537] tot_loss[loss=2.264, over 31028107.11 frames., ppl: 9.62120392022-06-18 20:56:22,223 IN2022-06-18 20:56:22,422 INFO [train.py:445] Epoch 19, batch 9800, loss[loss=2.166, over 47600.00 frames., ppl: 8.721689130020811] tot_loss[loss=2.264, over 31012843.45 frames., ppl: 9.62232352022-06-18 20:57:36,405 INFO [train.py:445] Epoch 19, ba2022-06-18 20:57:2022-06-18 20:57:36,566 INFO [train.py:445] Epoch 19, batch 10000, loss[loss=2.173, over 37600.00 frames., ppl: 8.78759519804053] tot_loss[loss=2.262, over 31266781.47 frames., ppl: 9.598701243122463], batch size: 2022-06-18 20:57:36,754 INFO 2022-06-18 20:57:362022-06-12022-06-18 20:57:36,754 INFO [train.py:480] Epoch 19, validation: lo2022-02022-06-18 20:58:52,028 INFO [train.py:445] Epoch 19, ba2022-06-18 20:58:52,132 INFO [train.py:445] Epoch 19, batch 10200, loss[loss=2.163, over 56400.00 frames., ppl: 8.693344758490158] tot_loss[loss=2.26, over 2022-062022-06-18 21:00:04,6002022-06-18 21:00:04,658 INFO [train.py:445] Epoch 19, batch 10400, loss[loss=2.165, over 34800.00 frames., ppl: 8.718419695297449] tot_loss[loss=2.266, over 30900094.51 frames., ppl: 9.63702022-06-18 21:01:15,760 INFO [train.py:445] Epoch 19, batch 10600, loss[loss=2.189, over 38400.00 frames., ppl: 8.92930334445482] tot_loss[loss=2.262, over 31747754.16 frames., ppl: 9.59889395404481], batch size: 400
+2022-06-2022-06-18 21:02:27,540 INFO [train.py:445] Epoch 19, batch 10800, loss[loss=2.174, over 71600.00 frames., ppl: 8.789969203440478] tot_loss[loss=2.261, over 31845745.13 frames., ppl: 9.59296651537226], batch siz2022-06-18 21:03:39,906 INFO [train.py:445] Epoch 19, batch2022-06-18 21:03:39,929 INFO [train.py:445] Epoch 19, batch 11000, loss[loss=2.289, over 12800.00 frames., ppl: 9.864784086780611] tot_loss[loss=2.26, over 320252022-02022-06-18 21:04:54,747 INFO [train.py:445] Epoch 19, batch 11200, loss[loss=2.157, over 36800.00 frames., ppl: 8.642482584548196] tot_loss[loss=2.261, over 31944565.48 frames., ppl: 9.595087607473873], batch size:2022-2022-06-18 21:06:11,210 INFO [train.py:445] Epoch 2022-06-18 21:06:11,228 INFO [train.py:445] Epoch 19, batch 11400, loss[loss=2.188, over 29600.00 frames., ppl: 8.92069529540588] tot_loss[loss=2.259, over 3224802022-06-18 21:07:23,254 INFO [train.py:445] Epoch 19, batch 11600, loss[loss=2.151, over 58000.00 frames., ppl: 8.59571659318031] tot_loss[loss=2.261, over 32022463.32 frames., ppl: 9.59460801981558], batch size: 400
+2022-06-2022-06-18 21:08:35,213 INFO [train.py:445] Epoch 19, batch 11800, loss[loss=2.221, over 21200.00 frames., ppl: 9.219474044357597] tot_loss[loss=2.262, over 31786971.00 frames., ppl: 9.603403788309524], batch 2022-06-18 21:09:45,810 INFO [t2022-06-18 21:09:45,820 INFO 2022-06-18 21:09:45,879 INFO [train.py:445] Epoch 19, batch 12000, loss[loss=2.198, over 19600.00 frames., ppl: 9.006847239629105] tot_loss[loss=2.259, over 3242022-06-18 21:10:56,900 INFO [train.py:445] Epoch 12022-06-18 21:10:56,970 INFO [train.py:445] Epoch 19, batch 12200, loss[loss=2.155, over 32000.00 frames., ppl: 8.630470927528062] tot_loss[loss=2.263, over 31381270.82022-06-18 21:12:08,011 INFO [t2022-06-18 21:12:08,053 INFO [train.py:445] Epoch 19, batch 12400, loss[loss=2.169, over 36000.00 frames., ppl: 8.747445533775913] tot_loss[loss=2.265, over 31356401.80 frames., ppl: 9.62022-06-18 22022-06-18 21:13:20,513 INFO [train.py:4452022-06-18 21:13:20,667 INFO [train.py:445] Epoch 19, batch 12600, loss[loss=2.203, over 28400.00 frames., ppl: 9.052932047413847] tot_loss[loss=2.263, over 316230852022-06-18 21:13:50,765 INFO [train.py:445] Epoch 20, batch 0, 2022-06-18 21:13:50,834 INFO [train.py:445] Epoch 20, batch 0, loss[loss=2.172, over 24800.00 frames., ppl: 8.779399525304935] tot_loss[loss=2.172, o2022-06-18 21:15:08,389 INFO [tr2022-06-18 21:15:08,628 INFO [train.py:445] Epoch 20, batch 200, loss[loss=2.114, over 36800.00 frames., ppl: 8.278980675044552] tot_loss[loss=2.251, over 2984008.70 frames., ppl: 9.42022-06-18 21:16:19,342 INFO [tra2022-06-18 21:16:19,512 INFO2022-06-18 21:16:19,532 INFO [train.py:445] Epoch 20, batch 400, loss[loss=2.195, over 34800.00 frames., ppl: 8.98064619501061] tot_loss[loss=2.24, over 582022-06-18 21:17:32,885 INFO [train.py:445] Epoch 202022-06-18 21:17:32,895 INFO [train.py:445] Epoch 20, batch 600, loss[loss=2.205, over 20400.00 frames., ppl: 9.069016788848511] tot_loss[loss=2.248, over 8051842.2022-06-18 21:18:45,803 INFO [train2022-06-18 21:18:46,149 INFO [train.py:445] Epoch 20, batch 800, loss[loss=2.163, over 58400.00 frames., ppl: 8.692858902998909] tot_loss[loss=2.245, over 11083084.81 frames., ppl: 2022-06-18 21:2022-06-18 21:19:57,452022-06-18 21:19:57,782 INFO [train.py:445] Epoch 20, batch 1000, loss[loss=2.148, over 43200.00 frames., ppl: 8.563450932296472] tot_loss[loss=2.244, over 13097887.34 frames., ppl: 2022-06-18 21:2022-06-18 21:21:08,996 INFO [train.py:445] Epo2022-06-18 21:21:09,095 INFO [train.py:445] Epoch 20, batch 1200, loss[loss=2.19, over 20000.00 frames., ppl: 8.936267252797329] tot_loss[loss=2.251, over 12022-06-18 21:22:23,169 INFO [train.p2022-06-18 21:22:2022-062022-06-18 21:22:23,474 INFO [train.py:445] Epoch 20, batch 1400, loss[loss=2.168, over 38800.00 frames., ppl: 8.741840679760022] tot_loss[loss=2.245, over 12022-06-18 21:23:35,811 INFO [train.py2022-06-18 21:23:35,901 INFO [train.py:445] Epoch 20, batch 1600, loss[loss=2.162, over 35200.00 frames., ppl: 8.692611104571537] tot_loss[loss=2.25, over 17729012.36 frames., ppl2022-06-18 21:2022-06-18 21:24:48,514 INFO [train.py:445] Epoch 20, batch 1800, loss[loss=2.192, over 48641.00 frames., ppl: 8.95585244287278] tot_loss[loss=2.25, over 18917965.96 frames., ppl: 9.483270290470891], batc2022-06-18 22022-06-18 21:25:59,982 INFO [train.py:445] Epoc2022-06-18 21:26:00,077 INFO [train.py:445] Epoch 20, batch 2000, loss[loss=2.202, over 22800.00 frames., ppl: 9.041766929574484] tot_loss[loss=2.251, over 192022-06-18 22022-06-18 21:27:11,304 INFO [train.py:4452022-06-18 21:27:11,435 INFO [train.py:445] Epoch 20, batch 2200, loss[loss=2.153, over 40400.00 frames., ppl: 8.60925372437178] tot_loss[loss=2.248, over 215304602022-06-18 22022-06-18 21:28:25,566 2022-06-18 21:28:25,57222022-06-18 2022-06-18 21:28:25,678 INFO [train.py:445] Epoch 20, batch 2400, loss[loss=2.206, over 22000.00 frames., ppl: 9.082447910983547] tot_loss[loss=22022-06-18 21:22022-06-18 21:29:36,184 INFO [train.py:445] Ep2022-06-18 21:29:36,497 INFO [train.py:445] Epoch 20, batch 2600, loss[loss=2.147, over 55878.00 frames., ppl: 8.561758229662463] tot_loss[loss=2.253, over2022-06-18 21:30:48,621 INFO [train.py:445] Epoch 20, batch 2802022-06-18 21:30:48,813 INFO [train.py:445] Epoch 20, batch 2800, loss[loss=2.188, over 40800.00 frames., ppl: 8.916028438181588] tot_loss[loss=2.252, ove2022-06-18 21:31:58,854 INFO [train.py:445] Epoch 20, batch 3002022-06-18 21:31:59,245 INFO [train.py:445] Epoch 20, batch 3000, loss[loss=2.192, over 54800.00 frames., ppl: 8.953312285182633] tot_loss[loss=2.254, over 2022-06-18 21:33:2022-06-18 21:33:15,6692022-06-18 21:33:16,002 INFO [train.py:445] Epoch 20, batch 3200, loss[loss=2.162, over 71600.00 frames., ppl: 8.68749091819548] tot_loss[loss=2.254, over 25247154.34 frames., pp2022-06-18 21:34:30,015 INFO [train.py:445] Epoch 20, batch 3400, loss[loss=2.159, over 62800.00 frames., ppl: 8.6634476794552] tot_loss[loss=2.255, over 25406969.66 frames., ppl: 9.531041050703555], batch size: 400
+2022-06-18 21:35:42,389 INFO [train.py:445] Epoch 22022-06-18 21:35:42,511 INFO [train.py:445] Epoch 20, batch 3600, loss[loss=2.198, over 38800.00 frames., ppl: 9.006669216755903] tot_loss[loss=2.252, over 26763764.72022-06-18 21:36:532022-06-18 21:36:53,661 INFO [tra2022-06-18 21:36:53,721 INFO [train.py:445] Epoch 20, batch 3800, loss[loss=2.218, over 27600.00 frames., ppl: 9.18879701284815] tot_loss[loss=2.252, over 27232313.82 2022-06-18 21:38:07,126 INFO [train.py2022-06-18 21:38:07,133 I2022-06-18 21:38:07,220 INFO [train.py:445] Epoch 20, batch 4000, loss[loss=2.215, over 22800.00 frames., ppl: 9.159525791826846] tot_loss[loss=2.256, ove2022-06-18 21:39:12022-06-18 21:39:19,2022-06-18 21:39:19,72022-06-18 21:39:19,777 INFO [train.py:445] Epoch 20, batch 4200, loss[loss=2.262, over 18000.00 frames., ppl: 9.598626436258764] tot_loss[loss=2.257, over 22022-06-18 21:40:34,2022-06-18 21:40:34,239 INFO [tr2022-06-18 21:40:34,285 INFO [train.py:445] Epoch 20, batch 4400, loss[loss=2.163, over 58000.00 frames., ppl: 8.700705330343204] tot_loss[loss=2.253, over 28610274.2022-06-18 21:41:48,258 INFO [train.py:445] Epoch 20,2022-06-18 21:41:48,669 INFO [train.py:445] Epoch 20, batch 4600, loss[loss=2.16, over 68000.00 frames., ppl: 8.667332916015651] tot_loss[loss=2.253, over 28818567.02022-06-18 21:43:01,148 INFO [train.py:445]2022-06-18 21:43:01,195 INFO [train.py:445] Epoch 20, batch 4800, loss[loss=2.191, over 23200.00 frames., ppl: 8.94614358638138] tot_loss[loss=2.253, over 29326622.15 frames.,2022-06-18 21:44:10,006 INFO [train.py:445] Epoch 20, batch 502022-2022-06-18 21:44:10,112 INFO [train.py:445] Epoch 20, batch 5000, loss[loss=2.227, over 19600.00 frames., ppl: 9.273255333614161] tot_loss[loss=2.257,2022-06-18 21:45:24,639 INFO [train.py:4452022-06-182022-06-182022-06-18 21:45:25,193 INFO [train.py:445] Epoch 20, batch 5200, loss[loss=2.188, over 79200.00 frames., ppl: 8.919527520555222] tot_loss[loss=2.256, over 2022-06-18 21:46:38,62022-06-18 21:46:38,741 INF2022-06-18 21:2022-06-18 21:46:38,869 INFO [train.py:445] Epoch 20, batch 5400, loss[loss=2.192, over 30800.00 frames., ppl: 8.951351124981006] tot_loss[loss=2.257, over2022-06-18 21:47:50,42022-06-18 21:47:50,612022-06-18 21:47:50,864 INFO [train.py:445] Epoch 20, batch 5600, loss[loss=2.176, over 50400.00 frames., ppl: 8.809086364133066] tot_loss[loss=2.253, over 30563510.92 frames2022-06-18 21:49:05,732022-06-18 21:49:06,246 INFO [train.py:445] Epoch 20, batch 5800, loss[loss=2.157, over 63600.00 frames., ppl: 8.646079105115877] tot_loss[loss=2.254, over 30254979.99 frames., ppl: 9.5234306117652022-06-18 21:50:17,972022-06-18 21:50:18,109 IN2022-06-18 21:502022-06-18 21:50:18,196 INFO [train.py:445] Epoch 20, batch 6000, loss[loss=2.21, over 25600.00 frames., ppl: 9.117480285657834] tot_loss[loss=2.255, over2022-06-18 21:51:29,042 INFO [train.py:445] Epoch 20, batch 6200, loss[loss=2.175, over 74400.00 frames., ppl: 8.801331120959242] tot_loss[loss=2.258, over 30175544.24 frames., ppl: 9.559827244646508], batch size: 400
+2022-06-18 21:52:41,82022-06-18 21:52:42,037 INFO [train.py:445] Epoch 20, batch 6400, loss[loss=2.162, over 41200.00 frames., ppl: 8.685989755387595] tot_loss[loss=2.256, over 30476306.67 frames., ppl: 9.54051178748252022-06-18 21:53:54,534 INFO [train.py:445] E2022-06-18 21:53:54,828 INFO [train.py:445] Epoch 20, batch 6600, loss[loss=2.177, over 42000.00 frames., ppl: 8.819718517176806] tot_loss[loss=2.254, over 31366474.75 frames2022-06-18 21:55:042022-06-18 21:55:04,952 IN2022-06-18 21:55:04,971 INFO [train.py:445] Epoch 20, batch 6800, loss[loss=2.21, over 25600.00 frames., ppl: 9.119873493079663] tot_loss[loss=2.257, over 30701398.29 frame2022-06-18 21:56:16,685 INFO [train.py:4452022-06-18 21:56:16,928 INFO [train.py:445] Epoch 20, batch 7000, loss[loss=2.152, over 40000.00 frames., ppl: 8.597950218161845] tot_loss[loss=2.256, over 30936582.25 frames.,2022-06-18 21:57:30,594 INFO [train.py:445] Epoch 20, batch 7200,2022-06-18 21:57:31,030 INFO [train.py:445] Epoch 20, batch 7200, loss[loss=2.178, over 73200.00 frames., ppl: 8.829122448380165] tot_loss[loss=2.257, ov2022-06-18 21:58:45,564 INFO [train.py:445] Epoch 20, batch 7400, loss[l2022-06-18 21:58:45,603 INFO [train.py:445] Epoch 20, batch 7400, loss[loss=2.179, over 24800.00 frames., ppl: 8.836861384764086] tot_loss[loss=2.2022-06-18 21:59:592022-06-18 21:59:59,284 INF2022-06-18 21:59:59,329 INFO [train.py:445] Epoch 20, batch 7600, loss[loss=2.167, over 43600.00 frames., ppl: 8.729765083961409] tot_loss[loss=2.257, over 31083469.89 fram2022-06-18 22:01:10,483 INFO [train.py:445] Epoch 20, batch 2022-06-18 22:01:10,898 INFO [train.py:445] Epoch 20, batch 7800, loss[loss=2.183, over 72400.00 frames., ppl: 8.874017187377625] tot_loss[loss=2.256, over 32022-06-18 22:02:26,717 INFO [train.py:445] Epoch 20, batch 8000, loss[loss=2.216, over 23200.00 frames., ppl: 9.172982324223916] tot_loss[loss=2.257, over 31636837.51 frames., ppl: 9.55103342901432], batch size: 400
+2022-06-18 22:03:39,32022-06-18 22:03:39,442 INFO [train.py:445] Epoch 20, batch 8200, loss[loss=2.245, over 21600.00 frames., ppl: 9.43704263102438] tot_loss[loss=2.257, over 31425363.65 frames., ppl: 9.55815202183222022-06-18 22:04:56,12022-06-18 22:04:56,187 INFO [train.py:2022-062022-06-18 22:04:56,334 INFO [train.py:445] Epoch 20, batch 8400, loss[loss=2.135, over 23200.00 frames., ppl: 8.457251556272332] tot_loss[loss=2.257,2022-06-18 22:06:09,852022-06-18 22:06:09,937 INFO [train.py:445] Epoch2022-06-18 22:06:10,089 INFO [train.py:445] Epoch 20, batch 8600, loss[loss=2.184, over 36000.00 frames., ppl: 8.879457398715275] tot_loss[loss=2.22022-06-18 22:07:27,414 INFO [train.py:4452022-06-18 22:07:27,552 INFO [train.py:445] Epoch 20, batch 8800, loss[loss=2.194, over 25200.00 frames., ppl: 8.973797712346412] tot_loss[loss=2.255, over 32143799.39 frames.,2022-06-18 22:08:43,309 INFO [train.py:445] Epoch 20, batch 9000, loss[loss=2.157, over 62000.00 frames., ppl: 8.648168204414459] tot_loss[loss=2.258, over 31868049.94 frames., ppl: 9.56303846427237], batch size: 400
+2022-06-18 22:09:53,272022-06-18 22:09:53,426 INFO [train.py:445] Epoch 20, batch 9200, loss[loss=2.196, over 22400.00 frames., ppl: 8.991200149871661] tot_loss[loss=2.257, over 31732187.99 frames., ppl: 9.5570769158352022-06-18 22:11:08,270 INFO [train.py:445] 2022-02022-06-18 22:11:08,586 INFO [train.py:445] Epoch 20, batch 9400, loss[loss=2.178, over 48800.00 frames., ppl: 8.829066380766003] tot_loss[loss=2.259, over 31314341.29 2022-06-18 22:12:20,42022-06-18 22:12:20,472022-06-18 22:12022-06-18 22:12:20,564 INFO [train.py:445] Epoch 20, batch 9600, loss[loss=2.284, over 19200.00 frames., ppl: 9.812445509762808] tot_loss[loss=2.259, over 31712022-06-18 22:13:32,772 INFO [train.py:445] Epoc2022-06-182022-06-18 22:13:32,984 INFO [train.py:445] Epoch 20, batch 9800, loss[loss=2.171, over 30800.00 frames., ppl: 8.767392733028798] tot_loss[loss=2.26, over 31732022-06-18 22:14:46,2022-06-18 22:14:46,372 2022-06-18 22:14:46,460 2022-06-18 22:14:46,475 INFO [train.py:445] Epoch 20, batch 10000, loss[loss=2.222, over 24800.00 frames., ppl: 9.229036158481835] tot_loss[loss=2.257, over 32233140.06 frames., ppl: 9.551470114976626], batch size: 400
+202022-06-18 22:14:46,661 INFO [train.py:480] 2022-06-18 222022-06-2022-06-18 22:14:46,661 INFO [train.py:480] Epoch 20, validat2022-06-18 22:15:59,12022-06-18 22:15:59,208 INFO [train.py:445] Epo2022-06-18 22:15:59,278 INFO [train.py:445] Epoch 20, batch 10200, loss[loss=2.224, over 26400.00 frames., ppl: 9.246495766582091] tot_loss[loss=2.2572022-06-18 22:17:10,322 INFO [train.py:445] Epoch 20, batch 10400, loss[loss=2.193, over 22000.00 frames., ppl: 8.957908328563356] tot_loss[loss=2.26, over 31871671.58 frames., ppl: 9.587465514922235], batch size: 400
+2022-06-18 22:18:22,7652022-06-18 22:18:22,785 INFO [train.2022-06-18 22:18:22,952 INFO [train.py:445] Epoch 20, batch 10600, loss[loss=2.172, over 28400.00 frames., ppl: 8.776059794064048] tot_loss[loss=2.261, over 312022-06-18 22:19:35,679 2022-06-18 22:19:35,793 2022-06-18 22:19:35,878 INFO [train.py:445] Epoch 20, batch 10800, loss[loss=2.207, over 27200.00 frames., ppl: 9.089934309864764] tot_loss[loss=2.26, over 31698212.93 fr2022-06-18 22:20:44,329 INFO [train.py:445] Epoch 20, batch 11000, loss[loss=2.286, over 14000.00 frames., ppl: 9.831122834363988] tot_loss[loss=2.262, over 31559150.06 frames., ppl: 9.597839771042047], batch size: 400
+2022-06-18 22:21:58,581 INFO [train.py:445] Epo2022-06-18 22:21:58,835 INFO [train.py:445] Epoch 20, batch 11200, loss[loss=2.207, over 58400.00 frames., ppl: 9.084828671904823] tot_loss[loss=2.26, over 31975839.89 fra2022-06-18 22:23:11,372022-06-18 22:23:11,460 IN2022-06-18 22:23:11,542022-06-18 22:23:11,951 INFO [train.py:445] Epoch 20, batch 11400, loss[loss=2.157, over 70800.00 frames., ppl: 8.649393354767458] tot_loss[loss=2.22022-06-18 22:24:25,628 INFO [train.py:445] Epoch 20, batch 2022-06-18 22:24:25,674 INFO [train.py:445] Epoch 20, batch 11600, loss[loss=2.141, over 44800.00 frames., ppl: 8.512176164037772] tot_loss[loss=2.263, over 312022-06-18 22:25:37,32022-06-18 22:25:37,614 INFO [train.py:2022-06-18 22:25:37,970 INFO [train.py:445] Epoch 20, batch 11800, loss[loss=2.196, over 69600.00 frames., ppl: 8.988758728875288] tot_loss[loss=2.264, over 312022-06-18 22:26:46,354 INFO [train.py:445] Epoch2022-06-18 22:26:46,378 INFO [train.py:445] Epoch 20, batch 12000, loss[loss=2.194, over 22799.00 frames., ppl: 8.975403289814498] tot_loss[loss=2.259, over 32118486.10 f2022-06-18 22:27:592022-06-18 22:27:59,831 INFO [2022-06-18 22:27:59,944 INFO [train.py:445] Epoch 20, batch 12200, loss[loss=2.155, over 26800.00 frames., ppl: 8.631781957236914] tot_loss[loss=2.259, over 32102532.12 f2022-06-18 22:29:12022-06-18 22:29:11,171 INFO [train.py:4452022-06-18 22:29:11,172 INFO [train.py:445] Epoch 20, batch 12400, loss[loss=2.193, over 25600.00 frames., ppl: 8.965009462926114] tot_loss[loss=2.265, over 3092022-06-18 22:2022-06-18 22:30:24,650 INFO 2022-06-18 22:30:24,670 2022-06-18 22:30:24,819 INFO [train.py:445] Epoch 20, batch 12600, loss[loss=2.166, over 33200.00 frames., ppl: 8.725727841069938] tot_loss[loss=2.263,2022-06-18 22:32022-06-18 22:30:52,701 INFO2022-06-18 22:30:52,761 INFO [train.py:445] Epoch 21, batch 0, loss[loss=2.182, over 22400.00 frames., ppl: 8.863829849590843] tot_loss[loss=2.182, over 22400.00 frames., 2022-06-18 22:32:13,116 INFO [train.py:445] Epoch 21, 2022-06-18 22:32:13,166 INFO [train.py:445] Epoch 21, batch 200, loss[loss=2.264, over 18400.00 frames., ppl: 9.625599494558582] tot_loss[loss=2.244, over 3037002022-06-18 2022-06-18 22:33:24,862 INFO [train.py:445] Epoch 21, batch 400, loss[loss=2.181, over 53200.00 frames., ppl: 8.853857212046089] tot_loss[loss=2.252, over 5558195.28 frames., ppl: 9.504267139331892], batc2022-06-18 22022-06-18 22:34:36,173 INFO [train.py:445] Epoch 21, batch 600, loss[loss=2.15, over 50000.00 frames., ppl: 8.585294358087602] tot_loss[loss=2.25, over 8017457.78 frames., ppl: 9.490487128134042], batch 2022-06-182022-06-18 22:35:46,097 INFO [train.py:445] Epoch 21, batch 800, loss[loss=2.19, over 34000.00 frames., ppl: 8.937106299261943] tot_loss[loss=2.248, over 10371086.00 frames., ppl: 9.464834171054397], batch 2022-06-18 22:37:00,425 INFO [train.py:445] Epoch 21, batch 1000, loss[loss=2.186, over 23600.00 frames., ppl: 8.896660484593633] tot_loss[loss=2.239, over 12974187.00 frames., ppl: 9.387127940220358], batch size: 400
+2022-06-18 22:38:15,624 INFO [train.py:445]2022-06-18 22:38:15,831 INFO [train.py:445] Epoch 21, batch 1200, loss[loss=2.184, over 31600.00 frames., ppl: 8.883971206295055] tot_loss[loss=2.241, over 14721644.74 frames2022-06-18 22:39:26,751 INFO [train.py:445] 2022-06-18 22:39:27,086 INFO [train.py:445] Epoch 21, batch 1400, loss[loss=2.163, over 46000.00 frames., ppl: 8.696495919293248] tot_loss[loss=2.243, over 16388338.73 frame2022-02022-06-18 22:40:39,806 INFO [train.py:2022-06-18 22:40:39,942 INFO [train.py:445] Epoch 21, batch 1600, loss[loss=2.181, over 40400.00 frames., ppl: 8.859077195608148] tot_loss[loss=2.244, over 17867785.68 fram2022-06-18 22:41:56,317 INFO [train.py:445] 2022-06-18 22:2022-06-18 22:41:56,587 INFO [train.py:445] Epoch 21, batch 1800, loss[loss=2.148, over 61200.00 frames., ppl: 8.565675274046809] tot_loss[loss=2.245, over 19182022-06-18 22:43:09,498 INFO [train.py:445] 2022-06-18 22:43:09,795 INFO [train.py:445] Epoch 21, batch 2000, loss[loss=2.151, over 43200.00 frames., ppl: 8.59679475966046] tot_loss[loss=2.245, over 20537977.48 frames2022-02022-06-18 22:44:22,686 INFO [train.py2022-06-18 22:44:22,827 INFO [train.py:445] Epoch 21, batch 2200, loss[loss=2.196, over 27200.00 frames., ppl: 8.992839535378122] tot_loss[loss=2.247, over 21346360.24 frames2022-02022-06-18 22:45:36,420 INFO [train.py2022-06-18 222022-06-18 22:45:36,653 INFO [train.py:445] Epoch 21, batch 2400, loss[loss=2.153, over 34800.00 frames., ppl: 8.606780194563832] tot_loss[loss=2.25, over 2204472022-06-18 22:46:48,416 INFO [train.py:445] Epoch 21, batch 2600, loss[loss=2.264, over 18400.00 frames., ppl: 9.622696150054553] tot_loss[loss=2.249, over 23335167.18 frames., ppl: 9.475516584268625], batch size: 400
+2022-06-18 22:47:59,776 INFO [train.py:445] Epoch 21, batch 2800, loss[loss=2.203, over 18400.00 frames., ppl: 9.054077655308435] tot_loss[loss=2.25, over 23915527.40 frames., ppl: 9.491041787383635], batch size: 400
+2022-06-18 22:49:15,504 INFO [train.py:445] Ep2022-06-18 22:49:15,722 INFO [train.py:445] Epoch 21, batch 3000, loss[loss=2.156, over 36000.00 frames., ppl: 8.63586267537988] tot_loss[loss=2.247, over 25161711.08 frame2022-06-18 22:50:28,045 INFO [train.py:445] E2022-06-18 22:50:28,054 2022-06-18 22:50:28,145 INFO [train.py:445] Epoch 21, batch 3200, loss[loss=2.18, over 34400.00 frames., ppl: 8.84655940428245] tot_loss[loss=2.2492022-06-18 22:51:41,527 INFO [train.py:442022-06-18 22:51:41,662 INFO [train.py:445] Epoch 21, batch 3400, loss[loss=2.163, over 25200.00 frames., ppl: 8.69484520373486] tot_loss[loss=2.252, over 25682793.89 frames., pp2022-062022-06-18 22:52:51,973 INFO [train.py2022-06-18 22:52:52,103 INFO [train.py:445] Epoch 21, batch 3600, loss[loss=2.202, over 23600.00 frames., ppl: 9.042059030893922] tot_loss[loss=2.249, over 27049386.67 frame2022-062022-06-18 22:54:04,727 INFO [train.py2022-06-18 22:54:05,393 INFO [train.py:445] Epoch 21, batch 3800, loss[loss=2.205, over 80000.00 frames., ppl: 9.067651329451056] tot_loss[loss=2.249, over 27659560.18 fram2022-06-2022-06-18 22:55:17,735 INFO [t2022-06-182022-06-18 22:55:17,805 INFO [train.py:445] Epoch 21, batch 4000, loss[loss=2.212, over 19600.00 frames., ppl: 9.135230960809482] tot_loss[loss=2.251, over 27580782.45 f2022-06-2022-06-18 22:56:29,009 INFO [t2022-02022-06-18 22:56:29,430 INFO [train.py:445] Epoch 21, batch 4200, loss[loss=2.168, over 60000.00 frames., ppl: 8.739994017829419] tot_loss[loss=2.25, over 28409701.21 frames2022-06-18 22:57:42,111 INFO [train.py:2022-2022-06-18 22:52022-06-18 22:57:42,251 INFO [train.py:445] Epoch 21, batch 4400, loss[loss=2.199, over 21600.00 frames., ppl: 9.014606529398675] tot_loss[loss=2.253, over 282022-062022-06-18 22:58:51,471 INFO [tr2022-06-18 22:58:51,611 INFO [train.py:445] Epoch 21, batch 4600, loss[loss=2.173, over 29200.00 frames., ppl: 8.78860469251987] tot_loss[loss=2.254, over 28294405.11 frames., pp2022-06-18 23:00:07,057 INFO [train.py:445] E2022-06-18 23:00:07,090 INFO [train.py:445] Epoch 21, batch 4800, loss[loss=2.205, over 16800.00 frames., ppl: 9.069639987621445] tot_loss[loss=2.251, over 29414134.89 fram2022-06-12022-06-18 23:01:19,226 INFO [train.py:442022-06-18 23:01:19,341 INFO [train.py:445] Epoch 21, batch 5000, loss[loss=2.183, over 26000.00 frames., ppl: 8.872205188273204] tot_loss[loss=2.251, over 29379882.042022-06-182022-06-18 23:02:31,404 INFO [train.py:442022-06-18 23:02:31,468 INFO [train.py:445] Epoch 21, batch 5200, loss[loss=2.14, over 38000.00 frames., ppl: 8.498207536146065] tot_loss[loss=2.252, over 29663683.132022-06-18 23:03:44,455 INFO [train.py:445] Epoch 21, batch 5400, loss[loss=2.226, over 20800.00 frames., ppl: 9.259901017767632] tot_loss[loss=2.256, over 29207016.31 frames., ppl: 9.543810617012808], batch size: 400
+2022-06-18 23:04:58,447 INFO [train.py:445] Epoch 21, batch 562022-06-18 23:04:58,582 INFO [train.py:445] Epoch 21, batch 5600, loss[loss=2.154, over 36400.00 frames., ppl: 8.61920592329472] tot_loss[loss=2.255, over 22022-06-18 2022-06-18 23:06:12,643 INFO [train.py:445] Epoch 21, batch 5800, loss[loss=2.166, over 48800.00 frames., ppl: 8.727587778818544] tot_loss[loss=2.252, over 30788864.85 frames., ppl: 9.503474521153707], batch2022-06-18 23:07:25,783 INFO [train.py:445] Epoch 21, batch 6000, loss[loss=2.17, over 36800.00 frames., ppl: 8.762103979219816] tot_loss[loss=2.255, over 30225976.69 frames., ppl: 9.532201635613564], batch size: 400
+2022-06-18 23:08:37,902 INFO [train.py:445] Epoch 21, batch 6200, loss[loss=2.199, over 67566.00 frames., ppl: 9.016267019714938] tot_loss[loss=2.255, over 30470301.51 frames., ppl: 9.531669741222169], batch size: 400
+2022-06-182022-06-18 23:09:51,768 INFO [train.py:445] Epoch 22022-06-18 23:09:52,038 INFO [train.py:445] Epoch 21, batch 6400, loss[loss=2.153, over 41600.00 frames., ppl: 8.610974419012505] tot_loss[loss=2.251, over 32022-06-18 23:11:03,384 INFO [train.py:445] Epo2022-06-18 23:112022-06-18 23:11:03,401 INFO [train.py:445] Epoch 21, batch 6600, loss[loss=2.207, over 21600.00 frames., ppl: 9.089032837837326] tot_loss[loss=2.255, over2022-06-18 23:12:15,155 INFO [train.py:445] Epoch 21, batch 6800, loss[loss=2.203, over 22000.00 frames., ppl: 9.049019665197969] tot_loss[loss=2.255, over 31070349.00 frames., ppl: 9.531576513724016], batch size: 400
+2022-02022-06-18 23:13:28,979 INFO [train.py:445]2022-06-18 23:13:29,120 INFO [train.py:445] Epoch 21, batch 7000, loss[loss=2.154, over 26800.00 frames., ppl: 8.618501293447249] tot_loss[loss=2.257, over 30558452.96 f2022-06-18 23:14:43,276 INFO [train.py:445] Epoch 21, batch 7200, loss[loss=2.185, over 29200.00 frames., ppl: 8.892671872340337] tot_loss[loss=2.255, over 31182233.98 frames., ppl: 9.535919529872702], batch size: 400
+2022-06-18 23:15:59,795 INFO [train.py:445] Epoch 21, batch 7400, loss[loss=2.15, over 64400.00 frames., ppl: 8.581359847312374] tot_loss[loss=2.254, over 31522404.77 frames., ppl: 9.528372966995807], batch size: 400
+2022-06-18 23:17:09,730 INFO [train.2022-06-18 2022-06-18 23:17:09,921 INFO [train.py:445] Epoch 21, batch 7600, loss[loss=2.163, over 30400.00 frames., ppl: 8.697615702543098] tot_loss[loss=2.257, over 30865325.95 fram2022-2022-06-18 23:18:24,037 INFO [train.py:445] Epoch 21, batch 7800, loss[loss=2.155, over 44400.00 frames., ppl: 8.630454697574638] tot_loss[loss=2.255, over 31504458.35 frames., ppl: 9.538603410280444], batch size2022-02022-06-18 23:19:39,873 INFO 2022-06-18 222022-06-18 23:19:40,417 INFO [train.py:445] Epoch 21, batch 8000, loss[loss=2.178, over 69600.00 frames., ppl: 8.828450570261673] tot_loss[loss=2.254, over 31352330.71 f2022-062022-06-18 23:20:53,711 INFO [train.py:445] Epoch 21, batch 8200, loss[loss=2.142, over 35200.00 frames., ppl: 8.5184966799826] tot_loss[loss=2.256, over 31455626.62 frames., ppl: 9.545045732150808], batch size:2022-2022-06-18 23:22:09,401 INFO [train.py:445] Epoch 22022-06-18 23:22:09,440 INFO [train.py:445] Epoch 21, batch 8400, loss[loss=2.152, over 39200.00 frames., ppl: 8.602288396874883] tot_loss[loss=2.253, over 321842022-2022-06-18 23:23:22,916 INFO [train.py:445] Epoch 21, batch 8600, loss[loss=2.245, over 16800.00 frames., ppl: 9.443688924147597] tot_loss[loss=2.257, over 31438893.44 frames., ppl: 9.55049993929843], batch size:2022-2022-06-18 23:24:36,660 INFO [train.py:442022-06-18 23:24:36,690 INFO [train.py:445] Epoch 21, batch 8800, loss[loss=2.147, over 33600.00 frames., ppl: 8.561949752041293] tot_loss[loss=2.258, over 31116794.83 fram2022022-06-18 23:25:47,823 INFO [tra2022-06-18 23:25:47,950 INFO [train.py:445] Epoch 21, batch 9000, loss[loss=2.179, over 26000.00 frames., ppl: 8.841706206304014] tot_loss[loss=2.255, over 32012900.96 frames., ppl: 202022-06-18 23:27:00,108 INFO [train.py:445] Epoch 21, batch 9200, loss[loss=2.269, over 15200.00 frames., ppl: 9.671839249768892] tot_loss[loss=2.257, over 31555977.21 frames., ppl: 9.555844658941494], batch size: 4002022-06-18 23:28:08,703 INFO [train.py:445] Epoch 21, b2022-06-18 23:28:08,710 INFO [train.py:445] Epoch 21, batch 9400, loss[loss=2.294, over 14800.00 frames., ppl: 9.913246948731672] tot_loss[loss=2.256, over 318382022-06-18 23:29:22,238 INFO [train.py:445] Epoch 21, batch 9600, loss[loss=2.211, over 43818.00 frames., ppl: 9.127084445285698] tot_loss[loss=2.256, over 32081777.09 frames., ppl: 9.543213297398278], batch size: 201
+2022-06-18 23:30:34,353 INFO [train2022-06-18 23:30:34,366 INFO [train.py:445] Epoch 21, batch 9800, loss[loss=2.176, over 30400.00 frames., ppl: 8.812823296722911] tot_loss[loss=2.259, over 31404758.92 frames., ppl: 92022-06-18 23:31:47,649 INFO [train.py:445] Epoch 21, batch 10000, loss[loss=2.275, over 16800.00 frames., ppl: 9.72468505334788] tot_loss[loss=2.257, over 31996592.22 frames., ppl: 9.551198999468818], batch size: 400
+2022-06-18 23:31:47,649 INFO [train.py:469] Computing validation loss
+2022-06-18 23:31:47,832 INFO [train.p2022-06-18 23:31:47,82022-06-18 23:31:47,832 INFO [train.py:480] Epoch 21, validation: lo2022-06-18 23:32:58,490 INFO [train.p2022-06-18 2022-06-18 23:32:58,722 INFO [train.py:445] Epoch 21, batch 10200, loss[loss=2.158, over 41600.00 frames., ppl: 8.655996594919282] tot_loss[loss=2.257, over 31792700.52 fr22022-06-18 23:34:12,005 INFO [train2022-06-18 23:34:12,088 INFO [train.py:445] Epoch 21, batch 10400, loss[loss=2.244, over 18800.00 frames., ppl: 9.429670571120534] tot_loss[loss=2.26, over 31417078.42 frames., ppl: 922022-06-18 23:35:21,029 INFO [train.py:445] Epoc22022-06-18 23:35:21,223 INFO [train.py:445] Epoch 21, batch 10600, loss[loss=2.166, over 29600.00 frames., ppl: 8.722241580690401] tot_loss[loss=2.259, over 31479155.58 2022-06-18 23:36:32,803 INFO [train.py:445] Epoc2022-06-18 23:36:32,976 INFO [train.py:445] Epoch 21, batch 10800, loss[loss=2.165, over 27600.00 frames., ppl: 8.715214930535115] tot_loss[loss=2.258, over 31797445.07 fr2022-06-18 23:37:48,537 INFO [train.py:445] Epoch 21, batch 11000, loss[loss=2.147, over 46400.00 frames., ppl: 8.557154755274286] tot_loss[loss=2.256, over 32275494.15 frames., ppl: 9.548900969688125], batch size: 40022022-06-18 23:39:01,491 INFO [train.py:445] Epoch 21, batch2022-06-18 23:39:01,528 INFO [train.py:445] Epoch 21, batch 11200, loss[loss=2.196, over 17600.00 frames., ppl: 8.986241550549343] tot_loss[loss=2.258, over 3202022-06-18 23:40:17,905 INFO [train.py:445] Epo2022-06-18 23:40:18,025 INFO [train.py:445] Epoch 21, batch 11400, loss[loss=2.2, over 22800.00 frames., ppl: 9.024126009193784] tot_loss[loss=2.259, over 31711317.37 frame2022-06-18 23:41:34,202 INFO [tra2022-06-18 23:41:34,266 INFO [train.py:445] Epoch 21, batch 11600, loss[loss=2.178, over 22400.00 frames., ppl: 8.826760614118138] tot_loss[loss=2.26, over 31725434.37 frames., ppl: 9.202022-06-18 23:42:47,518 INFO [tr2022-06-18 23:42:47,568 INFO [train.py:445] Epoch 21, batch 11800, loss[loss=2.201, over 30400.00 frames., ppl: 9.029611601070574] tot_loss[loss=2.26, over 31754730.33 frames., ppl: 9.522022-06-18 23:44:00,994 INFO [train.py:445] Epoch 21, batch 12000, loss[loss=2.232, over 24800.00 frames., ppl: 9.32044467026987] tot_loss[loss=2.258, over 32086329.16 frames., ppl: 9.566135880597662], batch size: 400
+2022-06-18 23:45:11,838 INFO [train.py:445] Epo2022-06-18 23:45:12,139 INFO [train.py:445] Epoch 21, batch 12200, loss[loss=2.196, over 70800.00 frames., ppl: 8.989885841992779] tot_loss[loss=2.26, over 31698152.40 fra22022-06-18 23:46:24,050 INFO [train.py:445] Epoch 21, batch 12400, loss[loss=2.135, over 49600.00 frames., ppl: 8.457672602972472] tot_loss[loss=2.257, over 32349966.40 frames., ppl: 9.551236794912697], batch size: 40202022-06-18 23:47:36,910 INFO [train.py:445] E2022-06-18 23:47:37,110 INFO [train.py:445] Epoch 21, batch 12600, loss[loss=2.175, over 40000.00 frames., ppl: 8.798486231152962] tot_loss[loss=2.26, over 31739643.43 fram202022-06-18 23:48:05,610 INFO [train.py:445] 2022-06-18 23:48:05,620 INFO [train.py:445] Epoch 22, batch 0, loss[loss=2.194, over 25200.00 frames., ppl: 8.969909241685285] tot_loss[loss=2.194, over 25200.00 fram202022-06-18 23:49:24,099 INFO [train.py:445] Epoch 22, bat2022-06-18 23:49:24,218 INFO [train.py:445] Epoch 22, batch 200, loss[loss=2.222, over 24400.00 frames., ppl: 9.225520552623887] tot_loss[loss=2.242, over 3122022-06-18 23:50:40,325 INFO [train.py:445] Epoch 22, batc2022-06-18 23:50:40,522 INFO [train.py:445] Epoch 22, batch 400, loss[loss=2.154, over 32400.00 frames., ppl: 8.620121254591105] tot_loss[loss=2.227, over 62022-06-18 23:51:51,967 INFO [train.py:445] Epoch 22, batch2022-06-18 23:51:52,074 INFO [train.py:445] Epoch 22, batch 600, loss[loss=2.121, over 37200.00 frames., ppl: 8.335743841701262] tot_loss[loss=2.243, over 812022-06-18 23:53:07,062 INFO [2022-06-18 232022-06-18 23:532022-06-18 23:53:07,317 INFO [train.py:445] Epoch 22, batch 800, loss[loss=2.146, over 40000.00 frames., ppl: 8.551838499910534] tot_loss[loss=2.24, over 106152022-06-18 23:54:18,635 INFO [train.py:445] E2022-06-18 23:54:18,766 INFO [train.py:445] Epoch 22, batch 1000, loss[loss=2.164, over 30000.00 frames., ppl: 8.709406477002359] tot_loss[loss=2.244, over 12359011.63 fr2022-06-18 23:55:29,168 INFO [train.py:445] Ep202022-06-18 23:55:29,316 INFO [train.py:445] Epoch 22, batch 1200, loss[loss=2.153, over 30800.00 frames., ppl: 8.609026378543243] tot_loss[loss=2.246, over 14130934.60 fr202022-06-18 23:56:44,716 INFO2022-06-18 23:56:44,777 INFO [train.py:445] Epoch 22, batch 1400, loss[loss=2.254, over 17200.00 frames., ppl: 9.521452589155178] tot_loss[loss=2.241, over 16349330.35 frames., ppl: 9.40120222022-06-18 23:57:54,737 INFO [train.py:445] Epoch 22, ba22022-06-18 23:57:54,839 INFO [train.py:445] Epoch 22, batch 1600, loss[loss=2.175, over 38000.00 frames., ppl: 8.805598802293026] tot_loss[loss=2.243, over 12022-06-18 23:59:06,955 INFO [train.py:445] Epoch 22, batch2022-06-18 23:59:06,974 INFO [train.py:445] Epoch 22, batch 1800, loss[loss=2.187, over 18000.00 frames., ppl: 8.90738441581188] tot_loss[loss=2.246, over 18802022-06-19 00:00:15,446 INFO2022-06-19 00:00:15,474 INFO [train.py:445] Epoch 22, batch 2000, loss[loss=2.181, over 21200.00 frames., ppl: 8.85799288211044] tot_loss[loss=2.244, over 20287488.08 frames., ppl: 9.43245402022-2022-06-19 00:01:25,909 INFO [train.py:445] Epoch 22, batch 2200, loss[loss=2.189, over 26400.00 frames., ppl: 8.922207934350734] tot_loss[loss=2.249, over 21082875.91 frames., ppl: 9.477631339123652], batch size:2022-2022-06-19 00:02:38,334 INFO [train.py:442022-06-19 00:02:38,450 INFO [train.py:445] Epoch 22, batch 2400, loss[loss=2.189, over 27600.00 frames., ppl: 8.925016081644287] tot_loss[loss=2.247, over 22597545.74 fram2022-2022-06-19 00:03:55,014 INFO [train.py:445] Epoch 22, batch 2600, loss[loss=2.172, over 46000.00 frames., ppl: 8.775803232504174] tot_loss[loss=2.248, over 23272831.89 frames., ppl: 9.467786485222], batch size: 402022-06-19 00:05:12,644 INFO [train.py:442022-06-19 00:05:12,789 INFO [train.py:445] Epoch 22, batch 2800, loss[loss=2.118, over 50800.00 frames., ppl: 8.312724659576082] tot_loss[loss=2.251, over 23246814.30 frames.,2022-06-19 00:06:26,319 INFO [train.py:445] Epoch 22, batch 3000, loss[loss=2.192, over 18400.00 frames., ppl: 8.954048406761238] tot_loss[loss=2.246, over 25303933.18 frames., ppl: 9.445875985592489], batch size: 400
+2022-06-19 00:07:36,353 INF2022-06-19 00:07:32022-06-19 00:02022-06-19 00:07:36,656 INFO [train.py:445] Epoch 22, batch 3200, loss[loss=2.145, over 45200.00 frames., ppl: 8.538129590109593] tot_loss[loss=2.25, over 2508202022-06-19 00:08:51,056 INFO [train.py:445] Epoch 22, batch 3400, loss[loss=2.16, over 64400.00 frames., ppl: 8.667789010982554] tot_loss[loss=2.246, over 26910652.60 frames., ppl: 9.453188675305405], batch size: 4002022-06-19 00:10:03,966 I2022-06-19 00:10:2022-06-12022-06-19 00:10:04,329 INFO [train.py:445] Epoch 22, batch 3600, loss[loss=2.145, over 62800.00 frames., ppl: 8.545119238899126] tot_loss[loss=2.25, over 26772328.74 2022-06-19 00:11:19,313 I2022-06-19 00:11:2022-06-19 00:11:19,536 INFO [train.py:445] Epoch 22, batch 3800, loss[loss=2.164, over 30400.00 frames., ppl: 8.706285896945761] tot_loss[loss=2.249, over 27376172.76 frames.22022-06-19 00:12:32,704 INFO [train.py:445] Epoch 22, batch 4000, loss[loss=2.218, over 28000.00 frames., ppl: 9.189357655959151] tot_loss[loss=2.249, over 27992347.88 frames., ppl: 9.482575937482006], batch size: 40022022-06-19 00:13:47,943 INFO [train.py2022-06-2022-06-19 00:13:48,078 INFO [train.py:445] Epoch 22, batch 4200, loss[loss=2.196, over 34800.00 frames., ppl: 8.991426855353364] tot_loss[loss=2.249, over 28430698.53 fr202022-06-19 00:15:00,723 INFO [train.py:445] Epoch 22, batch 4400, loss[loss=2.172, over 54400.00 frames., ppl: 8.779434218487307] tot_loss[loss=2.249, over 28902090.53 frames., ppl: 9.474208371809468], batch size: 42022-06-19 00:16:12,535 INFO [train.py:4452022-06-19 00:12022-06-19 00:16:12,854 INFO [train.py:445] Epoch 22, batch 4600, loss[loss=2.126, over 52000.00 frames., ppl: 8.385443581569147] tot_loss[loss=2.255, over 2782022-06-19 00:17:28,106 INFO [train.py:445] Epoch 22, batch 4800, loss[loss=2.153, over 52800.00 frames., ppl: 8.611635023636646] tot_loss[loss=2.249, over 29281755.61 frames., ppl: 9.476462975354641], batch size: 400
+2022022-06-19 00:18:42,211 INFO [train.py:445] Epoch 22, batch 5000, loss[loss=2.169, over 43600.00 frames., ppl: 8.745628458274782] tot_loss[loss=2.25, over 29776931.01 frames., ppl: 9.487687957143354], batch size: 2022-06-19 00:19:51,803 IN2022-06-19 00:192022-06-19 00:19:51,939 INFO [train.py:445] Epoch 22, batch 5200, loss[loss=2.163, over 36400.00 frames., ppl: 8.696145604651992] tot_loss[loss=2.251, over 29857170.10 frames20222022-06-19 00:21:06,4182022-06-19 00:21:06,450 INFO [train.py:445] Epoch 22, batch 5400, loss[loss=2.184, over 31200.00 frames., ppl: 8.877550204095932] tot_loss[loss=2.255, over 29159435.21 frames., ppl: 9.5333396202022-06-19 00:22:20,532 INFO [train.py:445] Epoch 2022-06-19 00:22:20,682 INFO [train.py:445] Epoch 22, batch 5600, loss[loss=2.2, over 26400.00 frames., ppl: 9.022070807860615] tot_loss[loss=2.251, over 30303505.102022-06-19 00:23:34,349 INFO [train.py:445] Epoch 22022-06-19 00:23:34,495 INFO [train.py:445] Epoch 22, batch 5800, loss[loss=2.174, over 34400.00 frames., ppl: 8.789963471140785] tot_loss[loss=2.251, over 30548377.06 2022-06-19 00:24:48,821 INFO [train.py:445] Epoch 22, batch 6000, loss[loss=2.155, over 26400.00 frames., ppl: 8.626246052592762] tot_loss[loss=2.25, over 31024962.85 frames., ppl: 9.488393588372121], batch size: 400
+2022-06-19 00:26:02,274 IN2022-06-19 00:26:02022-06-19 00:26:02,751 INFO [train.py:445] Epoch 22, batch 6200, loss[loss=2.263, over 59496.00 frames., ppl: 9.608810848912642] tot_loss[loss=2.254, over 30298722.12 frame2022-06-19 00:27:15,114 INFO [train.py:445] Epoch 22, batch 6400, loss[loss=2.164, over 39600.00 frames., ppl: 8.70451911709475] tot_loss[loss=2.249, over 31648774.02 frames., ppl: 9.47604287372668], batch size: 400
+2022-06-19 00:28:27,894 INFO [train.py:445] Ep2022-06-19 00:28:27,947 INFO [train.py:445] Epoch 22, batch 6600, loss[loss=2.175, over 19600.00 frames., ppl: 8.79881056908502] tot_loss[loss=2.253, over 30875588.41 frame2022-06-19 00:29:39,919 INFO [train.py:42022-06-19 00:29:40,005 INFO [train.py:445] Epoch 22, batch 6800, loss[loss=2.19, over 26400.00 frames., ppl: 8.936598772459476] tot_loss[loss=2.251, over 31677662.98 frames., 2022-02022-06-19 00:30:53,7602022-06-19 00:30:54,099 INFO [train.py:445] Epoch 22, batch 7000, loss[loss=2.179, over 52400.00 frames., ppl: 8.837139194322557] tot_loss[loss=2.255, over 30730589.91 frames., ppl: 9.538372022-06-19 00:32:09,964 INFO 2022-06-19 02022-06-19 00:32:10,239 INFO [train.py:445] Epoch 22, batch 7200, loss[loss=2.192, over 40800.00 frames., ppl: 8.956467697945055] tot_loss[loss=2.252, over 31775837.99 frames., 2022-06-19 00:33:22,094 INFO [2022-06-19 00:33:22,230 INFO [train.py:445] Epoch 22, batch 7400, loss[loss=2.159, over 51200.00 frames., ppl: 8.665815095620847] tot_loss[loss=2.257, over 30601656.01 frames., ppl: 9.552022-06-19 00:34:34,469 INFO [train.py:445] Epoch 22, batch 7600, loss[loss=2.204, over 26800.00 frames., ppl: 9.060344327119914] tot_loss[loss=2.25, over 32308118.97 frames., ppl: 9.486027690886779], batch size: 400
+2022-06-19 00:35:49,095 INFO [tr2022-06-19 00:35:49,330 INFO [train.py:445] Epoch 22, batch 7800, loss[loss=2.161, over 42000.00 frames., ppl: 8.680660812527503] tot_loss[loss=2.256, over 30854876.55 frames., ppl: 9.54202022-06-19 00:37:01,410 INFO [2022-06-192022-06-19 00:37:01,654 INFO [train.py:445] Epoch 22, batch 8000, loss[loss=2.164, over 51600.00 frames., ppl: 8.708120626378546] tot_loss[loss=2.253, over 31750632.29 frames.,202022-06-19 00:38:13,080 INFO [train.py:445] Epoch 22, batch 8200, loss[loss=2.187, over 41600.00 frames., ppl: 8.907732292961445] tot_loss[loss=2.254, over 31373169.81 frames., ppl: 9.526520144175572], batch size: 42022022-06-19 00:39:26,103 INFO 2022-06-19 00:39:26,148 INFO [t2022-06-19 00:39:26,303 INFO [train.py:445] Epoch 22, batch 8400, loss[loss=2.198, over 37600.00 frames., ppl: 9.009367194228625] tot_loss[loss=2.254, over2022-06-19 00:40:34,356 INFO [train.py:445] Epoch 22, b2022-06-19 00:40:34,383 INFO [train.py:445] Epoch 22, batch 8600, loss[loss=2.212, over 20000.00 frames., ppl: 9.135789476656463] tot_loss[loss=2.255, over 31528120222022-06-19 00:41:45,983 INFO [train.py:445] Epoch 22, batch 8800, loss[loss=2.157, over 41200.00 frames., ppl: 8.643420468147864] tot_loss[loss=2.256, over 31144612.01 frames., ppl: 9.543880494259263], batch size: 2022-06-19 00:43:00,417 INFO [t2022-06-19 00:43:00,522 INFO [tra2022-06-19 00:43:00,615 INFO [train.py:445] Epoch 22, batch 9000, loss[loss=2.189, over 30000.00 frames., ppl: 8.928102678045837] tot_loss[loss=2.255, ove2022-06-19 00:44:11,419 INFO [train.py:442022-06-19 00:44:11,515 INFO [train.py:445] Epoch 22, batch 9200, loss[loss=2.24, over 21600.00 frames., ppl: 9.397412537981603] tot_loss[loss=2.254, over 32028308.58 frames., pp2022-06-19 00:45:19,637 INFO2022-06-19 00:45:19,771 I2022-06-19 00:45:20,058 INFO [train.py:445] Epoch 22, batch 9400, loss[loss=2.203, over 78000.00 frames., ppl: 9.052818283684873] tot_loss[loss=2.257, over 31464529.2022-06-19 00:46:30,126 INFO [train.py:445] E2022-06-2022-06-19 00:46:30,227 INFO [train.py:445] Epoch 22, batch 9600, loss[loss=2.239, over 20000.00 frames., ppl: 9.38629259671111] tot_loss[loss=2.258, over 31309518.2022-06-19 00:47:42,249 INFO [train.2022-06-19 00:47:42,479 INFO [train.py:445] Epoch 22, batch 9800, loss[loss=2.185, over 54400.00 frames., ppl: 8.889125455489602] tot_loss[loss=2.257, over 31616642.42 frames., ppl:2022-06-19 00:48:55,446 INFO [train.py:445] Epoch 22, batch 10000, loss[loss=2.224, over 26400.00 frames., ppl: 9.241160158763494] tot_loss[loss=2.256, over 31891900.49 frames., ppl: 9.541983346506102], batch size: 400
+2022-06-19 00:48:55,446 INFO [train.py:469] Computing validation loss
+22022-06-19 00:48:55,751 INFO [train.py:480] Epoch 22, validation: loss=2.322, over 211809.00 frames., ppl: 10.19221686133408722022-06-19 00:50:09,835 INFO [train.2022-06-12022-06-19 00:50:10,032 INFO [train.py:445] Epoch 22, batch 10200, loss[loss=2.181, over 28800.00 frames., ppl: 8.857349431186947] tot_loss[loss=2.256, over 31785660.28 fra2022-06-19 00:51:24,936 INFO [train.py:445] Epoch 22, batch 10402022-06-19 00:51:25,027 INFO [train.py:445] Epoch 22, batch 10400, loss[loss=2.245, over 20400.00 frames., ppl: 9.439640094616205] tot_loss[loss=2.258, over22022-06-19 00:52:37,243 INFO [train.py:445] Epoch 22, batch 10600, loss[loss=2.176, over 30800.00 frames., ppl: 8.814929643937866] tot_loss[loss=2.257, over 31702518.76 frames., ppl: 9.557936551438168], batch size: 2022-06-19 00:53:49,568 INFO2022-06-19 2022-06-12022-06-19 00:53:49,932 INFO [train.py:445] Epoch 22, batch 10800, loss[loss=2.134, over 58400.00 frames., ppl: 8.44809098216734] tot_loss[loss=2.257, over 31670690.69 fra20222022-06-19 00:55:00,381 INFO [trai2022-06-19 00:55:00,535 INFO [train.py:445] Epoch 22, batch 11000, loss[loss=2.184, over 34000.00 frames., ppl: 8.883966735990025] tot_loss[loss=2.257, over 31815212.06 frames., ppl22022-06-19 00:56:11,997 INFO [train.py:445] Epoch 22022-06-19 00:2022-06-19 00:56:12,326 INFO [train.py:445] Epoch 22, batch 11200, loss[loss=2.162, over 47600.00 frames., ppl: 8.688392332669938] tot_loss[loss=2.2572022-06-19 00:57:22,588 INFO 2022-06-19 00:57:22,62022-06-19 00:57:22,673 INFO [train.py:445] Epoch 22, batch 11400, loss[loss=2.264, over 17200.00 frames., ppl: 9.617236512300884] tot_loss[loss=2.258, over 31616062.92022-06-19 00:58:34,269 INFO [t2022-06-19 2022-06-192022-06-19 00:58:35,127 INFO [train.py:445] Epoch 22, batch 11600, loss[loss=2.257, over 66439.00 frames., ppl: 9.558651689118493] tot_loss[loss=2.258, over 31718231.92022-06-19 00:59:50,064 INFO [t2022-06-19 00:59:50,22022-06-19 00:59:50,473 INFO [train.py:445] Epoch 22, batch 11800, loss[loss=2.167, over 62000.00 frames., ppl: 8.73380483053622] tot_loss[loss=2.258, over 31738110.42022-06-19 01:01:03,763 INFO [tr2022-06-19 01:01:04,057 INFO [train.py:445] Epoch 22, batch 12000, loss[loss=2.147, over 38000.00 frames., ppl: 8.562829764255442] tot_loss[loss=2.258, over 31919992.43 frames., ppl: 9.2022-06-19 01:02:18,007 INFO [train.py:445] 2022-06-12022-06-19 01:02:18,209 INFO [train.py:445] Epoch 22, batch 12200, loss[loss=2.173, over 38800.00 frames., ppl: 8.788063765219217] tot_loss[loss=2.26, over 31506669.82022-06-19 01:03:28,174 INFO [trai2022-06-19 01:03:28,223 INFO [train.py:445] Epoch 22, batch 12400, loss[loss=2.355, over 11200.00 frames., ppl: 10.53941533289716] tot_loss[loss=2.258, over 31884637.20 frames., ppl: 92022-062022-06-19 01:04:43,889 INFO2022-06-19 01:04:44,324 INFO [train.py:445] Epoch 22, batch 12600, loss[loss=2.222, over 58290.00 frames., ppl: 9.227786318349118] tot_loss[loss=2.257, over 32120302.47 frames., ppl: 2022-06-2022-06-19 01:05:11,214 INFO [train.py:445] Epo2022-06-19 01:05:11,354 INFO [train.py:445] Epoch 23, batch 0, loss[loss=2.184, over 24400.00 frames., ppl: 8.882118399805437] tot_loss[loss=2.184, over 24402022-06-19 01:06:29,590 INFO [train2022-06-19 01:06:29,630 INFO [train.py:445] Epoch 23, batch 200, loss[loss=2.234, over 20000.00 frames., ppl: 9.333402287021832] tot_loss[loss=2.242, over 2990811.26 frames., ppl: 92022-02022-06-19 01:07:44,921 INFO 2022-06-2022-062022-06-19 01:07:45,150 INFO [train.py:445] Epoch 23, batch 400, loss[loss=2.129, over 41200.00 frames., ppl: 8.406321612731114] tot_loss[loss=2.239, over 5859649.57 2022-06-19 01:08:55,673 INFO [tra2022-06-19 01:08:55,762 INFO [train.py:445] Epoch 23, batch 600, loss[loss=2.175, over 26000.00 frames., ppl: 8.800556022350758] tot_loss[loss=2.248, over 7917205.31 frames., ppl: 9.42022-06-19 01:10:10,019 INFO [train.py:445] Epoch 2022-06-19 01:10:10,292 INFO [train.py:445] Epoch 23, batch 800, loss[loss=2.146, over 52800.00 frames., ppl: 8.550905876004574] tot_loss[loss=2.243, over 10446332.82022-06-19 01:11:22,590 INFO [train2022-06-19 01:11:22,918 INFO [train.py:445] Epoch 23, batch 1000, loss[loss=2.135, over 44400.00 frames., ppl: 8.45583678574551] tot_loss[loss=2.248, over 12124672.49 frames., ppl: 920222022-06-19 01:12:34,104 INFO [t2022-06-19 01:12:34,226 INFO [train.py:445] Epoch 23, batch 1200, loss[loss=2.187, over 30000.00 frames., ppl: 8.908011511562364] tot_loss[loss=2.249, over 13810549.64 frames., ppl: 2022-06-19 01:13:46,801 INFO [train.2022-06-12022-06-19 01:13:46,850 INFO [train.py:445] Epoch 23, batch 1400, loss[loss=2.207, over 19200.00 frames., ppl: 9.089570756314654] tot_loss[loss=2.237, over 16589750.48 frame2022-02022-06-19 01:15:00,472 INFO [train.py:445] Epoch 23, batch 1600, loss[loss=2.151, over 30000.00 frames., ppl: 8.592187717060737] tot_loss[loss=2.249, over 17094718.01 frames., ppl: 9.474407595329577], batch size2022-02022-06-19 01:16:15,678 INFO [train.py:445] Epoch 23, batch 1800, loss[loss=2.126, over 39600.00 frames., ppl: 8.378148713573639] tot_loss[loss=2.246, over 18695623.63 frames., ppl: 9.452368340681396], batch siz2022-06-19 01:17:26,770 INFO [train.py:445] Epoch 23, batch 2000, loss[loss=2.182, over 33600.00 frames., ppl: 8.86590549172802] tot_loss[loss=2.245, over 20324446.44 frames., ppl: 9.437924363620652], batch size: 400
+2022-062022-06-19 01:18:40,048 INFO [2022-06-19 01:18:40,144 INFO [train.py:445] Epoch 23, batch 2200, loss[loss=2.18, over 24000.00 frames., ppl: 8.846638865355255] tot_loss[loss=2.247, over 21077779.13 frames., ppl: 2022-06-19 01:19:55,423 INFO [train.py:445] Epoc2022-06-19 01:19:55,456 INFO [train.py:445] Epoch 23, batch 2400, loss[loss=2.252, over 18400.00 frames., ppl: 9.503439515692984] tot_loss[loss=2.243, over 22657368.99 f2022-06-19 01:21:06,491 INFO [train.py:445] E2022-06-19 01:21:06,529 I2022-06-19 01:21:06,550 INFO [train.py:445] Epoch 23, batch 2600, loss[loss=2.169, over 29200.00 frames., ppl: 8.751753381752403] tot_loss[loss=2.22022-2022-06-19 01:22:17,511 INFO [2022-06-19 01:22:17,538 INFO [train2022-06-19 01:22:17,638 INFO [train.py:445] Epoch 23, batch 2800, loss[loss=2.193, over 21600.00 frames., ppl: 8.958470100529908] tot_loss[loss=2.2520222022-06-19 01:23:29,720 INFO [train.py:445] Epoch 23, batch 3000, loss[loss=2.176, over 26800.00 frames., ppl: 8.814687532774785] tot_loss[loss=2.252, over 24081419.10 frames., ppl: 9.502613137629426], batch size:2022-06-19 01:24:44,995 INFO [train.py:445] Ep2022-06-19 01:24:45,135 INFO [train.py:445] Epoch 23, batch 3200, loss[loss=2.135, over 44400.00 frames., ppl: 8.456984007804541] tot_loss[loss=2.244, over 25917132.58 fram2022-06-19 01:25:59,199 INFO [train2022-06-19 20222022-06-12022-06-19 01:25:59,333 INFO [train.py:445] Epoch 23, batch 3400, loss[loss=2.181, over 22000.00 frames., ppl: 8.856767164526154] tot_loss[loss=2.248, over 262022-02022-06-19 01:27:12,262 INFO [2022-06-19 01:27:12,275 2022-06-2022-06-19 01:27:12,553 INFO [train.py:445] Epoch 23, batch 3600, loss[loss=2.186, over 43617.00 frames., ppl: 8.89969263628011] tot_loss[loss=2.248, 2022-06-19 01:28:24,019 INFO [train.py:445] Epoch 23, batch 3800, loss[loss=2.17, over 51600.00 frames., ppl: 8.759744141651092] tot_loss[loss=2.252, over 26693308.73 frames., ppl: 9.504672928913346], batch size: 400
+2022-062022-06-19 01:29:38,533 INFO 2022-06-19 01:29:38,616 INFO [train.py:445] Epoch 23, batch 4000, loss[loss=2.162, over 31200.00 frames., ppl: 8.692638015741686] tot_loss[loss=2.251, over 27240951.78 frames., ppl:2022-06-2022-06-19 01:30:53,161 INFO [train.py:445] Epoch 2022-06-19 01:30:53,166 INFO [train.py:445] Epoch 23, batch 4200, loss[loss=2.174, over 31600.00 frames., ppl: 8.791184917719425] tot_loss[loss=2.253, over 27462022-06-2022-06-19 01:32:02,286 INFO [train.py:445] Epoch 23, batch 42022-06-19 01:32:02,348 INFO [train.py:445] Epoch 23, batch 4400, loss[loss=2.202, over 23200.00 frames., ppl: 9.042709470425022] tot_loss[loss=2.2472022-06-19 01:33:16,356 INFO [train.py:445] Epoc2022-06-12022-06-19 01:33:16,581 INFO [train.py:445] Epoch 23, batch 4600, loss[loss=2.176, over 39200.00 frames., ppl: 8.809043448656004] tot_loss[loss=2.252, over 284292022-06-2022-06-19 01:34:30,542 INFO [train.py:445] Epoch 23, batch 2022-06-19 01:34:30,584 INFO [train.py:445] Epoch 23, batch 4800, loss[loss=2.286, over 14000.00 frames., ppl: 9.832531495982483] tot_loss[loss=2.252022-06-19 01:35:46,475 INFO [train.py:445] Epoch 23, batch 5000, loss[loss=2.18, over 32400.00 frames., ppl: 8.848196368827507] tot_loss[loss=2.251, over 29094606.61 frames., ppl: 9.498251896161245], batch size: 400
+2022-06-19 01:36:59,017 INFO [train.py:445] Epoch 2022-06-19 01:36:59,085 INFO [train.py:445] Epoch 23, batch 5200, loss[loss=2.288, over 13200.00 frames., ppl: 9.850696563047444] tot_loss[loss=2.252, over 29288093.222022-06-19 01:38:11,594 INFO [train.py:445] Epoch 23, batch 5400, loss[loss=2.183, over 31829.00 frames., ppl: 8.871873471620932] tot_loss[loss=2.252, over 29383485.43 frames., ppl: 9.505102176957903], batch size: 400
+2022-06-19 01:39:22,454 INFO [train.2022-06-19 01:39:22,572 INFO [train.py:445] Epoch 23, batch 5600, loss[loss=2.184, over 41600.00 frames., ppl: 8.885444052248268] tot_loss[loss=2.252, over 29855445.93 frames., ppl2022-06-19 01:40:34,140 INFO [train.py2022-06-19 01:40:34,616 INFO [train.py:445] Epoch 23, batch 5800, loss[loss=2.178, over 69600.00 frames., ppl: 8.825656446396993] tot_loss[loss=2.251, over 30219326.07 frames., ppl2022-06-19 01:41:47,729 INFO [train.py2022-06-19 012022-06-19 01:41:47,877 INFO [train.py:445] Epoch 23, batch 6000, loss[loss=2.175, over 34800.00 frames., ppl: 8.805849509729844] tot_loss[loss=2.251, over 30097983.682022-062022-06-19 01:43:01,636 INFO [train.py:445] Epoch 23, batch 6200, loss[loss=2.179, over 25600.00 frames., ppl: 8.834918252382163] tot_loss[loss=2.254, over 30279115.08 frames., ppl: 9.52169883664813], batch size2022-02022-06-19 01:44:14,710 INFO [train.py:445] Epoch 23, batch 6400, loss[loss=2.182, over 32800.00 frames., ppl: 8.860743000380525] tot_loss[loss=2.254, over 30458631.59 frames., ppl: 9.523516196856656], batch size2022-02022-06-19 01:45:29,325 INFO [t2022-06-19 01:42022-06-19 01:45:29,52022-06-19 01:45:29,648 INFO [train.py:445] Epoch 23, batch 6600, loss[loss=2.175, over 41004.00 frames., ppl: 8.803671813229798] tot_loss[loss=22022-2022-06-19 01:46:39,556 INFO [train.py:445] Epoch 23, batch 6800, loss[loss=2.156, over 64800.00 frames., ppl: 8.635312123367045] tot_loss[loss=2.255, over 30438846.96 frames., ppl: 9.538137388514055], batch size:2022-2022-06-19 01:47:51,418 INFO [train.py:445] Ep2022-06-192022-06-19 02022-06-19 01:47:51,880 INFO [train.py:445] Epoch 23, batch 7000, loss[loss=2.185, over 60000.00 frames., ppl: 8.893026656800012] tot_loss[loss=2022-02022-06-19 01:49:04,117 INFO [tr2022-06-19 01:49:04,123 INFO [train.py:445] Epoch 23, batch 7200, loss[loss=2.157, over 29200.00 frames., ppl: 8.646770465954065] tot_loss[loss=2.253, over 30994806.56 frames., ppl2022-06-19 01:50:17,767 INFO [train.py:445] Epoch 232022-06-19 01:50:17,82022-06-19 01:50:17,912 INFO [train.py:445] Epoch 23, batch 7400, loss[loss=2.176, over 28800.00 frames., ppl: 8.814361487642715] tot_loss[loss=2.2022-2022-06-19 01:51:32,482 INFO [tr2022-06-19 01:51:32,490 INFO [train.py:445] Epoch 23, batch 7600, loss[loss=2.176, over 35200.00 frames., ppl: 8.810053871018921] tot_loss[loss=2.253, over 31224877.60 frames., ppl:2022-2022-06-19 01:52:44,034 INFO [train.py:445] E2022-06-19 01:52:44,402 INFO [train.py:445] Epoch 23, batch 7800, loss[loss=2.165, over 62000.00 frames., ppl: 8.714364728293083] tot_loss[loss=2.252, over 31401221.71 20222022-06-19 01:53:57,482 INFO [tra2022-06-19 01:53:57,685 INFO [train.py:445] Epoch 23, batch 8000, loss[loss=2.14, over 34400.00 frames., ppl: 8.501922270741483] tot_loss[loss=2.25, over 31904159.54 frames., ppl: 9.2022-06-19 01:55:09,636 INFO [train.py:445] Epoch 23, batch 8200, loss[loss=2.166, over 43600.00 frames., ppl: 8.724689005587571] tot_loss[loss=2.255, over 31310221.18 frames., ppl: 9.530667674021446], batch size: 400
+2022-06-19 01:56:21,967 INFO [train.py:445] Epo2022-06-19 01:56:22,100 INFO [train.py:445] Epoch 23, batch 8400, loss[loss=2.163, over 39600.00 frames., ppl: 8.698400903277147] tot_loss[loss=2.253, over 31350557.96 fra2022-06-19 01:57:36,145 INFO [train.py:445] Epo2022-06-19 01:57:36,232022-06-19 01:57:36,251 INFO [train.py:445] Epoch 23, batch 8600, loss[loss=2.151, over 34800.00 frames., ppl: 8.596314053296824] tot_loss[loss=2.220222022-06-19 01:58:47,184 INFO [train.py:445] E2022-06-19 02022-06-192022-06-19 01:58:47,381 INFO [train.py:445] Epoch 23, batch 8800, loss[loss=2.126, over 32800.00 frames., ppl: 8.384444501919251] tot_loss[loss=2.2022-06-19 01:59:57,469 INFO [train.py:445] Epoch 23, batch 9000, loss[loss=2.168, over 30400.00 frames., ppl: 8.739417989029683] tot_loss[loss=2.255, over 31383573.83 frames., ppl: 9.538675704935846], batch size: 400
+2022-2022-06-19 02:01:09,547 INFO [t2022-06-19 022022-06-19 02:01:09,731 INFO [train.py:445] Epoch 23, batch 9200, loss[loss=2.216, over 26400.00 frames., ppl: 9.172526011603921] tot_loss[loss=2.253, over 31734537.13 f20222022-06-19 02:02:22,256 INFO [tr2022-06-19 02:02:22,348 INFO [train.py:445] Epoch 23, batch 9400, loss[loss=2.202, over 21200.00 frames., ppl: 9.04454067624448] tot_loss[loss=2.253, over 32242347.74 frames., ppl: 9.2022-06-19 02:03:36,850 INFO [train.py:445] Epoc2022-06-19 02:03:36,887 INFO [train.py:445] Epoch 23, batch 9600, loss[loss=2.16, over 30000.00 frames., ppl: 8.673951718512162] tot_loss[loss=2.253, over 31808021.06 fr20222022-06-19 02:04:46,627 INFO [2022-06-19 02:04:46,682 INFO [train.p2022-06-19 02:04:46,700 INFO [train.py:445] Epoch 23, batch 9800, loss[loss=2.2, over 22400.00 frames., ppl: 9.027815364940588] tot_loss[loss=2.252022-2022-06-19 02:05:59,686 INFO [train.py:445] Epoch 23, batch 10000, loss[loss=2.174, over 45600.00 frames., ppl: 8.797084867877915] tot_loss[loss=2.254, over 31832056.32 frames., ppl: 9.530118585572373], batch size: 400
+2022-06-19 02:05:59,687 INFO [train.py:469] Computing validation 2022-2022-06-19 02:05:59,868 INFO [train.py:480]2022-06-19 02:05:59,868 INFO [train.py:480] Epoch 23, validation: loss=2.321, 2022-06-19 02:07:13,026 INFO [train.py:445] Epoch 23, batch 12022-06-19 02:07:13,256 INFO [train.py:445] Epoch 23, batch 10200, loss[loss=2.175, over 33600.00 frames., ppl: 8.80240411528753] tot_loss[loss=2.257, over 312022-06-19 02:08:26,100 INFO [trai2022-06-19 02:08:26,264 IN2022-06-19 02:08:26,475 INFO [train.py:445] Epoch 23, batch 10400, loss[loss=2.17, over 47600.00 frames., ppl: 8.754596241195534] tot_loss[loss=2.257, over 31520222022-06-19 02:09:41,278 INFO [train.py:445]22022-06-12022-06-19 02:09:41,373 INFO [train.py:445] Epoch 23, batch 10600, loss[loss=2.274, over 15200.00 frames., ppl: 9.722712949467597] tot_loss[loss=2.257, over 314420222022-06-19 02:10:51,042 INFO [train.py:445] Epoch 23, 2022-06-19 02:10:51,167 INFO [train.py:445] Epoch 23, batch 10800, loss[loss=2.191, over 43600.00 frames., ppl: 8.948239243744888] tot_loss[loss=2.258, over 31432022022-06-19 02:12:05,354 INFO 2022-06-19 02:12:05,866 INFO [train.py:445] Epoch 23, batch 11000, loss[loss=2.178, over 61600.00 frames., ppl: 8.832360276192784] tot_loss[loss=2.255, over 31974625.66 frames., ppl: 9.532022-06-19 02:13:19,721 INFO [tr2022-06-19 02:132022-06-19 02:13:19,824 INFO [train.py:445] Epoch 23, batch 11200, loss[loss=2.195, over 26000.00 frames., ppl: 8.98017644917782] tot_loss[loss=2.256, over 31523638.51 fra202022-06-19 02:14:33,743 INFO [train.py:445] Epoch 23, batch 11400, l2022-06-19 02:14:33,835 INFO [train.py:445] Epoch 23, batch 11400, loss[loss=2.19, over 31600.00 frames., ppl: 8.93849638358914] tot_loss[loss=2.252022022-06-19 02:15:47,454 INFO [train.py:445] Ep2022-06-19 02:15:47,537 INFO [train.py:445] Epoch 23, batch 11600, loss[loss=2.207, over 24000.00 frames., ppl: 9.089839275432531] tot_loss[loss=2.256, over 32016347.872022022-06-19 02:17:04,743 INFO [train.py:445] Epoch 23, batch 11800, loss[loss=2.167, over 43200.00 frames., ppl: 8.728263851927293] tot_loss[loss=2.257, over 31649437.11 frames., ppl: 9.558009272978783], batch size2022-06-19 02:18:20,809 INFO [train2022-06-19 02:18:202022-06-19 02:18:21,033 INFO [train.py:445] Epoch 23, batch 12000, loss[loss=2.165, over 30800.00 frames., ppl: 8.71713991901822] tot_loss[loss=2.254, over 325808452022-062022-06-19 02:19:32,967 INFO [train.py:445] Epo2022-06-19 02:19:32,979 INFO [train.py:445] Epoch 23, batch 12200, loss[loss=2.289, over 12400.00 frames., ppl: 9.868236172538364] tot_loss[loss=2.254, over 325635832022-062022-06-19 02:20:44,490 INFO [train.py:445] Ep2022-06-19 02:20:44,582 INFO [train.py:445] Epoch 23, batch 12400, loss[loss=2.198, over 40400.00 frames., ppl: 9.008774655095298] tot_loss[loss=2.255, over 319974612022-06-2022-06-19 02:21:56,710 INFO 2022-06-19 02:21:56,732 INFO [train.py:445] Epoch 23, batch 12600, loss[loss=2.181, over 29200.00 frames., ppl: 8.853783902316124] tot_loss[loss=2.257, over 31950457.50 frames., ppl:2022-06-2022-06-19 02:22:25,951 INFO [train.py:445] Epoch 24, batch 0, loss[loss=2.141, over 45600.00 frames., ppl: 8.506379164703779] tot_loss[loss=2.141, over 45600.00 frames., ppl: 8.506379164703779], batch 2022-06-192022-06-19 02:23:36,941 INFO 2022-06-19 02:23:37,332 INFO [train.py:445] Epoch 24, batch 200, loss[loss=2.153, over 57600.00 frames., ppl: 8.608761730072233] tot_loss[loss=2.251, over 2909613.60 frames., 2022-06-19 022022-06-19 02:24:51,074 INFO [train.py:445] Epoch 24, ba2022-06-19 02:24:51,363 INFO [train.py:445] Epoch 24, batch 400, loss[loss=2.149, over 54000.00 frames., ppl: 8.576178567879012] tot_loss[loss=2.242022-06-19 02:26:03,348 INFO [train.py:42022-06-19 02:26:2022-06-19 02:26:03,466 INFO [train.py:445] Epoch 24, batch 600, loss[loss=2.189, over 25600.00 frames., ppl: 8.925595379853007] tot_loss[loss=2.244, over 7952022-06-19 02:2022-06-19 02:27:20,709 INF2022-06-19 02:2022022-06-19 02:27:21,007 INFO [train.py:445] Epoch 24, batch 800, loss[loss=2.138, over 34400.00 frames., ppl: 8.483697223388587] tot_loss[loss=2.242, over 1032022-06-19 02:2022-06-19 02:28:33,836 INFO2022-06-19 0220222022-06-19 2022-06-19 02:28:34,125 INFO [train.py:445] Epoch 24, batch 1000, loss[loss=2.169, over 43416.00 frames., ppl: 8.75389412244952] tot_loss[loss=2.2432022-06-19 02:29:48,337 INFO [train.py:442022-06-19 02:29:48,346 INFO [train.py:445] Epoch 24, batch 1200, loss[loss=2.162, over 24800.00 frames., ppl: 8.692227868804787] tot_loss[loss=2.244, over 14073307.25 frames., 2022-06-19 022022-06-19 02:30:59,391 INFO [train.py:445] Epoch 24, ba2022-06-19 02:30:59,396 INFO [train.py:445] Epoch 24, batch 1400, loss[loss=2.168, over 31200.00 frames., ppl: 8.73759706355877] tot_loss[loss=2.245,2022-06-19 022022-06-19 02:32:14,578 INF2022-06-19 02:32:14,645 INFO2022-06-19 02:32:14,886 INFO [train.py:445] Epoch 24, batch 1600, loss[loss=2.143, over 46000.00 frames., ppl: 8.525538909933465] tot_loss[loss=2.245,2022-06-19 02:33:27,815 INFO [train.py:445] Epoch 24, batc2022-06-19 02:33:27,865 INFO [train.py:445] Epoch 24, batch 1800, loss[loss=2.107, over 36400.00 frames., ppl: 8.222452819802829] tot_loss[loss=2.244, over 18772022-06-19 2022-06-19 02:34:39,992 INF2022-06-19 02:34:2022022-06-19 02:34:40,374 INFO [train.py:445] Epoch 24, batch 2000, loss[loss=2.169, over 54000.00 frames., ppl: 8.745495546645323] tot_loss[loss=2.246, over 19862022-06-19 02:35:53,619 INFO [train.py:445] Epoch 24, batch 2200, loss[loss=2.165, over 31200.00 frames., ppl: 8.718731507473974] tot_loss[loss=2.247, over 21364173.56 frames., ppl: 9.455137638896982], batch size: 400
+2022-06-192022-06-19 02:37:06,290 INFO [train.py:445] Epoch 24, batch 2400, loss[loss=2.175, over 52800.00 frames., ppl: 8.798224092659343] tot_loss[loss=2.246, over 22145243.13 frames., ppl: 9.448184561364108], batch2022-06-19 2022-06-19 02:38:21,188 INFO [train.py:445] Epoch 24, batch 2600, loss[loss=2.157, over 45600.00 frames., ppl: 8.648630399901782] tot_loss[loss=2.246, over 23051663.18 frames., ppl: 9.448690888512173], batch2022-06-19 02:39:34,698 INFO [train.py:445] Epoch 24, batch 2800, loss[loss=2.156, over 63600.00 frames., ppl: 8.639267564573663] tot_loss[loss=2.246, over 24359598.59 frames., ppl: 9.454355336910186], batch size: 400
+2022-06-19 02:40:45,937 INFO [train.py:445] Epoch 24, batch 3000, loss[loss=2.155, over 46000.00 frames., ppl: 8.627517992005883] tot_loss[loss=2.246, over 25203577.30 frames., ppl: 9.448434069235214], batch size: 400
+2022-06-19 02:41:58,747 INFO [train.py:445] Epoch 24, batch 3200, loss[loss=2.206, over 21200.00 frames., ppl: 9.078628436440999] tot_loss[loss=2.246, over 25888226.38 frames., ppl: 9.450539970009864], batch size: 400
+2022-06-12022-06-19 02:43:16,629 INFO [train.py:445] Epoch 24, batch 3400, loss[loss=2.174, over 49200.00 frames., ppl: 8.793772167512468] tot_loss[loss=2.245, over 26356832.01 frames., ppl: 9.440871585881322], batch s2022-06-19 02:44:31,088 INFO [train.py:445] Epoch 24, batch 3600, loss[loss=2.161, over 58800.00 frames., ppl: 8.677489186303443] tot_loss[loss=2.247, over 26996384.06 frames., ppl: 9.459391006488037], batch size: 400
+2022-06-2022-06-19 02:45:43,461 INFO [train.py:445]2022-2022-06-19 02:45:43,484 INFO [train.py:445] Epoch 24, batch 3800, loss[loss=2.258, over 15200.00 frames., ppl: 9.564542856493244] tot_loss[loss=2.245, over 27462022-06-12022-06-19 02:46:55,615 INFO [train.py:445] Epoch 24, batch 4000, loss[loss=2.151, over 56800.00 frames., ppl: 8.593047577635405] tot_loss[loss=2.247, over 27672182.67 frames., ppl: 9.460640506052183], batch 2022-06-192022-06-19 02:48:05,930 INF2022-06-19 02:48:2022-06-19 02:48:06,337 INFO [train.py:445] Epoch 24, batch 4200, loss[loss=2.158, over 55600.00 frames., ppl: 8.64974090340242] tot_loss[loss=2.245, over 2867422022-06-19 02:49:17,750 INFO [train.py:445] Epoch 24, ba2022-06-19 02:49:17,896 INFO [train.py:445] Epoch 24, batch 4400, loss[loss=2.185, over 31600.00 frames., ppl: 8.888371671343569] tot_loss[loss=2.244, over 292982022-06-12022-06-19 02:50:29,489 INFO [tr2022-06-19 02:50:29,903 INFO [train.py:445] Epoch 24, batch 4600, loss[loss=2.155, over 58000.00 frames., ppl: 8.624052411899942] tot_loss[loss=2.249, over 28800145.87 frames2022-06-19 02:51:41,584 INFO [train.py:445] Epoch 24, batch 4800, loss[loss=2.197, over 39200.00 frames., ppl: 8.998224355434447] tot_loss[loss=2.252, over 28519082.89 frames., ppl: 9.50781822024918], batch size: 400
+2022-06-19 022022-06-19 02:52:53,100 INFO [train.py:445] Epoch 24, batch 5000, loss[loss=2.221, over 46029.00 frames., ppl: 9.215428520427325] tot_loss[loss=2.25, over 29352609.40 frames., ppl: 9.48400239844624], batch2022-06-19 02:54:05,900 INFO [train.py:445] Epoch 24, batc2022-06-19 02:54:05,2022-06-19 02:54:06,039 INFO [train.py:445] Epoch 24, batch 5200, loss[loss=2.183, over 35600.00 frames., ppl: 8.875657456462244] tot_loss[l2022-06-19 2022-06-19 02:55:16,951 INFO [train.py:445] Epoch 24, batch 5400, loss[loss=2.151, over 33200.00 frames., ppl: 8.590015385668174] tot_loss[loss=2.25, over 29952908.49 frames., ppl: 9.483320069212372], batch s2022-06-12022-06-19 02:56:30,408 INFO [tr2022-06-19 02:56:302022-06-19 02:56:32022-06-19 02:56:31,269 INFO [train.py:445] Epoch 24, batch 5600, loss[loss=2.234, over 64311.00 frames., ppl: 9.33501797164635] tot_loss[lo2022-06-2022-06-19 02:57:42,717 INFO [tr2022-06-19 02:57:42,746 INFO [train2022-06-19 02:57:42,767 INFO [train.py:445] Epoch 24, batch 5800, loss[loss=2.229, over 15200.00 frames., ppl: 9.291817742353253] tot_loss[loss2022-06-19 02:58:55,553 INFO [train.py:445] Epoch 24, batch 6000, loss[loss2022-06-19 02:58:55,708 INFO [train.py:445] Epoch 24, batch 6000, loss[loss=2.147, over 33600.00 frames., ppl: 8.556842932295554] tot_loss[los2022-06-2022-06-19 03:00:08,299 INFO [2022-06-19 03:00:08,546 INFO [train.py:445] Epoch 24, batch 6200, loss[loss=2.166, over 73600.00 frames., ppl: 8.72620221221441] tot_loss[loss=2.251, over 30577633.51 frames., ppl:2022-06-2022-06-19 03:01:23,221 INFO 2022-06-19 03:01:23,277 INFO [train.py:445] Epoch 24, batch 6400, loss[loss=2.161, over 30800.00 frames., ppl: 8.676992388108003] tot_loss[loss=2.252, over 30516960.03 frames., pp2022-06-12022-06-19 03:02:38,482 INFO [train.py:445] Epoch 24, batch 6600, lo2022-06-19 03:02:38,946 INFO [train.py:445] Epoch 24, batch 6600, loss[loss=2.152, over 79600.00 frames., ppl: 8.599268449918837] tot_loss[l2022-062022-06-19 03:03:51,836 INFO [tra2022-06-19 03:03:52022-06-19 03:03:51,929 INFO [train.py:445] Epoch 24, batch 6800, loss[loss=2.24, over 17600.00 frames., ppl: 9.390146234022849] tot_loss[loss=2.25, over 3103562022-02022-06-19 03:05:06,607 INFO [train.py:445] Epoc2022-06-19 03:05:06,72022-06-19 03:05:06,736 INFO [train.py:445] Epoch 24, batch 7000, loss[loss=2.19, over 23600.00 frames., ppl: 8.93449437480905] tot_loss[loss=2022-02022-06-19 03:06:19,427 INFO [train2022-06-19 03:06:19,445 INFO [train2022-06-19 03:06:19,459 INFO [train.py:445] Epoch 24, batch 7200, loss[loss=2.389, over 10800.00 frames., ppl: 10.898388223025881] tot_loss[lo2022-06-19 03:07:33,084 INFO [train.py:445] Epoch 24, b2022-06-19 03:07:33,092022-06-19 03:07:33,110 INFO [train.py:445] Epoch 24, batch 7400, loss[loss=2.197, over 20400.00 frames., ppl: 9.000665869046298] tot_loss[lo2022-2022-06-19 03:08:46,546 INFO [train.py:445] Epoch 24, batch 7600, loss[2022-06-19 03:08:46,875 INFO [train.py:445] Epoch 24, batch 7600, loss[loss=2.132, over 60000.00 frames., ppl: 8.432609303164089] tot_loss[los2022-06-19 03:09:59,709 INFO [train.py:445] Epoch2022-06-19 03:09:59,926 INFO [train.py:445] Epoch 24, batch 7800, loss[loss=2.202, over 38800.00 frames., ppl: 9.042694119400005] tot_loss[loss=2.25, over 31805841.37 fr2022-2022-06-19 03:11:13,698 INFO [train.py:445] Epoch 24, batch 8000, loss[loss=2.204, over 33200.00 frames., ppl: 9.06145792837462] tot_loss[loss=2.252, over 31379429.42 frames., ppl: 9.508181745616985], batch size: 2022-06-19 03:12:23,334 INFO [train.py:2022-06-19 03:12:23,354 IN2022-06-19 03:12:23,513 INFO [train.py:445] Epoch 24, batch 8200, loss[loss=2.171, over 26400.00 frames., ppl: 8.764633712808209] tot_loss[loss=2.248, o2022-06-19 03:13:33,150 INFO [train.py:445] Epoch 2022-06-19 03:132022-06-19 03:13:33,509 INFO [train.py:445] Epoch 24, batch 8400, loss[loss=2.176, over 65540.00 frames., ppl: 8.812585014709185] tot_loss[loss=2.25, ov22022-06-19 03:14:43,582 INFO [train.py2022-06-19 03:14:43,670 INFO [train.py:445] Epoch 24, batch 8600, loss[loss=2.161, over 31200.00 frames., ppl: 8.682155103346702] tot_loss[loss=2.258, over 30558118.61 frames., p2022-06-19 03:15:52,483 INFO [train.py:445] Epoch 24, batch 8800, loss[loss=2.19, over 25600.00 frames., ppl: 8.933339992149397] tot_loss[loss=2.255, over 30935126.33 frames., ppl: 9.539492884075823], batch size: 400
+202022-06-19 03:17:03,247 INFO [train.py:445] Epoch 24, batch 9000, loss[loss=2.192, over 35200.00 frames., ppl: 8.954597441082205] tot_loss[loss=2.253, over 31661351.19 frames., ppl: 9.515081675705792], batch size: 40022022-06-19 03:18:13,132 INFO [train.py:445] Epoch 24, batch 9200, loss[loss=2.182, over 34800.00 frames., ppl: 8.860143394742927] tot_loss[loss=2.253, over 31710159.50 frames., ppl: 9.51410585915301], batch size: 4002022-06-19 03:19:25,295 INFO [train.p2022-06-19 032022-06-19 03:19:25,435 INFO [train.py:445] Epoch 24, batch 9400, loss[loss=2.193, over 28800.00 frames., ppl: 8.966197227826125] tot_loss[loss=2.255, over 31144271.20222022-06-19 03:20:40,087 INFO [train2022-06-19 03:20:40,221 INFO [train.py:445] Epoch 24, batch 9600, loss[loss=2.167, over 35200.00 frames., ppl: 8.731569490315845] tot_loss[loss=2.259, over 30633028.92 frames.,2022-06-19 03:21:51,640 INFO [train.py:445] Epoch 24, ba2022-06-19 03:21:51,813 INFO [train.py:445] Epoch 24, batch 9800, loss[loss=2.153, over 42400.00 frames., ppl: 8.614678355704674] tot_loss[loss=2.254, over 3161582022-06-19 03:23:06,655 INFO [train.py:445] Epoch 242022-06-19 03:23:06,980 INFO [train.py:445] Epoch 24, batch 10000, loss[loss=2.165, over 59200.00 frames., ppl: 8.717734059637424] tot_loss[loss=2.252, over 31979829.62 frames., ppl: 9.509379512597455], batch size: 400
+2022-06-19 03:23:02022-062022-06-19 03:23:07,162 INFO [train.py:480] E2022022-06-19 03:23:07,162 INFO [train.py:480] Epoch 24, validation: loss=2022-062022-06-19 03:24:19,570 INFO [train2022-06-192022022-06-19 03:24:19,707 INFO [train.py:445] Epoch 24, batch 10200, loss[loss=2.182, over 31600.00 frames., ppl: 8.866130271451157] tot_loss[loss=2.254, over 31787412022-062022-06-19 03:25:31,952 INFO [train2022-06-19 03:25:31,996 INFO [train.2022-06-19 03:25:32,140 INFO [train.py:445] Epoch 24, batch 10400, loss[loss=2.166, over 41200.00 frames., ppl: 8.727052443377056] tot_loss[l2022-02022-06-19 03:26:44,211 INFO [train.py:445]2022-06-19 03:26:44,231 INFO [train.py:445] Epoch 24, batch 10600, loss[loss=2.199, over 24800.00 frames., ppl: 9.01471470473708] tot_loss[loss=2.253, over 32154528.31 f20222022-06-19 03:27:55,070 INFO [train.2022-06-19 03:27:55,148 INFO [train.py:445] Epoch 24, batch 10800, loss[loss=2.158, over 31200.00 frames., ppl: 8.65514590588857] tot_loss[loss=2.258, over 30962875.82 frames., pp2022-06-19 03:29:09,306 INFO [train.py:445] Epoch 24, batch 11000, loss[loss=2.148, over 37600.00 frames., ppl: 8.570626918742846] tot_loss[loss=2.255, over 31785534.21 frames., ppl: 9.530608278907348], batch size: 400
+2022022-06-19 03:30:20,781 INFO [train.py:445] Epoch 24, batch 11200, loss[loss=2.247, over 20000.00 frames., ppl: 9.45937809697201] tot_loss[loss=2.252, over 32332991.01 frames., ppl: 9.511334535187187], batch size: 40202022-06-19 03:31:32,153 INFO [train.py:445] Epoch 24, batch 11400, loss[loss=2.347, over 13200.00 frames., ppl: 10.4587540383973] tot_loss[loss=2.254, over 32120121.56 frames., ppl: 9.524183921581471], batch size: 402022-06-19 03:32:43,677 INFO [train.py:2022-06-19 03:32:44,044 INFO [train.py:445] Epoch 24, batch 11600, loss[loss=2.197, over 54400.00 frames., ppl: 8.995271722683707] tot_loss[loss=2.257, over 31300795.36 frames., p2022-06-19 03:33:57,603 INFO [train.py:445] Epoch 24, batch 11800, loss[loss=2.168, over 64800.00 frames., ppl: 8.744879807158298] tot_loss[loss=2.256, over 31710696.11 frames., ppl: 9.548986528313975], batch size: 400
+2022-06-19 03:35:10,740 INFO [train.py:445] Epoch 22022-06-19 03:35:10,824 INFO [train.py:445] Epoch 24, batch 12000, loss[loss=2.204, over 22000.00 frames., ppl: 9.058438307482586] tot_loss[loss=2.255, over 31972955.61 2022-06-19 03:36:25,479 INFO [train.py:2022-06-1202022-06-19 2022-06-19 03:36:25,788 INFO [train.py:445] Epoch 24, batch 12200, loss[loss=2.185, over 38800.00 frames., ppl: 8.888386079582032] tot_loss[loss=2.258, over 2022-06-19 03:37:38,323 INFO [train.py:445] Epoch 22022-06-19 2022-06-19 03:37:38,754 INFO [train.py:445] Epoch 24, batch 12400, loss[loss=2.17, over 57600.00 frames., ppl: 8.754908627536423] tot_loss[loss=2.257, over 312022-06-19 03:38:53,000 INFO [train.py:445] Epoch 24, batch 2022-06-19 03:38:53,394 INFO [train.py:445] Epoch 24, batch 12600, loss[loss=2.183, over 69600.00 frames., ppl: 8.87694313111036] tot_loss[loss=2.257, over 312022-06-19 03:39:19,072 INFO [train.py2022-06-19 03:39:19,132022-06-19 03:39:19,248 INFO [train.py:445] Epoch 25, batch 0, loss[loss=2.162, over 24000.00 frames., ppl: 8.684985265318828] tot_loss[loss=2.162, ove22022-06-19 03:40:39,748 INFO [train.py:445] Epoch 25, batch 200, loss[loss=2.203, over 19600.00 frames., ppl: 9.052098884718669] tot_loss[loss=2.241, over 3008977.27 frames., ppl: 9.404268266990403], batch size: 40202022-06-19 03:41:53,535 INFO [tr2022-06-19 03:41:54,013 INFO [train.py:445] Epoch 25, batch 400, loss[loss=2.166, over 65600.00 frames., ppl: 8.722277066975348] tot_loss[loss=2.23, over 6108883.38 frames., ppl: 9.32022-06-19 03:43:06,726 INFO [tra2022-06-19 032022-06-19 03:43:07,144 INFO [train.py:445] Epoch 25, batch 600, loss[loss=2.113, over 58800.00 frames., ppl: 8.269858949701245] tot_loss[loss=2.235, over 8463093.09 fram2022-06-19 03:44:21,246 INFO [train.py:445] Epoch 25, batch 800, loss[loss=2.162, over 28800.00 frames., ppl: 8.688011071575694] tot_loss[loss=2.239, over 10656229.73 frames., ppl: 9.38120438309581], batch size: 400
+2022-06-19 03:45:32,746 INFO [tra2022-06-19 03:45:32,787 INFO [train.py:445] Epoch 25, batch 1000, loss[loss=2.175, over 27600.00 frames., ppl: 8.804709434923135] tot_loss[loss=2.237, over 12728297.03 frames., ppl: 9.320222022-06-19 03:46:44,992 INFO [train.py:442022-06-19 03:46:45,009 INFO [train.py:445] Epoch 25, batch 1200, loss[loss=2.146, over 28400.00 frames., ppl: 8.554231359174661] tot_loss[loss=2.239, over 14758201.73 frame2022-06-19 03:47:58,254 INFO [tr2022-06-19 03:47:58,463 INFO [train.py:445] Epoch 25, batch 1400, loss[loss=2.169, over 29200.00 frames., ppl: 8.750933879861204] tot_loss[loss=2.237, over 16414456.24 frames., ppl: 9.3622022-06-19 03:49:11,762 INFO [train.py:445] Epoch 25, batch 1600, loss[loss=2.182, over 23600.00 frames., ppl: 8.859978100637063] tot_loss[loss=2.241, over 17674370.01 frames., ppl: 9.398579100592524], batch size: 40022022-06-19 03:50:27,795 INFO [train.py:445] Epoch 25, batch 1800, loss[loss=2.154, over 42800.00 frames., ppl: 8.619586620166341] tot_loss[loss=2.241, over 19051740.84 frames., ppl: 9.404052883616068], batch size: 2022-06-19 03:51:38,315 INFO [train.py:445]2022-06-19 03:51:38,52022-06-19 03:51:38,708 INFO [train.py:445] Epoch 25, batch 2000, loss[loss=2.202, over 52800.00 frames., ppl: 9.043951630890321] tot_loss[loss=2.246, over2022-06-19 03:52:51,507 INFO [train.py:445] Epoch 25, batch 2200, loss[loss=2.15, over 28800.00 frames., ppl: 8.586521315618187] tot_loss[loss=2.248, over 20923239.55 frames., ppl: 9.468855492291173], batch size: 400
+20222022-06-19 03:54:03,056 INFO [train.py:2022-06-19 03:54:03,095 INFO 2022-06-19 03:54:03,364 INFO [train.py:445] Epoch 25, batch 2400, loss[loss=2.183, over 47600.00 frames., ppl: 8.868519559389735] tot_loss[loss=2.20222022-06-19 03:55:17,637 INFO [train.py:445] Epoch 25, batch 2600, lo2022-06-19 03:55:17,938 INFO [train.py:445] Epoch 25, batch 2600, loss[loss=2.2, over 48305.00 frames., ppl: 9.026922910721808] tot_loss[loss=2.242022-06-19 03:56:28,834 INFO [train.py:445] Epoch 25, batch 2800, loss[loss=2.171, over 39200.00 frames., ppl: 8.763766665643423] tot_loss[loss=2.248, over 23526303.20 frames., ppl: 9.467724964237014], batch size: 400
+2022-06-19 03:57:39,691 INFO [train.py:2022-06-19 03:57:40,239 INFO [train.py:445] Epoch 25, batch 3000, loss[loss=2.198, over 76000.00 frames., ppl: 9.004011770204617] tot_loss[loss=2.245, over 24543083.38 frames., 2022-2022-06-19 03:58:55,036 IN2022-06-19 03:58:55,055 INFO 2022-06-192022-06-19 03:58:55,112 INFO [train.py:445] Epoch 25, batch 3200, loss[loss=2.227, over 22400.00 frames., ppl: 9.27273625024913] tot_loss[loss=2.2472022-06-19 04:00:04,924 INFO [train.py:445] Epoch 25, batch 3400, loss[loss=2.175, over 41200.00 frames., ppl: 8.803720831516747] tot_loss[loss=2.249, over 25606804.34 frames., ppl: 9.47678543140856], batch size: 400
+2022-06-19 04:01:20,407 INFO [train.py:445] Epoch 25, batch 3600, loss[loss=2.179, over 28400.00 frames., ppl: 8.835137407043542] tot_loss[loss=2.249, over 26287610.53 frames., ppl: 9.47356848341214], batch size: 400
+2022-02022-06-19 04:02:30,780 INFO [train.py:445] Epoch 25, ba2022-06-19 04:02:30,927 INFO [train.py:445] Epoch 25, batch 3800, loss[loss=2.151, over 31600.00 frames., ppl: 8.597436711737137] tot_loss[loss=2.246, over2022-062022-06-19 04:03:46,245 2022-06-19 04:03:46,349 INFO [t2022-06-12022-06-19 04:03:46,582 INFO [train.py:445] Epoch 25, batch 4000, loss[loss=2.158, over 40803.00 frames., ppl: 8.652682970150085] tot_loss[loss=2.22022-06-19 04:05:02,888 INFO [train.py:445] Epoch 25, batch 4200, loss[loss=2.15, over 52400.00 frames., ppl: 8.587306004086358] tot_loss[loss=2.248, over 27899761.64 frames., ppl: 9.468725523369361], batch size: 400
+2022-06-19 04:06:17,922 INFO [train.py:445] Epoch 2022-06-19 02022-06-19 04:06:18,027 INFO [train.py:445] Epoch 25, batch 4400, loss[loss=2.191, over 26000.00 frames., ppl: 8.944467245308726] tot_loss[loss=2.248, over 2022-2022-06-19 04:07:27,251 2022-06-19 04:07:27,291 INFO [tra2022-06-19 04:07:27,328 INFO [train.py:445] Epoch 25, batch 4600, loss[loss=2.226, over 19200.00 frames., ppl: 9.262090405171318] tot_loss[loss=2.248, over 2022-06-19 04:08:38,524 INFO [train.py:445] 2022-2022-06-19 04:08:38,762 INFO [train.py:445] Epoch 25, batch 4800, loss[loss=2.154, over 52400.00 frames., ppl: 8.62167671406371] tot_loss[loss=2.247, over 29122580.56 fr2022-06-19 04:09:51,839 INF2022-06-19 04:092022-2022-06-19 04:09:51,8672022-06-19 04:09:51,911 INFO [train.py:445] Epoch 25, batch 5000, loss[loss=2.204, over 21600.00 frames., ppl: 9.059045345171823] tot_loss[loss=2.22022-06-19 04:11:05,617 INFO [train.py:445] Epoch 25, batch 5200, loss[loss=2.209, over 44622.00 frames., ppl: 9.10254401371261] tot_loss[loss=2.249, over 29290187.44 frames., ppl: 9.481843629303187], batch size: 201
+20222022-06-19 04:12:18,231 INFO [train.py:445] E2022-06-19 04:12:18,320 INFO [train.py:445] Epoch 25, batch 5400, loss[loss=2.172, over 37200.00 frames., ppl: 8.77838734190236] tot_loss[loss=2.248, over 29886799.32 fr2022-06-19 04:13:27,337 INFO [train.py:445] Epoch 25, batch 2022-06-19 04:13:27,398 INFO [train.py:445] Epoch 25, batch 5600, loss[loss=2.247, over 17200.00 frames., ppl: 9.461011716562632] tot_loss[loss=2.25, over 29620222022-06-19 04:14:40,735 INFO [train.py:445] Epoch 25, b2022-06-19 04:14:40,805 INFO [train.py:445] Epoch 25, batch 5800, loss[loss=2.151, over 21200.00 frames., ppl: 8.590899914215074] tot_loss[loss=2.249, over 30092022022-06-19 04:15:2022-06-19 04:15:54,943 INFO [train.py:445] Epoch 25, batch 6000, loss[loss=2.162, over 29200.00 frames., ppl: 8.690420658068975] tot_loss[loss=2.249, over 30129605.68 frames., ppl: 9.482982957482082022022-06-19 04:172022-06-19 04:17:04,622022-06-19 04:12022-06-19 04:17:04,682 INFO [train.py:445] Epoch 25, batch 6200, loss[loss=2.212, over 22800.00 frames., ppl: 9.134553549054136] tot_loss[loss=2.25, over 304832022-06-19 04:18:212022-06-19 04:18:22,399 INFO [train.py:445] Epoch 25, batch 6400, loss[loss=2.152, over 74000.00 frames., ppl: 8.599855103864948] tot_loss[loss=2.248, over 30648374.64 frames., ppl: 9.4730619552832022-06-19 04:19:36,383 INFO [train.py:445] Epoc2022-06-19 04:19:36,640 INFO [train.py:445] Epoch 25, batch 6600, loss[loss=2.203, over 38400.00 frames., ppl: 9.049809734909791] tot_loss[loss=2.251, over 30630881.58 fr22022-06-19 04:20:53,880 INFO [train.py:442022-06-19 04:20:2022-06-19 04:20:54,288 INFO [train.py:445] Epoch 25, batch 6800, loss[loss=2.199, over 66400.00 frames., ppl: 9.016934192167058] tot_loss[loss=2.249, over 3132022-06-19 04:22:02022-06-19 04:22:04,163 INFO [train.py:445] Epoch 25, batch 7000, loss[loss=2.152, over 59600.00 frames., ppl: 8.604208707309896] tot_loss[loss=2.249, over 31019769.94 frames., ppl: 9.47562393521110202022-06-19 04:23:12022-06-19 04:23:14,8762022-06-19 04:23:14,937 INFO [train.py:445] Epoch 25, batch 7200, loss[loss=2.198, over 25200.00 frames., ppl: 9.007627123268856] tot_loss[loss=2.251, over 30906632.90 frames.2022-06-19 04:24:30,2022-06-19 04:24:30,582 INFO [train.py:445] Epoch 25, batch 7400, loss[loss=2.184, over 33600.00 frames., ppl: 8.885581203787119] tot_loss[loss=2.249, over 31166874.77 frames., ppl: 9.48247505302882022022-06-19 04:25:42,282 INFO [train.py:445] Epoch 25, batch 7600, loss[loss=2.217, over 22000.00 frames., ppl: 9.179039644097747] tot_loss[loss=2.252, over 31089244.79 frames., ppl: 9.508191548447112], batch size: 42022-06-19 04:26:55,470 INFO [train.py:445] Epoch 25, batc2022-06-19 04:26:55,770 INFO [train.py:445] Epoch 25, batch 7800, loss[loss=2.187, over 45600.00 frames., ppl: 8.905130792480495] tot_loss[loss=2.251, over 312022-2022-06-19 04:28:06,642 INFO [train.py:445] Epoch 25, batch 8000, loss[loss=2.167, over 33200.00 frames., ppl: 8.735801008715528] tot_loss[loss=2.251, over 31305403.70 frames., ppl: 9.500056332011896], batch size:2022-2022-06-19 04:29:19,334 INFO [train.py:445] Epoch 25, batch 8200, loss[loss=2.152, over 36000.00 frames., ppl: 8.598930231356537] tot_loss[loss=2.25, over 31678398.85 frames., ppl: 9.491276928373125], batch size2022-06-19 04:30:33,164 INFO [train.py:445] Epoch 25, batch 2022-06-19 04:30:33,309 INFO [train.py:445] Epoch 25, batch 8400, loss[loss=2.171, over 42400.00 frames., ppl: 8.766624775568122] tot_loss[loss=2.252, over 32022-062022-06-19 04:32022-06-19 04:31:45,944 INFO [train.py:2022-06-19 04:31:46,098 INFO [train.py:445] Epoch 25, batch 8600, loss[loss=2.183, over 29200.00 frames., ppl: 8.87433847849131] tot_loss[loss=2.251, over 32022-06-19 04:32:59,636 INFO [train.py:445] Ep2022-06-19 04:32:59,809 INFO [train.py:445] Epoch 25, batch 8800, loss[loss=2.214, over 25600.00 frames., ppl: 9.15246539354635] tot_loss[loss=2.252, over 31408325.95 fram2022-06-2022-06-19 04:34:13,905 INFO [train.py:445] Epoch 25, batch 9000, loss[loss=2.171, over 28800.00 frames., ppl: 8.763220043754721] tot_loss[loss=2.253, over 31678884.55 frames., ppl: 9.51343985168989], batch siz2022-062022-06-19 04:352022-06-19 04:35:25,961 2022-06-19 04:2022-06-19 04:35:26,205 INFO [train.py:445] Epoch 25, batch 9200, loss[loss=2.135, over 38000.00 frames., ppl: 8.45992628756437] tot_loss[loss=2.252, over 32022-06-12022-06-19 04:32022-06-19 04:36:38,198 INFO [2022-06-19 04:36:38,308 INFO [train.py:445] Epoch 25, batch 9400, loss[loss=2.15, over 35200.00 frames., ppl: 8.588917805761765] tot_loss[loss=2.257, over 30834592022-06-19 04:37:53,203 IN2022-06-19 04:37:53,2202022-02022-06-19 04:37:53,333 INFO [train.py:445] Epoch 25, batch 9600, loss[loss=2.199, over 34000.00 frames., ppl: 9.01773752830884] tot_loss[loss=2.257, over 30871082022-06-192022-06-19 04:39:03,691 INFO [train.py:445] Epoch 25, 2022-06-19 04:39:03,697 INFO [train.py:445] Epoch 25, batch 9800, loss[loss=2.301, over 17600.00 frames., ppl: 9.980790413450906] tot_loss[loss=2.255, ove2022-06-12022-06-19 04:40:2022-06-19 04:40:11,4912022-06-19 04:40:11,498 INFO [2022-06-19 04:40:11,602 INFO [train.py:445] Epoch 25, batch 10000, loss[loss=2.144, over 42400.00 frames., ppl: 8.5304319447368] tot_loss[loss=2.254, over 31296304.36 frames., ppl: 9.530431906772426], batch s2022-06-12022-06-19 04:40:11,787 INFO [train.py:480] Epoch 25, validation: loss=2.322, over 211809.00 frames., ppl: 10.19499462022-06-12022-06-19 04:41:23,669 INFO [train.py:442022-06-19 04:41:23,691 INFO [train.py:445] Epoch 25, batch 10200, loss[loss=2.295, over 12800.00 frames., ppl: 9.92341236532098] tot_loss[loss=2.253, over 31497430.24 f2022-06-19 04:42:37,642 INFO [train.py:445] Epoch 25, batch 10400, loss[loss=2.2022-06-19 04:42:37,665 INFO [train.py:445] Epoch 25, batch 10400, loss[loss=2.16, over 32800.00 frames., ppl: 8.666979054424997] tot_loss[l2022-06-19 04:43:46,691 INFO [train.py:445] Epoch2022-06-19 04:43:46,727 INFO2022-06-19 04:43:46,823 INFO [train.py:445] Epoch 25, batch 10600, loss[loss=2.239, over 19600.00 frames., ppl: 9.379876957625495] tot_loss[lo2022-06-19 04:44:59,471 INFO [train.py:445] Epoch 25, batch 10800, loss[loss=2.158, over 43200.00 frames., ppl: 8.651506527220912] tot_loss[loss=2.252, over 32071012.63 frames., ppl: 9.50883487819398], batch size: 400
+2022-06-2022-06-19 04:46:10,348 INFO [train.py:445] Epoch 25, batch 11000, loss[loss=2.188, over 36000.00 frames., ppl: 8.918246908555615] tot_loss[loss=2.254, over 31805782.92 frames., ppl: 9.524909084319095], batch s2022-06-19 04:47:22,222 INFO [train.py:445] Epoch 25, batch 11200, loss[loss=2.223, over 22800.00 frames., ppl: 9.23066264826407] tot_loss[loss=2.253, over 32020890.92 frames., ppl: 9.517828425416432], batch size: 400
+2022-06-19 04:48:37,150 INFO [train.py:445] Epoch 25, batch 11400, loss[loss=2.169, over 34800.00 frames., ppl: 8.75153467892944] tot_loss[loss=2.253, over 32005841.90 frames., ppl: 9.516999344545814], batch size: 400
+2022-06-19 04:49:50,546 INFO [tra2022-06-19 04:49:50,658 INFO [train.py:445] Epoch 25, batch 11600, loss[loss=2.158, over 27600.00 frames., ppl: 8.650160208087048] tot_loss[loss=2.254, over 32067990.70 frames., ppl: 9.52022-06-19 04:51:05,088 INFO [train.py:445] Epoch 25, batch 11800, loss[loss=2.148, over 74223.00 frames., ppl: 8.571733816462615] tot_loss[loss=2.253, over 32101478.22 frames., ppl: 9.51383551527015], batch size: 400
+2022-06-19 04:52:19,193 INFO [tra2022-06-19 04:52:19,207 INF2022-06-19 04:52:19,2022-06-19 04:52:19,474 INFO [train.py:445] Epoch 25, batch 12000, loss[loss=2.166, over 36800.00 frames., ppl: 8.723371990879858] tot_loss2022-06-19 04:53:33,227 INFO [train.py:445] Epoch 2022-06-19 04:53:33,32022-06-19 04:53:33,489 INFO [train.py:445] Epoch 25, batch 12200, loss[loss=2.126, over 37600.00 frames., ppl: 8.382098670473415] tot_loss[loss=2.2022-06-19 02022-06-19 04:54:45,912022-06-19 04:542022-06-19 04:54:46,191 INFO [train.py:445] Epoch 25, batch 12400, loss[loss=2.154, over 40000.00 frames., ppl: 8.618660580420016] tot_loss[loss=2.255, over 31925060.2022-06-19 04:552022-06-19 04:55:592022-06-19 04:55:59,595 INF2022-06-19 04:55:59,652 INFO [train.py:445] Epoch 25, batch 12600, loss[loss=2.141, over 34800.00 frames., ppl: 8.509231817173454] tot_loss[loss=2.257, over2022-06-19 04:56:28,858 INFO [train.py:445] Epoch 26, 2022-06-12022-06-19 04:56:29,103 INFO [train.py:445] Epoch 26, batch 0, loss[loss=2.13, over 39200.00 frames., ppl: 8.414055148670839] tot_loss[loss=2.13, o2022-06-19 04:57:43,2022-06-19 04:57:42022-06-19 04:57:42022-06-19 04:57:43,619 INFO [train.py:445] Epoch 26, batch 200, loss[loss=2.142, over 32800.00 frames., ppl: 8.517715740454365] tot_loss[loss=2.244, over 291292022-06-19 04:58:58,2022-06-19 04:58:52022-06-19 04:58:52022-06-19 04:58:58,335 INFO [train.py:445] Epoch 26, batch 400, loss[loss=2.195, over 26000.00 frames., ppl: 8.981022428120712] tot_loss[loss=2.237, over 5852022-06-19 05:00:10,312022-06-19 05:00:10,625 INFO [train.py:445] Epoch 26, batch 600, loss[loss=2.132, over 38800.00 frames., ppl: 8.435296742602077] tot_loss[loss=2.246, over 7916313.67 frames., ppl: 9.4500704552022-06-19 05:01:25,720 INFO [train.py:445]2022-06-19 05:01:25,745 INFO [train.2022-06-19 05:01:26,241 INFO [train.py:445] Epoch 26, batch 800, loss[loss=2.189, over 58608.00 frames., ppl: 8.926148226217293] tot_loss2022-06-19 05:02:36,644 INFO [train.py:445] Epoch 26, batch 2022-06-19 05:02:36,691 INFO [train.py:445] Epoch 26, batch 1000, loss[loss=2.201, over 26800.00 frames., ppl: 9.033538984384789] tot_loss[loss=2.237, over 12022-06-19 05:03:51,2602022-06-19 05:03:51,384 INFO [train.py:445] Epoch 26, batch 1200, loss[loss=2.2, over 18800.00 frames., ppl: 9.026720102402118] tot_loss[loss=2.241, over 14252125.02 frames., ppl: 9.39806552857172022-06-19 05:05:01,02022-06-19 05:05:01,0612022-06-19 05:05:01,1472022-06-19 05:05:01,309 INFO [train.py:445] Epoch 26, batch 1400, loss[loss=2.144, over 39600.00 frames., ppl: 8.535821671807902] tot_loss[loss=2.237,2022-06-19 05:06:15,159 INFO [train.py:445] Epoch 26, batch 1600, loss[loss=2.208, over 30800.00 frames., ppl: 9.099166653511395] tot_loss[loss=2.238, over 18157587.02 frames., ppl: 9.371113734719868], batch size: 400
+2022-06-19 05:07:27,801 INFO [train.py:445] Epoch 26, batch 1800, loss[loss=2.149, over 40400.00 frames., ppl: 8.57903000398599] tot_loss[loss=2.237, over 19738590.38 frames., ppl: 9.364114423363706], batch size: 400
+2022-06-19 05:08:37,492022-06-19 05:08:37,6932022-06-19 05:08:37,728 INFO [train.py:445] Epoch 26, batch 2000, loss[loss=2.177, over 36800.00 frames., ppl: 8.817403634209745] tot_loss[loss=2.245, over 19740211.87 f2022-06-19 05:09:50,333 2022-06-19 05:09:50,378 INFO [train.py2022-06-19 05:09:50,392 INFO [tr2022-06-19 05:09:50,478 INFO [train.py:445] Epoch 26, batch 2200, loss[loss=2.219, over 24000.00 frames., ppl: 9.197863155822022-06-19 05:11:04,842022-06-19 05:11:04,950 INFO [train.py:445] Epoch 26, batch 2400, loss[l2022-06-19 05:11:05,044 INFO [train.py:445] Epoch 26, batch 2400, loss[loss=2.142, over 43200.00 frames., ppl: 8.51282536882022-06-19 05:12:15,9672022-06-19 05:12:15,996 2022-06-19 05:2022-06-19 05:12:16,196 INFO [train.py:445] Epoch 26, batch 2600, loss[loss=2.163, over 29200.00 frames., ppl: 8.697439582703325] tot_loss[loss=2.243, over 22022-06-19 05:13:28,442022-06-19 05:13:28,451 INFO [train.py:445] E2022-06-19 05:13:28,487 INFO [train.py:445] Epoch 26, batch 2800, loss[loss=2.305, over 18400.00 frames., ppl: 10.025778707545735] tot_loss[loss=2.2432022-06-19 05:14:42,792 INFO [train.py:445] Epo2022-06-19 05:14:43,082 INFO [train.py:445] Epoch 26, batch 3000, loss[loss=2.185, over 43014.00 frames., ppl: 8.890423261974643] tot_loss[loss=2.245, over 24660085.23 fra2022-06-19 05:15:56,702022-06-19 05:15:56,767 I2022-06-19 05:15:56,976 INFO [train.py:445] Epoch 26, batch 3200, loss[loss=2.141, over 40000.00 frames., ppl: 8.51143826825083] tot_loss[loss=2.245, over 25273894.27 fr2022-06-19 05:17:08,306 INFO [train.py:445] Epoc2022-06-19 05:2022-06-19 05:17:08,398 INFO [train.py:445] Epoch 26, batch 3400, loss[loss=2.203, over 24800.00 frames., ppl: 9.048473605845174] tot_loss[loss=2.243, over2022-06-19 05:18:22,202 INFO [train.py:445] Epoch2022-06-19 05:2022-06-19 05:18:22,469 INFO [train.py:445] Epoch 26, batch 3600, loss[loss=2.153, over 33200.00 frames., ppl: 8.607258787563662] tot_loss[loss=2.244, over2022-06-19 05:19:35,4702022-06-19 05:19:35,511 IN2022-06-19 05:19:35,514 INFO [train.py:445]2022-06-19 05:19:35,631 INFO [train.py:445] Epoch 26, batch 3800, loss[loss=2.146, over 28000.00 frames., ppl: 8.55038357036342022-06-19 05:20:50,421 INFO [train.py:445] Epoch 26, batch 2022-06-19 05:20:50,466 INFO [t2022-06-19 05:20:50,772 INFO [train.py:445] Epoch 26, batch 4000, loss[loss=2.175, over 49200.00 frames., ppl: 8.800334755680332022-06-19 05:22:03,518 INFO [train.py:445] Epoch 26, batch 4200, loss2022-06-19 05:22:03,760 INFO [train.py:445] Epoch 26, batch 4200, loss[loss=2.17, over 42000.00 frames., ppl: 8.754431959949013] tot_loss[loss=2.2432022-06-19 05:23:18,286 INFO [train.py:445] Epoch 26, batch 4400, loss[loss=2.16, over 41600.00 frames., ppl: 8.672170153312058] tot_loss[loss=2.246, over 28524825.43 frames., ppl: 9.446218725198007], batch size: 400
+2022-06-19 05:24:28,672022-06-19 05:24:28,950 INFO [train.py:445] Epoch 26, batch 4600, loss[loss=2.152, over 33600.00 frames., ppl: 8.603764355870052] tot_loss[loss=2.246, over 28858132.32 frames., ppl: 9.4499915183242022-06-19 05:25:43,382022-06-19 05:25:43,410 INFO [train.py:445] Epoc2022-06-19 05:25:43,2022-06-19 05:25:43,488 INFO [train.py:445] Epoch 26, batch 4800, loss[loss=2.199, over 23600.00 frames., ppl: 9.012714371976065]2022-06-19 05:26:54,295 INFO [train.py:445] Epoch 26, batch 5000, loss[loss=2.217, over 19200.00 frames., ppl: 9.177475032613684] tot_loss[loss=2.249, over 28724429.34 frames., ppl: 9.482742928296254], batch size: 400
+2022-06-19 05:28:03,919 INFO [train.py:445] Epoch 26, batch 5200, loss[loss=2.202, over 36800.00 frames., ppl: 9.04120075250095] tot_loss[loss=2.252, over 28575295.92 frames., ppl: 9.502687514895129], batch size: 400
+2022-06-19 05:29:18,222 INFO [train.py:445] Epoch 26, batch 5400, loss[loss=2.142, over 51200.00 frames., ppl: 8.518365044740063] tot_loss[loss=2.252, over 28777310.57 frames., ppl: 9.507371292293616], batch size: 400
+2022-06-19 05:30:33,052 INFO [train.py:445] Epoch 26, batch 5600, loss[loss=2.143, over 52800.00 frames., ppl: 8.528729958593724] tot_loss[loss=2.251, over 29022898.44 frames., ppl: 9.500574290609508], batch size: 400
+2022-06-19 05:31:48,308 INFO [train.py:445] Epoch 26, batch 5800, loss[loss=2.155, over 47200.00 frames., ppl: 8.627950158551496] tot_loss[loss=2.25, over 29465205.09 frames., ppl: 9.487983276870919], batch size: 400
+2022-06-19 05:33:01,2022-06-19 05:33:01,100 INFO [train.py:445] Epoch 26, batch 6000, 2022-06-19 05:33:01,487 INFO [train.py:445] Epoch 26, batch 6000, loss[loss=2.131, over 58800.00 frames., ppl: 8.42357418900872] tot2022-06-19 05:34:13,786 INFO [train.py:445] Epoch 26, batch 6200, loss[loss=2.255, ov2022-06-19 05:34:13,788 INFO [train.py:445] Epoch 26, batch 6200, loss[loss=2.295, over 13200.00 frames., ppl: 9.919666134308859] tot2022-06-19 05:35:27,082 INFO [train.py:445] 2022-06-19 05:35:27,1002022-06-19 05:35:27,202022-06-19 05:35:27,297 INFO [train.py:445] Epoch 26, batch 6400, loss[loss=2.165, over 31200.00 frames., ppl: 8.7125683978884492022-06-19 05:36:2022-06-19 05:36:41,324 INFO [train.py:445] Epoch2022-06-19 05:36:41,339 2022-06-19 05:36:41,483 INFO [train.py:445] Epoch 26, batch 6600, loss[loss=2.205, over 25600.00 frames., ppl: 9.069733962678602022-06-19 05:37:54,595 INFO [train.py:445] Epoch 26, batch 6800, loss[loss=2.163, ove2022-06-19 05:37:54,702 INFO [train.py:445] Epoch 26, batch 6800, loss[loss=2.154, over 69910.00 frames., ppl: 8.62355017466581] tot2022-06-19 05:39:08,792 INFO [train.py:445] Epoch 26, batch 7000, loss[loss=2.183, over 38400.00 frames., ppl: 8.871228735151563] tot_loss[loss=2.254, over 29889055.63 frames., ppl: 9.526637668912189], batch size: 400
+2022-06-19 05:40:20,691 INFO [train.py:445] Epoch 26, batch 7200, loss[loss=2.207, over 30000.00 frames., ppl: 9.087267143887043] tot_loss[loss=2.255, over 29994450.61 frames., ppl: 9.531903973797771], batch size: 400
+2022-06-19 05:41:33,256 INFO [train.py:445] Epoch 26, batc2022-06-19 05:41:33,324 INFO [train.py:445] Epoch 26, batch 7400, loss[loss=2.212, over 28800.00 frames., ppl: 9.136915559860931] tot_loss[loss=2.247, over 31622022-06-19 05:42:42,231 INFO [train.py:445]2022-06-19 05:42:42,479 INFO [train.py:445] Epoch 26, batch 7600, loss[loss=2.158, over 45200.00 frames., ppl: 8.649826261297322] tot_loss[loss=2.249, over 31226248.86 frames.2022-06-19 05:43:54,352 INFO [train.py:445] Epoch 26, batch 7800, loss[loss=2.254, over 16400.00 frames., ppl: 9.529321995287091] tot_loss[loss=2.254, over 30496671.19 frames., ppl: 9.525725839484632], batch size: 400
+2022-06-19 05:45:04,091 INFO [train.py:445] Epoch 26, batch 8000, loss[loss=2.189, over 34000.00 frames., ppl: 8.928506010912066] tot_loss[loss=2.254, over 30555521.05 frames., ppl: 9.52518918340558], batch size: 400
+2022-06-19 02022-06-19 05:46:17,407 INFO [train.py:445] Epoch 26, batch 8200, loss[loss=2.177, over 26400.00 frames., ppl: 8.818331522080317] tot_loss[loss=2.252, over 31299224.03 frames., ppl: 9.502914490198464], batc2022-06-19 02022-06-19 05:47:32,800 INFO [train.py:445] Ep2022-06-19 05:47:32,847 INFO [train.py:445] Epoch 26, batch 8400, loss[loss=2.224, over 21200.00 frames., ppl: 9.24036686650877] tot_loss[loss=2.251, over 312902022-06-19 2022-06-19 05:48:40,374 INFO2022-06-19 05:42022-06-19 05:48:40,460 I2022-06-19 05:48:40,497 INFO [train.py:445] Epoch 26, batch 8600, loss[loss=2.193, over 29435.00 frames., ppl: 8.95765184445464] tot_loss[l2022-06-19 05:49:53,095 INFO [train.py:445] Epoch 26, 2022-06-19 05:49:53,156 2022-06-19 05:49:53,164 INFO [train.py:445] Epoch 26, batch 8800, loss[loss=2.178, over 28800.00 frames., ppl: 8.829991267061649] tot_loss[l2022-06-19 2022-06-19 05:51:02,296 INF2022-06-19 05:51:02,338 INFO [train.py:445] Epoch 26, batch 9000, loss[loss=2.174, over 27600.00 frames., ppl: 8.795039736750335] tot_loss[loss=2.249, over 31875348.56 frames., ppl2022-06-19 2022-06-19 05:52:12,573 INFO [train.py:445] Epoch 26, batch 9200, 2022-062022-06-19 05:52:12,640 INFO [train.py:445] Epoch 26, batch 9200, loss[loss=2.295, over 15200.00 frames., ppl: 9.924183517767801] tot_2022-06-19 05:53:26,121 INFO [train.py:445] Epoch 26, batch 9400, loss[loss=2.139, over 34800.00 frames., ppl: 8.494209137471252] tot_loss[loss=2.254, over 31065644.03 frames., ppl: 9.527831395475824], batch size: 400
+2022-06-19 05:54:39,121 INFO [train.p2022-06-19 05:54:39,267 INFO [train.p2022-06-19 05:54:39,474 INFO [train.py:445] Epoch 26, batch 9600, loss[loss=2.145, over 51600.00 frames., ppl: 8.540237263031482] tot_loss[loss=2022-06-2022-06-19 05:55:51,850 INFO [train.py:445] 2022-06-19 05:55:51,949 INFO [train.py:445] Epoch 26, batch 9800, loss[loss=2.187, over 26000.00 frames., ppl: 8.904781158792396] tot_loss[loss=2.253, over 31518484.52022-02022-06-19 05:57:06,526 INFO [train.py:445] 2022-06-19 05:57:06,620 INFO [train.py:445] Epoch 26, batch 10000, loss[loss=2.151, over 35600.00 frames., ppl: 8.595469797185395] tot_loss[loss=2.253, over 31785583.09 frames., ppl: 9.513077858713183], batch size: 400
+2022-06-19 05:57:062022-062022-06-19 05:57:06,806 INFO [train.py:480] Ep2022-06-19 05:57:06,806 INFO [train.py:480] Epoch 26, validation: loss=22022-06-19 05:58:22,261 INFO [train.2022-06-19 05:52022-06-19 05:58:22,503 INFO [train.py:445] Epoch 26, batch 10200, loss[loss=2.182, over 31200.00 frames., ppl: 8.860370152392022] tot_loss[loss=2.253, over 31906090.822022-062022-06-19 05:59:33,274 INFO 2022-06-19 05:59:33,371 INFO [train2022-06-19 05:59:33,637 INFO [train.py:445] Epoch 26, batch 10400, loss[loss=2.195, over 43818.00 frames., ppl: 8.978529333781347] tot_loss[loss=2.22022-062022-06-19 06:00:46,831 INFO [train.py:445] 2022-06-19 06:00:47,096 INFO [train.py:445] Epoch 26, batch 10600, loss[loss=2.153, over 40800.00 frames., ppl: 8.613411833341882] tot_loss[loss=2.251, over 32297082.652022022-06-19 06:01:58,508 INFO [tra2022-06-19 06:01:58,512 INFO [train.py:445] Ep2022-06-19 06:01:58,645 INFO [train.py:445] Epoch 26, batch 10800, loss[loss=2.187, over 27200.00 frames., ppl: 8.909324816830793] tot2022-2022-06-19 06:03:09,432 INFO [trai2022-06-19 06:02022-06-19 06:03:09,549 INFO [train.py:445] Epoch 26, batch 11000, loss[loss=2.303, over 19200.00 frames., ppl: 10.000864195289692] tot_loss[loss=2.252, over 32029492022-06-19 06:04:25,649 INFO [train.py:445] Epoch 26, batch 11200, loss[loss=2.168, over 32000.00 frames., ppl: 8.742603318226248] tot_loss[loss=2.252, over 31775024.20 frames., ppl: 9.510689669493011], batch size: 400
+2022-06-19 06:05:37,326 INFO [train.py:445] Epoch 26, batch 11400, loss[loss=2.166, over 52000.00 frames., ppl: 8.720555960167633] tot_loss[loss=2.253, over 31659269.47 frames., ppl: 9.52039117958518], batch size: 400
+2022-06-19 06:06:50,202 INFO [train.py:445] Epoch 26, batch 11600, loss[lo2022-06-192022-06-19 06:06:50,399 INFO [train.py:445] Epoch 26, batch 11600, loss[loss=2.159, over 32800.00 frames., ppl: 8.664411379889154] tot_l2022-06-19 06:08:06,791 INFO [train.2022-06-19 06:08:06,835 INFO [train.py:445] Epoch 26, batch 11800, loss[loss=2.171, over 34800.00 frames., ppl: 8.765467582558312] tot_loss[loss=2.253, over 31766640.74 frames., ppl:2022022-06-19 06:09:20,563 INFO [train.py:445] Epoch 26, 2022-06-19 06:09:20,686 INF2022-06-19 06:09:20,915 INFO [train.py:445] Epoch 26, batch 12000, loss[loss=2.187, over 48000.00 frames., ppl: 8.906899239466789] tot_2022022-06-19 06:10:33,439 INFO [train.py:445] Epoch 26, batch 12200, loss[loss=2.185, over 47637.00 frames., ppl: 8.889365309043336] tot_loss[loss=2.252, over 32386526.67 frames., ppl: 9.507272587883595], batch size: 22022022-06-19 06:11:44,652 INFO [tra2022-06-19 06:11:44,701 INFO [train2022-06-19 06:11:44,839 INFO [train.py:445] Epoch 26, batch 12400, loss[loss=2.185, over 28400.00 frames., ppl: 8.890031032795653] tot_loss[loss=2.2022-06-19 06:12:56,449 INFO [train.py2022-06-19 06:12:56,531 INFO [train.py:445] Epoc2022-06-19 06:12:56,546 INFO [train.py:445] Epoch 26, batch 12600, loss[loss=2.188, over 21600.00 frames., ppl: 8.915005085511098] to20222022-06-19 06:13:25,588 INFO [trai2022-06-19 06:2022-06-19 06:13:25,707 INFO [train.py:445] Epoch 27, batch 0, loss[loss=2.197, over 22800.00 frames., ppl: 8.997329118127276] tot_loss[loss=2.197, over 228002022-02022-06-19 06:14:42,036 INFO [train.py:445] Epoch 27, batch 200, loss[loss=2.151, over 22000.00 frames., ppl: 8.590750313855466] tot_loss[loss=2.233, over 3045368.07 frames., ppl: 9.325278199617358], batch siz2022-062022-06-19 06:15:54,279 INFO [train.py:445] Epoch 27, batch 400, los2022-06-19 06:15:54,406 INFO [train.py:445] Epoch 27, batch 400, loss[loss=2.186, over 28000.00 frames., ppl: 8.897552399990717] tot_loss[lo2022-06-19 06:17:11,415 INFO [train.py:445] Epoch 27, batch 600, loss[loss=2.262, over 15600.00 frames., ppl: 9.606701592696112] tot_loss[loss=2.238, over 8290308.97 frames., ppl: 9.37664012243686], batch size: 400
+2022-06-19 06:18:22,925 INFO [train.py:445] 2022-06-19 2022-06-19 06:18:23,094 INFO [train.py:445] Epoch 27, batch 800, loss[loss=2.178, over 38400.00 frames., ppl: 8.829483480515771] tot_loss[loss=2.236, over 10947412022-02022-06-19 06:19:36,578 INFO [train.p2022-06-19 06:19:36,677 INFO [train.py:445] Epoch 27, batch 1000, loss[loss=2.167, over 20800.00 frames., ppl: 8.732126292310339] tot_loss[loss=2.239, over 12348670.05 frames.2022-02022-06-19 06:20:47,811 INFO [train.p2022-06-19 06:20:47,964 INFO [train.py:445] Epoch 27, batch 1200, loss[loss=2.167, over 33600.00 frames., ppl: 8.727798894485593] tot_loss[loss=2.241, over 14067728.16 frames.20222022-06-19 06:21:58,335 INFO [train.py:445] Epoch 27, batch 1400, loss[loss=2.157, over 33600.00 frames., ppl: 8.647898238779671] tot_loss[loss=2.239, over 15989375.28 frames., ppl: 9.3870483659675], batch size: 402022-06-19 06:23:10,944 INFO [train.py:445] Epoch 27, batch 1600, loss[loss=2.204, over 20800.00 frames., ppl: 9.061741980913865] tot_loss[loss=2.243, over 17261417.44 frames., ppl: 9.422008850114494], batch size: 400
+2022-06-19 06:24:22,191 INFO [train.py:445] Epoch 27, batch 1800, loss[l2022-06-19 06:24:22,541 INFO [train.py:445] Epoch 27, batch 1800, loss[loss=2.129, over 54800.00 frames., ppl: 8.404867626286364] tot_loss[loss=2.2022-06-19 06:25:35,847 INFO [train.py:445] Epoch 2022-06-19 06:25:36,197 INFO [train.py:445] Epoch 27, batch 2000, loss[loss=2.172, over 54800.00 frames., ppl: 8.773152666990853] tot_loss[loss=2.237, over 20948042.0922022-06-19 06:26:49,935 INFO [train.py:445]2022-06-19 06:26:49,993 INFO [train.py:445] Epoch 27, batch 2200, loss[loss=2.174, over 28000.00 frames., ppl: 8.791197849777557] tot_loss[loss=2.239, over 21540621.40 frame202022-06-19 06:28:04,326 INFO [train.py:445] Epoch 27, batch 2400, loss[2022-06-19 06:28:04,415 INFO [train.py:445] Epoch 27, batch 2400, loss[loss=2.135, over 39600.00 frames., ppl: 8.454936179171884] tot_loss[loss=2022-06-19 06:29:17,299 INFO [train.py:445] Epoch 2022-06-19 06:29:17,493 INFO [train.py:445]2022-06-19 06:29:17,697 INFO [train.py:445] Epoch 27, batch 2600, loss[loss=2.199, over 47838.00 frames., ppl: 9.0127932152962022-06-19 06:30:29,297 INFO [train.py:442022-06-192022-06-19 06:30:29,3462022-06-19 06:30:22022-06-19 06:30:29,723 INFO [train.py:445] Epoch 27, batch 2800, loss[loss=2.113, over 56400.00 frames., ppl: 8.27553712629852022-06-19 06:31:40,115 INFO [train.py:442022-06-19 06:31:40,466 INFO [train.py:445] Epoch 27, batch 3000, loss[loss=2.152, over 58000.00 frames., ppl: 8.606338119147464] tot_loss[loss=2.242, over 24959214.72 frames.,2022-06-19 06:32:50,562 INFO [train.py:445] Epoch 27, batch 3200, loss[loss=2.138, over 45600.00 frames., ppl: 8.480631500184991] tot_loss[loss=2.243, over 25277206.00 frames., ppl: 9.425865338551132], batch size: 400
+202022-06-19 06:34:04,645 INFO [train.py:445] Epo2022-06-19 06:34:04,765 INFO [train.py:445] Epoch 27, batch 3400, loss[loss=2.144, over 39600.00 frames., ppl: 8.536976968904813] tot_loss[loss=2.24, over 26725227.96 fr2022-06-19 06:35:17,791 INFO [train.py:445] Epoch 27, batch 3600, loss[loss=2.143, over 45600.00 frames., ppl: 8.520911344507262] tot_loss[loss=2.244, over 26415289.58 frames., ppl: 9.435637961249622], batch size: 400
+22022-06-19 06:36:30,125 INFO [train.py:445] Epoch 27, batch 3800, loss[loss=2.155, over 34000.00 frames., ppl: 8.625368792795058] tot_loss[loss=2.245, over 26887776.81 frames., ppl: 9.442955571834158], batch size: 20222022-06-19 06:37:40,686 INFO [train.py:445] Ep2022-06-19 06:37:40,897 INFO [train.py:445] Epoch 27, batch 4000, loss[loss=2.167, over 45600.00 frames., ppl: 8.735332074707646] tot_loss[loss=2.245, over 27454604.36 2022-06-19 06:38:49,271 INFO [train.py:445] Epoch 27, batch 4200, loss[loss=2.175, over 46632.00 frames., ppl: 8.803835733832068] tot_loss[loss=2.247, over 27661931.57 frames., ppl: 9.457559475514648], batch size: 201
+20222022-06-19 06:40:05,145 INFO [train.py:445] Epoch 27, batch 4400, loss2022-06-19 06:40:05,178 INFO [train.py:445] Epoch 27, batch 4400, loss[loss=2.178, over 25600.00 frames., ppl: 8.83290441823333] tot_loss[loss=2022-06-19 06:41:21,042 INFO [train.py:445] Ep2022-06-19 06:41:21,263 INFO [train.py:445] Epoch 27, batch 4600, loss[loss=2.201, over 40803.00 frames., ppl: 9.034896547201699] tot_loss[loss=2.247, over 28662483.09 fra2022-02022-06-19 06:42:33,433 INFO [train.py:442022022-06-19 06:42:33,599 INFO [train.py:445] Epoch 27, batch 4800, loss[loss=2.223, over 23600.00 frames., ppl: 9.234959481656148] tot_loss[loss=2.244, over 29191616.122022-062022-06-19 06:43:49,002 INFO [train.py:42022-06-19 06:43:49,156 INFO [train.py:445] Epoch 27, batch 5000, loss[loss=2.14, over 41600.00 frames., ppl: 8.500883907739322] tot_loss[loss=2.248, over 28957668.07 fram2022-06-19 06:45:04,732 INFO [train.py:445] Ep2022-06-19 06:45:04,845 INFO [train.py:445] Epoch 27, batch 5200, loss[loss=2.176, over 21600.00 frames., ppl: 8.813013039398648] tot_loss[loss=2.247, over 29518942.67 fram2022-06-19 06:46:17,604 INFO [train.py:445] Epoch 27, batch 5400, loss[loss=2.141, over 40400.00 frames., ppl: 8.505669195251626] tot_loss[loss=2.246, over 29813433.15 frames., ppl: 9.446749854293987], batch size: 400
+2022-06-19 06:47:27,412 INFO [train.py:445] Epoch 27, batch 5600, loss[2022-06-19 06:47:27,422 IN2022-06-19 06:47:27,576 INFO [train.py:445] Epoch 27, batch 5600, loss[loss=2.158, over 34000.00 frames., ppl: 8.65807302022-062022-06-19 06:48:39,572 INFO [train.py:445] Epoch 27, batch 5800,2022-06-19 06:48:39,641 INFO [train.py:445] Epoch 27, batch 5800, loss[loss=2.143, over 50400.00 frames., ppl: 8.520950552268324] tot_loss[loss=2.2022-06-19 06:49:50,726 INFO [train.py:445] Epoch 27,2022-06-19 06:49:50,882 INFO [train.py:445] Epoch 27, batch 6000, loss[loss=2.128, over 35200.00 frames., ppl: 8.39439061210248] tot_loss[loss=2.246, over 30473359.52022-06-19 06:51:03,903 INFO [train.py:445] Epoch 27, batch 6200, loss[loss=2.188, over 30000.00 frames., ppl: 8.915737456431932] tot_loss[loss=2.247, over 30414885.23 frames., ppl: 9.456683593256152], batch size: 400
+2022-06-2022-06-19 06:52:16,949 INFO [train.py:4452022-06-19 06:52:17,02022-06-19 06:52:17,108 INFO [train.py:445] Epoch 27, batch 6400, loss[loss=2.144, over 34800.00 frames., ppl: 8.535519835988948] tot_loss[loss=2.2022-06-19 06:53:27,962 INFO [train.py:445] Epoch 27, batch 6600, loss[l2022-06-19 06:53:28,006 INFO [train.py:445] Epoch 27, batch 6600, loss[loss=2.178, over 31200.00 frames., ppl: 8.826662259586154] tot_loss[loss=2.2022-06-19 06:54:41,567 INFO [train.py:445] Epoch 27,2022-06-19 06:54:41,871 INFO [train.py:445] Epoch 27, batch 6800, loss[loss=2.175, over 63600.00 frames., ppl: 8.798064387744683] tot_loss[loss=2.245, over 31413827.2022-06-2022-06-19 06:55:55,388 INFO [train.py:445] Epoch 27, batch 7000, loss[loss=2.183, over 33600.00 frames., ppl: 8.869398284255933] tot_loss[loss=2.25, over 30597481.54 frames., ppl: 9.486633415361341], batch si2022-06-19 06:57:07,162 INFO [train.py:445] Epoch 22022-06-19 06:57:07,12022-06-19 06:57:07,269 INFO [train.py:445] Epoch 27, batch 7200, loss[loss=2.255, over 20000.00 frames., ppl: 9.533896640963382] tot_loss[loss=22022-062022-06-19 06:58:19,403 INFO [train.p2022-06-19 06:58:19,709 INFO [train.py:445] Epoch 27, batch 7400, loss[loss=2.152, over 42400.00 frames., ppl: 8.6002965974722] tot_loss[loss=2.248, over 31555575.93 frame2022-06-19 06:59:32,859 INFO [train.py:445] 2022-06-19 06:59:32,865 INF2022-06-19 06:59:32,991 INFO [train.py:445] Epoch 27, batch 7600, loss[loss=2.169, over 48400.00 frames., ppl: 8.747977015975495] tot_loss[loss=22022-06-19 07:00:45,127 INFO [train.py:445] Epoch 27, batch 7800, loss[lo2022-06-19 07:00:45,154 INFO [train.py:445] Epoch 27, batch 7800, loss[loss=2.218, over 22400.00 frames., ppl: 9.186289066544761] tot_loss[loss2022-06-19 2022-06-19 07:01:57,627 INFO [train.py:442022-06-19 07:01:57,718 INFO [train.py:445] Epoch 27, batch 8000, loss[loss=2.248, over 16000.00 frames., ppl: 9.471641661620673] tot_loss[loss=2.252, over 30894904.62022-06-192022-06-19 07:03:10,544 INFO [train.p2022-06-19 07:03:10,551 INFO [train.py:445] Epoch2022-06-19 07:03:10,617 INFO [train.py:445] Epoch 27, batch 8200, loss[loss=2.164, over 29600.00 frames., ppl: 8.705217982022-06-12022-06-19 07:04:24,904 INFO [train.py:2022-06-19 07:04:25,110 INFO [train.py:445] Epoch 27, batch 8400, loss[loss=2.142, over 40000.00 frames., ppl: 8.517241988152566] tot_loss[loss=2.247, over 32278392.67 f2022-06-19 07:05:39,006 INFO [train.py:445] Epoch 27, batch 8600, loss[loss=2.234, over 20000.00 2022-06-19 07:05:39,061 INFO [train.py:445] Epoch 27, batch 8600, loss[loss=2.204, over 26000.00 frames., ppl: 9.063519512022-062022-06-19 07:06:51,007 INFO [train.py:445] Epoch 27, batch 8800, loss[loss=2.384, over 10000.00 frames., ppl: 10.850419153914794] tot_loss[loss=2.248, over 31830509.61 frames., ppl: 9.472663803380197], batch s2022-06-12022-06-19 07:08:03,432 INFO [train.py:445] Epoch 27, batch 9000, loss[loss=2.239, over 19200.00 frames., ppl: 9.382433002631645] tot_loss[loss=2.248, over 31961111.23 frames., ppl: 9.47334954390668], batch 2022-06-192022-06-19 07:09:17,218 INFO [train.py:442022-2022-06-19 07:09:17,387 INFO [train.py:445] Epoch 27, batch 9200, loss[loss=2.194, over 33200.00 frames., ppl: 8.968417959041364] tot_loss[loss=2.251, over 3154432022-06-12022-06-19 07:10:32,991 INFO [train.py:42022-06-19 07:10:32,999 2022-06-19 07:10:33,114 INFO [train.py:445] Epoch 27, batch 9400, loss[loss=2.23, over 24000.00 frames., ppl: 9.297054142837286] tot_loss[loss=2022-06-19 02022-06-19 07:11:42,387 INFO [train.py:445] Epoch 27, batch 9600, loss[loss=2.181, over 40400.00 frames., ppl: 8.851071806052166] tot_loss[loss=2.251, over 31594726.15 frames., ppl: 9.49255297790464], bat2022-06-19 07:12:53,078 INFO [train.py:445] Epoch 27, batch 2022-06-19 072022-06-19 07:12:53,391 INFO [train.py:445] Epoch 27, batch 9800, loss[loss=2.144, over 40800.00 frames., ppl: 8.534723184973794] tot_loss[loss=22022-06-19 02022-06-19 07:14:09,721 INFO [train.py:445] Epoch 27, 2022-06-19 07:14:09,836 INFO [train.py:445] Epoch 27, batch 10000, loss[loss=2.2, over 31600.00 frames., ppl: 9.020864303908663] tot_loss[loss=2.251, over 31428238.73 frames., ppl: 9.499588222587812], batch size: 400
+2022-02022-06-19 07:14:10,023 INFO [train.py:480] Epoch 27, vali2022-06-19 07:14:10,023 INFO [train.py:480] Epoch 27, validation: l2022-06-19 2022-06-19 07:15:23,547 INFO [train.p2022-06-19 07:15:23,6282022-06-19 07:15:23,643 INFO [train.py:445] Epoch 27, batch 10200, loss[loss=2.215, over 25600.00 frames., ppl: 9.165550686773502] tot_loss[loss=2.2022-06-19 07:16:36,312 INFO [train.py:445] Epoch 27, batch 10400, loss[loss=2.208, over 53847.00 frames., ppl: 9.094449998139408] tot_loss[loss=2.251, over 31985871.45 frames., ppl: 9.494385235812985], batch size: 134
+2022-06-19 2022-06-19 07:17:52,696 INFO [train.p2022-06-19 07:17:52,765 INFO [train.py:445] Epoch 27, batch 10600, loss[loss=2.198, over 22800.00 frames., ppl: 9.010617257340863] tot_loss[loss=2.251, over 32238658.47 fr2022-06-19 07:19:02,567 INFO [train.py:445] Epoch 27, batch 10800, loss[loss=2.144, over 47200.00 frames., ppl: 8.532178118430988] tot_loss[loss=2.253, over 31391270.92 frames., ppl: 9.51852816950261], batch size: 400
+2022-06-192022-06-19 07:20:13,014 INFO [train.py:445] Epoch 27, batch 12022-06-19 07:20:13,035 INFO [train.py:445] Epoch 27, batch 11000, loss[loss=2.187, over 30400.00 frames., ppl: 8.904313061712] tot_loss[loss=2.25,2022-06-19 07:21:22,661 INFO [train.py:445] Epoch 27, batch 2022-06-19 07:21:22,701 INFO [train.py:445] Epoch 27, batch 11200, loss[loss=2.196, over 42411.00 frames., ppl: 8.989462169951516] tot_loss[loss=2.253, over 312022-06-192022-06-19 07:22:35,983 INFO [train.py:445] Epoch 2022-06-19 07:22:35,986 INFO [train.py:445] Epoch 27, batch 11400, loss[loss=2.294, over 14800.00 frames., ppl: 9.915471189962034] tot_loss[loss=2.253, over 312022-06-12022-06-19 07:23:51,033 INFO [train.py:442022-06-19 07:23:51,242 INFO [train.py:445] Epoch 27, batch 11600, loss[loss=2.187, over 34800.00 frames., ppl: 8.908010071689931] tot_loss[loss=2.255, over 31426529.92 f2022-06-19 07:25:03,588 INFO [train.py:445] Epoch 27, ba2022-06-19 07:25:03,873 INFO [train.py:445] Epoch 27, batch 11800, loss[loss=2.146, over 53600.00 frames., ppl: 8.554393588723372] tot_loss[loss=2.253, over 3167482022-06-12022-06-19 07:26:17,665 INFO [train.py:445] Epoch 27, batch 12000, loss[loss=2.264, over 15600.00 frames., ppl: 9.623444180827123] tot_loss[loss=2.252, over 31998298.38 frames., ppl: 9.510189526530596], batch s2022-06-12022-06-19 07:27:31,091 INFO [train.py:445] Epoch 27, batch 12200, loss[loss=2.285, over 17200.00 frames., ppl: 9.827127870760924] tot_loss[loss=2.253, over 31955426.99 frames., ppl: 9.515205118971263], batch 2022-06-19 07:28:43,612 INFO [train.py:445] Epoch 27, 2022-06-19 02022-06-19 07:28:43,931 INFO [train.py:445] Epoch 27, batch 12400, loss[loss=2.137, over 41808.00 frames., ppl: 8.475793854522193] tot_loss[loss=2.252, o2022-06-2022-06-19 07:29:56,589 INFO [train.py:445] Epoch 27, batch 12600, loss[loss=2.163, over 38000.00 frames., ppl: 8.693623655174298] tot_loss[loss=2.253, over 31891922.05 frames., ppl: 9.51883724352949], batch si2022-06-2022-06-19 07:30:26,859 INFO [train.py:445]2022-06-19 07:30:27,015 INFO [train.py:445] Epoch 28, batch 0, loss[loss=2.213, over 60903.00 frames., ppl: 9.146455610374742] tot_loss[loss=2.213, over 60903.002022-06-19 07:31:43,016 INFO [train.py:445] Epoch 22022-06-19 07:32022-06-19 07:31:43,758 INFO [train.py:445] Epoch 28, batch 200, loss[loss=2.237, over 56432.00 frames., ppl: 9.364613794931142] tot_loss[loss=2.249,2022-06-12022-06-19 07:32:56,607 INFO [train.py:4452022-06-19 07:32:56,864 INFO [train.py:445] Epoch 28, batch 400, loss[loss=2.16, over 34800.00 frames., ppl: 8.672991063016257] tot_loss[loss=2.237, over 5708853.00 2022-06-19 07:34:11,165 INFO [train.py:445] Epoch 2022-06-19 07:34:11,309 INFO [train.py:445] Epoch 28, batch 600, loss[loss=2.196, over 25600.00 frames., ppl: 8.990489508755452] tot_loss[loss=2.233, over 8316525.84 f2022-06-19 07:35:25,312 INFO [train.py:445] Epoch 28, batch 800, loss[loss=2.164, over 48441.00 frames., ppl: 8.705749197689222] tot_loss[loss=2.237, over 10528565.93 frames., ppl: 9.364594669501733], batch size: 201
+2022-02022-06-19 07:36:38,304 INFO [train.py:445] Epoch 28, batch 1000, loss[loss=2.155, over 30000.00 frames., ppl: 8.632196210848543] tot_loss[loss=2.235, over 12823344.35 frames., ppl: 9.350975998610416], batch siz2022-062022-06-19 07:37:49,319 INFO [train.py:42022-06-19 07:372022-06-19 07:37:49,400 INFO [train.py:445] Epoch 28, batch 1200, loss[loss=2.192, over 20800.00 frames., ppl: 8.950368561891192] tot_loss[loss=2.239, ove2022-062022-06-19 07:39:02,286 INFO [train.py:445] Epoch 28, batch 1400, loss[loss=2.169, over 37200.00 frames., ppl: 8.746509039946824] tot_loss[loss=2.236, over 16331775.36 frames., ppl: 9.359553528260545], batch siz2022-06-19 07:40:14,863 INFO [train.py:445] Epoch 28, bat2022-06-19 07:40:14,971 INFO [train.py:445] Epoch 28, batch 1600, loss[loss=2.192, over 25600.00 frames., ppl: 8.952367565704412] tot_loss[loss=2.241, over 17652022-062022-06-19 07:41:26,165 INFO [train.py:445] Epoch 28, batch 1800, loss[loss=2.176, over 25600.00 frames., ppl: 8.815240391252638] tot_loss[loss=2.238, over 19151080.36 frames., ppl: 9.376359206905864], batch si2022-06-19 07:42:41,176 INFO [train.py:445] Epoch 282022-06-19 07:42:41,342 INFO [train.py:445] Epoch 28, batch 2000, loss[loss=2.14, over 42400.00 frames., ppl: 8.49546227786937] tot_loss[loss=2.242, over 19880638.132022-06-2022-06-19 07:43:56,885 INFO [train.py:442022-06-19 07:43:56,928 INFO [train.py:445] Epoch 28, batch 2200, loss[loss=2.263, over 16800.00 frames., ppl: 9.611592408900226] tot_loss[loss=2.237, over 21653928.36 f2022-062022-06-19 07:45:12,932 INFO [train.py:445] Epoch 28, ba2022-06-19 07:45:12,959 INFO [trai2022-06-19 07:45:12,963 INFO [train.py:445] Epoch 28, batch 2400, loss[loss=2.351, over 12400.00 frames., ppl: 10.495652022-06-19 07:46:25,357 INFO [train.py:445] Epoch 282022-06-19 07:46:25,467 INFO [train.py:445] Epoch 28, batch 2600, loss[loss=2.223, over 23600.00 frames., ppl: 9.231960930152443] tot_loss[loss=2.243, over 22857882.572022-06-2022-06-19 07:47:38,536 INFO [train.py:445]2022-06-19 07:47:38,675 INFO [train.py:445] Epoch 28, batch 2800, loss[loss=2.16, over 34400.00 frames., ppl: 8.674186641219103] tot_loss[loss=2.24, over 24192271.65 f2022-06-19 07:48:49,793 INFO [train.py:445] Epoch 28, batch 302022-06-19 07:48:49,851 INFO [train.py:445] Epoch 28, batch 3000, loss[loss=2.178, over 24800.00 frames., ppl: 8.82450721938192] tot_loss[loss=2.24, over 242022-06-19 07:50:01,466 INFO [train.py:445] Epoch2022-2022-06-19 07:50:01,566 INFO [train.py:445] Epoch 28, batch 3200, loss[loss=2.2, over 24000.00 frames., ppl: 9.028408800414187] tot_loss[loss=2.244, over 25227704.52022-06-2022-06-19 07:51:15,937 INFO [train.p20222022-06-19 07:51:16,243 INFO [train.py:445] Epoch 28, batch 3400, loss[loss=2.144, over 42400.00 frames., ppl: 8.534714582023339] tot_loss[loss=2.241, over 26190332.87 f2022-06-19 07:52:34,337 INFO [train.py:445] Epoch 28, batch 3600, loss[loss=2.17, over 53200.00 frames., ppl: 8.754536951367578] tot_loss[loss=2.242, over 26792648.20 frames., ppl: 9.414772501681048], batch size: 400
+2022-06-2022-06-19 07:53:50,324 INFO [train.py:445]2022-06-19 07:53:50,513 INFO [train.py:445] Epoch 28, batch 3800, loss[loss=2.186, over 52000.00 frames., ppl: 8.90282007531105] tot_loss[loss=2.242, over 27460665.562022-06-19 07:55:02,055 INFO [train.py:445] Epoch 28, batch 42022-06-19 07:55:02,061 INFO [train.py:445] Epoch 28, batch 4000, loss[loss=2.179, over 18000.00 frames., ppl: 8.838814645452647] tot_loss[loss=2.243, over 272022-06-19 07:56:14,719 INFO [train.py:445] 2022-06-19 07:56:14,843 INFO [train.py:445] Epoch 28, batch 4200, loss[loss=2.216, over 20000.00 frames., ppl: 9.169002627443676] tot_loss[loss=2.244, over 27929591.13 frames22022-06-19 07:57:27,148 INFO [train.py:445]2022-06-19 07:57:27,262 INFO [train.py:445] Epoch 28, batch 4400, loss[loss=2.181, over 21600.00 frames., ppl: 8.852712614465895] tot_loss[loss=2.245, over 28184222.33 frame2022-06-19 07:58:40,875 INFO [train.py:445] E2022-02022-06-19 07:58:41,214 INFO [train.py:445] Epoch 28, batch 4600, loss[loss=2.157, over 48400.00 frames., ppl: 8.641823974078275] tot_loss[loss=2.243, over 28629096.482022-06-19 07:59:52,939 INFO [train.py:445]2022-06-19 07:59:52,984 INFO [train.py:445] Epoch 28, batch 4800, loss[loss=2.18, over 23200.00 frames., ppl: 8.846410522777886] tot_loss[loss=2.245, over 29014270.44 frames.2022-06-19 08:01:08,052 INFO [train.py:4452022-06-192022-06-2022-06-19 08:01:08,281 INFO [train.py:445] Epoch 28, batch 5000, loss[loss=2.169, over 30800.00 frames., ppl: 8.749056095977492] tot_loss[loss=2.243, over 2922022-06-19 08:02:22,329 INFO [train.py:445] Epoch 28, batch 5200, loss[loss=2.201, over 15602022-06-19 08:02:22,383 INFO [train.py:445] Epoch 28, batch 5200, loss[loss=2.199, over 21600.00 frames., ppl: 9.0117149374222022-06-19 08:03:34,851 INFO [train.py:445] Epoch 22022-06-12022-06-19 08:03:34,930 INFO [train.py:445] Epoch 28, batch 5400, loss[loss=2.164, over 31600.00 frames., ppl: 8.702764413718668] tot_loss[loss=2.245, over 202022-06-19 08:04:47,336 INFO [train.py:445] Epoch 22022-06-19 08:04:47,359 INFO [train.py:445] Epoch 28, batch 5600, loss[loss=2.157, over 28000.00 frames., ppl: 8.646176391046218] tot_loss[loss=2.246, over 300880072022-06-19 08:05:59,319 INFO [train.py:445] Epoch 28, batch 5800, loss[loss=2.174, over 30400.00 frames., ppl: 8.79517322285836] tot_loss[loss=2.25, over 29275640.87 frames., ppl: 9.49083741524496], batch size: 400
+2022-06-19 08:07:12,716 INFO [train.py:445] Epoc2022-06-19 08:07:13,038 INFO [train.py:445] Epoch 28, batch 6000, loss[loss=2.167, over 40400.00 frames., ppl: 8.728951557116458] tot_loss[loss=2.245, over 30529973.96 fr2022022-06-19 08:08:26,394 INFO [train.py:445] E2022-06-19 08:08:26,552 INFO [train.py:445] Epoch 28, batch 6200, loss[loss=2.191, over 38000.00 frames., ppl: 8.94067437456359] tot_loss[loss=2.245, over 30736957.90 fra2022022-06-19 08:09:39,057 INFO [train.py:445] Epoch 28, batch2022-06-19 08:09:39,268 INFO [train.py:445] Epoch 28, batch 6400, loss[loss=2.147, over 41200.00 frames., ppl: 8.556534299783985] tot_loss[loss=2.246, over 2022-06-19 08:10:50,697 INFO [train.py:445] Epo2022-02022-06-12022-06-19 08:10:51,258 INFO [train.2022-06-19 08:10:51,304 INFO [train.py:445] Epoch 28, batch 6600, loss[loss=2.203, over 75200.00 frames., ppl: 9.0554602022022-06-19 08:12:02,331 INFO [train.py:445] Epoch 22022-06-19 08:12:02,366 INFO [train.py:445] E2022-06-19 08:12:02,381 INFO [train.py:445] Epoch 28, batch 6800, loss[loss=2.194, over 19200.00 frames., ppl: 8.969852022-2022-06-19 08:13:14,826 INFO [train.py:445] Epoch 22022-06-19 08:13:14,841 INFO [train.py:445] Epoch 28, batch 7000, loss[loss=2.156, over 37200.00 frames., ppl: 8.63794704937728] tot_loss[loss=2.247, over 310712022-06-19 08:14:26,087 INFO [train.py:445] Epoch2022-02022-06-2022-06-19 08:14:26,345 INFO [train.py:445] Epoch 28, batch 7200, loss[loss=2.144, over 38000.00 frames., ppl: 8.533252587147174] tot_loss[loss=2.247, over20222022-06-19 08:15:40,370 INFO [train.py:445] Epoch 2022-06-12022-06-19 08:15:40,532 INFO [train.py:445] Epoch 28, batch 7400, loss[loss=2.144, over 36000.00 frames., ppl: 8.53456466389907] tot_loss[loss=2.246, over 32022022-06-19 08:16:52,093 INFO [train.py:445]2022-06-19 08:16:52,153 INFO [train.py:445] Epoch 28, batch 7600, loss[loss=2.222, over 17200.00 frames., ppl: 9.227020346438072] tot_loss[loss=2.248, over 31192392.86 fram2022-06-19 08:18:04,149 INFO [train.py:445] Ep2022-06-2022-06-19 08:18:04,358 INFO [train.py:445] Epoch 28, batch 7800, loss[loss=2.19, over 31829.00 frames., ppl: 8.931765505404236] tot_loss[loss=2.251, over 30757042.2022-06-19 08:19:16,550 INFO [train.py:445] Epoch 28,2022-06-2022-06-19 08:19:16,648 INFO [train.py:445] Epoch 28, batch 8000, loss[loss=2.182, over 26800.00 frames., ppl: 8.860932380827498] tot_loss[loss=2.247, over 2022022-06-19 08:20:26,285 INFO [train.py:445]2022-06-19 08:202022-06-19 08:20:26,525 INFO [train.py:445] Epoch 28, batch 8200, loss[loss=2.166, over 39200.00 frames., ppl: 8.721172996411186] tot_loss[loss=2.248, over 32022022-06-19 08:21:37,186 INFO [train.py:445] Epoch 28, batch 8400, loss[loss=2.153, over 30800.00 frames., ppl: 8.61061407621156] tot_loss[loss=2.251, over 31047318.33 frames., ppl: 9.493383663241978], batch size: 40202022-06-19 08:22:47,102 INFO [train.py:4452022-06-19 08:222022-06-19 08:22:47,181 INFO [train.py:445] Epoch 28, batch 8600, loss[loss=2.284, over 18800.00 frames., ppl: 9.81525320022343] tot_loss[loss=2.249, over 32022-06-19 08:24:03,221 INFO [train.py:445] Epoch 28, batch 82022-06-19 08:24:03,233 INFO [tra2022-06-19 08:24:03,378 INFO [train.py:445] Epoch 28, batch 8800, loss[loss=2.169, over 50800.00 frames., ppl: 8.75081445462022-06-19 08:25:19,050 INFO [train.py:445] Epoch 28, batch 9000, loss[loss=2.194, over 35200.00 frames., ppl: 8.972573944360862] tot_loss[loss=2.247, over 32056223.21 frames., ppl: 9.460367541291868], batch size: 400
+2022-06-19 08:26:28,847 INFO [train.py:445] Epoch 28, batch 9200, loss[loss=2.203, over 33600.00 frames., ppl: 9.05451966820218] tot_loss[loss=2.248, over 31829527.27 frames., ppl: 9.469835785843891], batch size: 400
+2022-2022-06-19 08:27:42,994 INFO [train.py:445]2022022-06-19 08:27:43,559 INFO [train.py:445] Epoch 28, batch 9400, loss[loss=2.148, over 75600.00 frames., ppl: 8.568392571769694] tot_loss[loss=2.25, over 31476582.52022-062022-06-19 08:28:53,212 INFO [train.py:445] 2022-06-19 08:28:53,297 INFO [train.py:445] Epoch 28, batch 9600, loss[loss=2.261, over 19200.00 frames., ppl: 9.589226788453217] tot_loss[loss=2.247, over 32185179.402022-06-19 08:30:03,405 INFO [train.py:445] Epoch 28, batch 9800, loss[loss=2.155, over 42400.2022-06-19 08:30:03,563 INFO [train.py:445] Epoch 28, batch 9800, loss[loss=2.161, over 58800.00 frames., ppl: 8.67792270262022-062022-06-19 08:31:19,949 INFO [train.py:445] Epoch 28, batch 10000, loss[loss=2.173, over 29200.00 frames., ppl: 8.78692007358575] tot_loss[loss=2.252, over 31264484.58 frames., ppl: 9.507261782651954], batch size: 400
+2022-06-19 08:31:19,950 INFO [train.py:469] Computing validation2022-02022-06-19 08:31:20,133 INFO [train.py:480] 2022-02022-2022-06-19 08:31:20,133 INFO [train.py:480] Epoch 28, validation:2022-02022-06-19 08:32:34,325 INFO [train.py:445] Epoch 28, batch 10200, loss[loss=2.19, over 34000.00 frames., ppl: 8.935069396893626] tot_loss[loss=2.253, over 31157129.71 frames., ppl: 9.513516469344005], batch size:2022-2022-06-19 08:33:44,986 INFO [train.py:445] Epoch 28, batch 10400, loss[loss=2.154, over 33600.00 frames., ppl: 8.619506696749372] tot_loss[loss=2.252, over 31273417.40 frames., ppl: 9.509916984571703], batch size2022-02022-06-19 08:34:58,861 INFO [train.py:445] Epoch 28, batch 10600, loss[loss=2.17, over 32400.00 frames., ppl: 8.757424558030335] tot_loss[loss=2.251, over 31635327.04 frames., ppl: 9.49464739243864], batch size:2022-2022-06-19 08:36:09,754 INFO [train.py:445] Epoch 28, ba2022-06-19 08:36:09,809 INFO [train.py:445] Epoch 28, batch 10800, loss[loss=2.181, over 26000.00 frames., ppl: 8.851225193868748] tot_loss[loss=2.251, over 32022-06-19 08:37:21,804 INFO [train.py:445] Epoch 22022-06-19 08:37:21,861 INFO [train.py:445] Epoch 28, batch 11000, loss[loss=2.259, over 15600.00 frames., ppl: 9.571299933375208] tot_loss[loss=2.254, over 31381472.32022-02022-06-19 08:38:32,896 INFO [train.py:445] Epoch 28, batch 11200, loss[loss=2.165, over 37200.00 frames., ppl: 8.715180274038982] tot_loss[loss=2.252, over 31353348.01 frames., ppl: 9.510752102847624], batch siz2022-062022-06-19 08:39:45,720 INFO [train.py:445] Epoch 28,2022-06-19 08:39:45,724 IN2022-06-19 08:39:45,820 INFO [train.py:445] Epoch 28, batch 11400, loss[loss=2.227, over 21600.00 frames., ppl: 9.268948306757858] t2022-06-19 08:40:58,494 INFO [train.py:445] Epoch 28, 2022-06-19 08:40:58,998 INFO [train.py:445] Epoch 28, batch 11600, loss[loss=2.168, over 68000.00 frames., ppl: 8.738937396612963] tot_loss[loss=2.254, over 31416892.2022-062022-06-19 08:42:09,602 INFO [train.py:445] E22022-06-19 08:42:09,756 INFO [train.py:445] Epoch 28, batch 11800, loss[loss=2.226, over 23200.00 frames., ppl: 9.260413981098807] tot_loss[loss=2.255, over 31236615.2022-062022-06-19 08:43:22,952 INFO [train.py:445] Ep2022-02022-06-19 08:43:23,117 INFO [train.py:445] Epoch 28, batch 12000, loss[loss=2.171, over 26400.00 frames., ppl: 8.766563638645213] tot_loss[loss=2.252, over 3172022-2022-06-19 08:44:35,458 INFO [train.py:445] Epoc2022-062022-06-19 08:44:35,619 INFO [train.py:445] Epoch 28, batch 12200, loss[loss=2.176, over 27200.00 frames., ppl: 8.811106605788803] tot_loss[loss=2.252, over 312022-02022-06-19 08:45:49,926 INFO [train.py:445] Epoch 28, batch 12400, loss[loss=2.193, over 19200.00 frames., ppl: 8.96219101320814] tot_loss[loss=2.253, over 31399536.64 frames., ppl: 9.518500171713134], batch size2022-06-19 08:47:03,298 INFO [train.py:445] Epoch 28, batch 12600, loss[loss=2.171, over 24400.00 frames., ppl: 8.76415112766853] tot_loss[loss=2.253, over 31645821.49 frames., ppl: 9.51266161962921], batch size: 400
+2022-06-2022-06-19 08:47:31,515 INFO [train.py:445] Epoch 29, batch 0, loss[loss=2.196, over 26400.00 frames., ppl: 8.990617132336359] tot_loss[loss=2.196, over 26400.00 frames., ppl: 8.990617132336359], batch si2022-06-19 08:48:49,634 INFO [train.py:445] Epoch 2022-06-19 08:48:49,719 INF2022-06-19 08:48:50,861 INFO [train.py:445] Epoch 29, batch 200, loss[loss=2.336, over 72899.00 frames., ppl: 10.343110320528059] tot_loss[2022-06-2022-06-19 08:50:03,140 INFO [train.py:445] E2022-06-19 08:50:03,263 INFO [train.py:445] Epoch 29, batch 400, loss[loss=2.134, over 40400.00 frames., ppl: 8.446785615286231] tot_loss[loss=2.231, over 5903503.02022-062022-06-19 08:51:16,430 INFO [train.py:4452022-06-19 08:51:16,626 INFO [train.py:445] Epoch 29, batch 600, loss[loss=2.153, over 32000.00 frames., ppl: 8.61139375495626] tot_loss[loss=2.23, over 8609451.96 fr2022-06-19 08:52:30,153 INFO [train.py:445] Epoch 29, batch 800, loss[loss=2.162, over 30800.00 frames., ppl: 8.68459336596336] tot_loss[loss=2.241, over 10304087.08 frames., ppl: 9.404885004947937], batch size: 400
+2022-06-192022-06-19 08:53:46,133 INFO [train.py:445]2022-2022-06-19 08:53:46,414 INFO [train.py:445] Epoch 29, batch 1000, loss[loss=2.169, over 40800.00 frames., ppl: 8.751759350395005] tot_loss[loss=2.235, over 122022-06-19 02022-06-19 08:55:02,424 INFO [train.py20222022-06-19 08:55:02,726 INFO [train.py:445] Epoch 29, batch 1200, loss[loss=2.133, over 41600.00 frames., ppl: 8.436041196607462] tot_loss[loss=2.23, over 15008972.2022-06-19 08:56:16,438 INFO [train.py:445] Epoch2022-06-192022-06-19 08:56:16,808 INFO [train.py:445] Epoch 29, batch 1400, loss[loss=2.131, over 46800.00 frames., ppl: 8.420283770512452] tot_loss[loss=2.232, over 1682022-06-192022-06-19 08:57:28,879 INFO [train.py:4452022-06-19 08:57:28,906 INFO [train.py:445] Epoch 29, batch 1600, loss[loss=2.158, over 24800.00 frames., ppl: 8.650628639999951] tot_loss[loss=2.232, over 18188032.22022-06-12022-06-19 08:58:40,462 INFO [train.py:4452022-06-19 08:58:40,703 INFO [train.py:445] Epoch 29, batch 1800, loss[loss=2.208, over 33200.00 frames., ppl: 9.098606181648572] tot_loss[loss=2.233, over 19687335.562022-062022-06-19 08:59:51,818 INFO [train.py:445] Epoch 29, batch 2000, loss[loss=2.173, over 29200.00 frames., ppl: 8.786129013679139] tot_loss[loss=2.245, over 19359758.27 frames., ppl: 9.439767961756143], batch s2022-06-19 09:01:03,920 INFO [train.py:445] Epoc2022-06-2022-06-19 09:01:04,125 INFO [train.py:445] Epoch 29, batch 2200, loss[loss=2.149, over 35600.00 frames., ppl: 8.572141631687689] tot_loss[loss=2.241, over 2116662022-06-192022-06-19 09:02:16,271 INFO [train.py:445] Epoch 29, batch 2400, loss[loss=2.164, over 29600.00 frames., ppl: 8.70443912497675] tot_loss[loss=2.246, over 21361177.97 frames., ppl: 9.447996518866866], batch s2022-06-12022-06-19 09:03:29,128 INFO [train.py2022-06-19 09:03:29,213 INFO 2022-06-19 09:03:29,321 INFO [train.py:445] Epoch 29, batch 2600, loss[loss=2.19, over 30000.00 frames., ppl: 8.933258750596274] tot_loss[los2022-06-19 09:04:40,830 INFO [train.py:445] Epo2022-02022-06-19 09:04:41,181 INFO [train.py:445] Epoch 29, batch 2800, loss[loss=2.143, over 49600.00 frames., ppl: 8.525118710727716] tot_loss[loss=2.237, over 24936377.72022-06-19 09:05:52,250 INFO [train.py:445] Epoch 29, batch 3000, loss[loss=2.144, over 34800.00 frames., ppl: 8.536874696678296] tot_loss[loss=2.243, over 24628311.38 frames., ppl: 9.422609349066848], batch size: 400
+2022-06-19 09:07:05,818 INFO [train.py:445] Epoch 29, batch 3200, loss[loss=2.15, over 40800.00 frames., ppl: 8.581645265833181] tot_loss[loss=2.243, over 25315480.14 frames., ppl: 9.41846921385709], batch size: 400
+2022-06-19 09:08:20,097 INFO [train.py:445] Epoch 22022-06-19 09:08:20,268 INFO [train.py:445] Epoch 29, batch 3400, loss[loss=2.154, over 30000.00 frames., ppl: 8.618948996153142] tot_loss[loss=2.24, over 26682263.25 2022-06-19 09:09:30,266 INFO [train.py:445] Ep2022-06-19 02022-06-19 09:09:30,402 INFO [train.py:445] Epoch 29, batch 3600, loss[loss=2.198, over 22000.00 frames., ppl: 9.004822780350032] tot_loss[loss=2.242, over 2672022-06-12022-06-19 09:10:42,294 INFO [train.2022-06-19 09:10:42,814 INFO [train.py:445] Epoch 29, batch 3800, loss[loss=2.168, over 71200.00 frames., ppl: 8.738802950026706] tot_loss[loss=2.243, over 27136295.40 fram2022-06-19 09:11:58,019 INFO [train.py:445] Epoch 29, batch 4000, loss[loss=2.208, over 21200.00 frames., ppl: 9.099693334444742] tot_loss[loss=2.242, over 27631155.80 frames., ppl: 9.413088418968718], batch size: 400
+2022-06-19 09:13:10,249 INFO [train.py:445] Ep2022-06-19 09:13:10,288 INFO [tr2022-06-19 09:13:10,887 INFO [train.py:445] Epoch 29, batch 4200, loss[loss=2.161, over 76800.00 frames., ppl: 8.677466199588482] tot_loss[l2022-06-19 09:14:24,137 INFO [train.py:445] Epoch 29, batch 4400, loss[loss=2.202, over 26800.00 frames., ppl: 9.043393187921927] tot_loss[loss=2.24, over 28975138.57 frames., ppl: 9.389153911854976], batch size: 400
+2022-06-12022-06-19 09:15:36,686 INFO [train.py:2022-06-12022-06-19 09:15:36,746 INFO [train.py:445] Epoch 29, batch 4600, loss[loss=2.199, over 19200.00 frames., ppl: 9.012327326551292] tot_loss[loss=2.248, over 28122022-06-2022-06-19 09:16:50,169 INFO [train.py:442022-06-192022-06-19 09:16:502022-06-19 09:16:50,353 INFO [train.py:445] Epoch 29, batch 4800, loss[loss=2.167, over 29600.00 frames., ppl: 8.73597296493286] tot_loss[lo2022-062022-06-19 09:18:03,388 INFO [train.py:445] Epoch 29, batch 5000, loss[loss=2.165, over 40000.00 frames., ppl: 8.717257551671604] tot_loss[loss=2.246, over 29032702.80 frames., ppl: 9.451729071841346], batch siz2022-06-19 09:19:16,578 INFO [train.py:445] Epoch 29, batch 5200, loss[los2022-06-19 09:19:16,579 INFO [train.py:445] Epoch 29, batch 5200, loss[loss=2.207, over 20400.00 frames., ppl: 9.085777917411281] tot_loss[loss2022-062022-06-19 09:20:27,371 INFO [train.py:445] Ep2022-062022-06-19 09:20:27,442 INFO [train.py:445] Epoch 29, batch 5400, loss[loss=2.22, over 15200.00 frames., ppl: 9.209796769315792] tot_loss[loss=2.248, over 22022-06-19 09:21:40,771 INFO [train.py:445] Epoch 29, batch 5600, loss[loss=2.152, over 44000.00 frames., ppl: 8.605033314088455] tot_loss[loss=2.246, over 29795445.07 frames., ppl: 9.446943968369048], batch size: 400
+2022-06-19 09:22:55,177 INFO [train.py:445] Epoch 29, batch 2022-06-19 09:222022-06-19 09:22:55,247 INFO [train.py:445] Epoch 29, batch 5800, loss[loss=2.213, over 26000.00 frames., ppl: 9.140510121724189] tot_loss[los2022-06-19 09:24:08,323 INFO [train.py:445] Epoch2022022-06-19 09:24:08,422 INFO [train.py:445] Epoch 29, batch 6000, loss[loss=2.246, over 20400.00 frames., ppl: 9.447886026911908] tot_loss[loss=2.246, over 30219686.2022-06-19 09:25:18,068 INFO [train.py:445] Epoch 29, batch 62022-06-19 09:25:18,134 INFO [train.py:445] Epoch 29, batch 6200, loss[loss=2.204, over 21200.00 frames., ppl: 9.060092669887498] tot_loss[loss=2.244, over 32022-06-2022-06-19 09:26:32,369 INFO [train.py:445] Epoch 22022-06-19 09:26:32,419 INFO [train.py:445] Epoch 29, batch 6400, loss[loss=2.153, over 34000.00 frames., ppl: 8.608431995405113] tot_loss[loss=2.247, over 3042022-062022-06-19 09:27:45,016 INFO [train.py:445] E2022-062022-06-19 09:27:45,369 INFO [train.py:445] Epoch 29, batch 6600, loss[loss=2.149, over 59200.00 frames., ppl: 8.575046074282664] tot_loss[loss=2.247, over 302022-06-2022-06-19 09:28:57,915 INFO [train.py:2022-02022-062022-06-19 09:28:58,065 INFO [train.py:445] Epoch 29, batch 6800, loss[loss=2.228, over 20800.00 frames., ppl: 9.279945687218639] tot_loss[loss=2.248, over 302022-06-2022-06-19 09:30:10,480 INFO [train.py:445] Epoch 29, batch 7000, l2022-06-19 09:30:10,509 INFO [train.py:445] Epoch 29, batch 7000, loss[loss=2.223, over 20800.00 frames., ppl: 9.23192784171504] tot_loss[loss2022-06-12022-06-19 09:31:21,056 INFO [train.py2022-06-19 09:31:21,241 INFO [train.py:445] Epoch 29, batch 7200, loss[loss=2.216, over 27200.00 frames., ppl: 9.16897086676463] tot_loss[loss=2.249, over 30449537.32 fra2022-06-19 09:32:33,004 INFO [train.py:445] Epo2022-06-19 09:32022-06-19 09:32:33,403 INFO [train.py:445] Epoch 29, batch 7400, loss[loss=2.135, over 56000.00 frames., ppl: 8.456444813183433] tot_loss[loss=2.249, over 2022-06-19 09:33:45,689 INFO [train.py:445] Epoch 29, batch 7600, loss[loss=2.23, over 67882.00 frames., ppl: 9.296751895269038] tot_loss[loss=2.249, over 30678577.54 frames., ppl: 9.482734593180124], batch size: 134
+2022-06-19 09:34:57,282 INFO [train.py:445] Epo2022-06-19 09:34:57,294 INFO [2022-06-19 09:34:57,299 INFO [train.py:445] Epoch 29, batch 7800, loss[loss=2.213, over 24400.00 frames., ppl: 9.139496904467482] tot_loss[lo2022-06-19 09:36:08,752 INFO [train.py:445] Epoch 292022-062022-06-19 09:36:08,972 INFO [train.py:445] Epoch 29, batch 8000, loss[loss=2.149, over 34800.00 frames., ppl: 8.58008778770642] tot_loss[loss=2.25, over 3097332022-06-19 09:37:23,090 INFO [train.py:445] Epoch 29, batch 8200, loss[loss=2.196, over 22400.00 frames., ppl: 8.991264435677323] tot_loss[loss=2.25, over 30998103.73 frames., ppl: 9.484558499668134], batch size: 400
+2022-06-19 09:38:36,829 INFO [train.py:445] Epoch 29, batch 8400, loss[loss=2.147, over 46800.00 frames., ppl: 8.562345262947293] tot_loss[loss=2.249, over 31184629.47 frames., ppl: 9.481880284496459], batch size: 400
+2022-06-19 09:39:51,243 INFO [train.py:445] Epoch 29, batch 8600, loss[loss=2.175, over 33200.00 frames., ppl: 8.800528240287148] tot_loss[loss=2.249, over 31369886.93 frames., ppl: 9.476890594892918], batch size: 400
+2022-2022-06-19 09:41:06,866 INFO [train.py:445] Epoch 29, batch 8800, loss[loss=2.175, over 48000.00 frames., ppl: 8.805725905680722] tot_loss[loss=2.247, over 31470738.09 frames., ppl: 9.462459083656587], batch siz2022-062022-06-19 09:42:15,621 INFO [train.py:445] Epoch 29, batch 9000, loss2022-06-19 09:42:15,710 INFO [train.py:445] Epoch 29, batch 9000, loss[loss=2.193, over 26000.00 frames., ppl: 8.963758400388855] tot_loss[lo2022-062022-06-19 09:43:28,223 INFO [train.p2022-06-19 09:432022-06-19 09:43:28,316 INFO [train.py:445] Epoch 29, batch 9200, loss[loss=2.233, over 23200.00 frames., ppl: 9.324625236366462] tot_loss[loss=2.248, over 3182022-06-19 09:44:43,652 INFO [train.py:445] Epoch 29, batch 9400, loss[loss=2.254, over 18400.00 frames., ppl: 9.521877556038167] tot_loss[loss=2.249, over 31763060.17 frames., ppl: 9.47442535572741], batch size: 400
+2022-06-19 09:45:54,296 INFO [train.py:445]2022-06-19 09:45:54,339 INFO [train.py:445] Epoch 29, batch 9600, loss[loss=2.16, over 27200.00 frames., ppl: 8.67075661098201] tot_loss[loss=2.251, over 31254457.45 frames.,2022-06-19 09:47:05,496 INFO [train.py:4452022-02022-06-19 09:47:05,682 INFO [train.py:445] Epoch 29, batch 9800, loss[loss=2.204, over 31600.00 frames., ppl: 9.061086833332027] tot_loss[loss=2.249, over 31580307.49 fr2022-062022-06-19 09:48:15,965 INFO [train.py:445] Epoch2022-06-19 09:48:16,009 INFO [train.py:445] Epoch 29, batch 10000, loss[loss=2.252, over 18000.00 frames., ppl: 9.508659483375581] tot_loss[loss=2.25, over 31644363.49 frames., ppl: 9.48328479201952], batch size: 400
+2022-06-19 09:482022-062022-06-19 09:48:16,195 INFO 2022-06-19 2022-062022-06-19 09:48:16,195 INFO [train.py:480] Epoch 29, validation: loss=22022-06-19 09:49:29,716 INFO [train.py:445] Epoch 29, batch 12022-06-19 09:49:29,849 INFO [train.py:445] Epoch 29, batch 10200, loss[loss=2.171, over 25600.00 frames., ppl: 8.768746341569669] tot_loss[loss=2.248, over 32022-06-19 09:50:41,381 INFO [train.py:445] Epoch 29, batch 10400, loss[loss=2.206, over 30800.00 frames., ppl: 9.077119434192253] tot_loss[loss=2.252, over 31343833.42 frames., ppl: 9.504086077463581], batch size: 400
+2022-02022-06-19 09:51:54,542 INFO [train.py:445] Epo2022-06-19 09:51:54,656 INFO [train.py:445] Epoch 29, batch 10600, loss[loss=2.14, over 34000.00 frames., ppl: 8.49883222175705] tot_loss[loss=2.249, over 31962804.352022-2022-06-19 09:53:07,162 INFO [train.py:445] Epoch 29, batch 10800, loss[loss=2.117, over 47600.00 frames., ppl: 8.302299001229073] tot_loss[loss=2.249, over 31921086.17 frames., ppl: 9.477008107624796], batch size:2022-2022-06-19 09:54:19,844 INFO [train.py:44520222022-06-19 09:54:20,103 INFO [train.py:445] Epoch 29, batch 11000, loss[loss=2.145, over 44400.00 frames., ppl: 8.541145476167273] tot_loss[loss=2.248, over 321452982022-06-2022-06-19 09:55:37,593 INFO [train.py:445] Epoch 29, batch 11200, loss[loss=2.172, over 56000.00 frames., ppl: 8.779448952237013] tot_loss[loss=2.248, over 32125255.19 frames., ppl: 9.471364339221655], batch 2022-06-19 09:56:46,699 INFO [train.py:445] Epoch 29, ba2022-06-19 09:56:46,832 INFO [train.py:445] Epoch 29, batch 11400, loss[loss=2.206, over 25600.00 frames., ppl: 9.078893291833253] tot_loss[loss=2.248, over 32180222022-06-12022-06-19 09:58:01,082 INFO [train.py:2022-06-19 09:58:01,106 INFO [train.py:445] Epoch 29, batch 11600, loss[loss=2.232, over 20400.00 frames., ppl: 9.322821711364238] tot_loss[loss=2.25, over 32072796.72 fra2022-06-19 09:59:14,702 INFO [train.py:445] Epoch 29, batch 11800, loss[loss=2.182, over 35600.00 frames., ppl: 8.865540598546836] tot_loss[loss=2.252, over 31935272.87 frames., ppl: 9.502314054747671], batch size: 400
+2022-06-19 10:00:25,986 INFO [train.py:445] Epoch 2022-06-19 10:00:25,989 INFO [train.py:445] Epoch 29, batch 12000, loss[loss=2.347, over 8400.00 frames., ppl: 10.449534005826086] tot_loss[loss=2.25, over 31966848.74 f2022-06-19 10:01:36,110 INFO [train.py:445] Ep2022-06-192022-06-19 10:01:2022-06-19 10:01:36,526 INFO [train.py:445] Epoch 29, batch 12200, loss[loss=2.197, over 55341.00 frames., ppl: 8.998504095000007] tot_loss[loss=2.2022-06-19 10:02:48,224 IN2022-06-19 10:02:48,762 INFO [train.py:445] Epoch 29, batch 12400, loss[loss=2.213, over 75200.00 frames., ppl: 9.1447681741992] tot_loss[loss=2.251, over 31678713.29 frames., ppl: 9.4973933111202022-06-19 10:04:00,703 INFO [train.py:4452022-06-19 10:04:00,707 INFO2022-06-19 10:04:01,067 INFO [train.py:445] Epoch 29, batch 12600, loss[loss=2.198, over 52400.00 frames., ppl: 9.007538018134868] tot_loss[loss=22022-06-19 10:04:30,936 INFO [train.py:600] 2022-06-19 12022-06-19 10:04:31,107 INFO [train.py:600] Done!
+in.py:600] Done!
+-19 09:48:16,195 INFO [train.py:480] Epoch 29, validation: loss=2.317, over 211809.00 frames., ppl: 10.146045501557857
+2022-06-19 09:49:29,714 INFO [train.py:445] Epoch 29, batch 10200, loss[loss=2.319, over 11200.00 frames., ppl: 10.168280890761586] tot_loss[loss=2.248, over 31606354.03 frames., ppl: 9.467503609611384], batch size: 400
+2022-06-19 09:50:41,580 INFO [train.py:445] Epoch 29, batch 10400, loss[loss=2.159, over 52400.00 frames., ppl: 8.660519848976822] tot_loss[loss=2.248, over 31645238.83 frames., ppl: 9.46872298277752], batch size: 400
+2022-06-19 09:51:54,434 INFO [train.py:445] Epoch 29, batch 10600, loss[loss=2.317, over 10000.00 frames., ppl: 10.143215700604472] tot_loss[loss=2.249, over 31556624.91 frames., ppl: 9.477599077917821], batch size: 400
+2022-06-19 09:53:06,989 INFO [train.py:445] Epoch 29, batch 10800, loss[loss=2.176, over 28800.00 frames., ppl: 8.807546048890144] tot_loss[loss=2.249, over 31576495.53 frames., ppl: 9.476558627175848], batch size: 400
+2022-06-19 09:54:19,831 INFO [train.py:445] Epoch 29, batch 11000, loss[loss=2.26, over 15200.00 frames., ppl: 9.585246786807604] tot_loss[loss=2.25, over 31226960.64 frames., ppl: 9.490878143005768], batch size: 400
+2022-06-19 09:55:37,362 INFO [train.py:445] Epoch 29, batch 11200, loss[loss=2.188, over 30000.00 frames., ppl: 8.921611239403445] tot_loss[loss=2.249, over 31554233.04 frames., ppl: 9.481458916190304], batch size: 400
+2022-06-19 09:56:46,797 INFO [train.py:445] Epoch 29, batch 11400, loss[loss=2.186, over 22000.00 frames., ppl: 8.90269507483592] tot_loss[loss=2.249, over 31683173.83 frames., ppl: 9.477871173848033], batch size: 400
+2022-06-19 09:58:00,994 INFO [train.py:445] Epoch 29, batch 11600, loss[loss=2.37, over 8000.00 frames., ppl: 10.694564221518203] tot_loss[loss=2.249, over 31727566.92 frames., ppl: 9.477978623873522], batch size: 400
+2022-06-19 09:59:14,614 INFO [train.py:445] Epoch 29, batch 11800, loss[loss=2.18, over 26400.00 frames., ppl: 8.844929366272972] tot_loss[loss=2.247, over 32108259.67 frames., ppl: 9.462453976617324], batch size: 400
+2022-06-19 10:00:26,072 INFO [train.py:445] Epoch 29, batch 12000, loss[loss=2.23, over 17600.00 frames., ppl: 9.302979215408081] tot_loss[loss=2.248, over 32036957.12 frames., ppl: 9.468546641062893], batch size: 400
+2022-06-19 10:01:36,397 INFO [train.py:445] Epoch 29, batch 12200, loss[loss=2.155, over 44800.00 frames., ppl: 8.629883983958301] tot_loss[loss=2.249, over 31799612.43 frames., ppl: 9.47992320223529], batch size: 400
+2022-06-19 10:02:48,134 INFO [train.py:445] Epoch 29, batch 12400, loss[loss=2.472, over 5600.00 frames., ppl: 11.840696403525873] tot_loss[loss=2.249, over 31831849.95 frames., ppl: 9.48232788169285], batch size: 400
+2022-06-19 10:04:00,775 INFO [train.py:445] Epoch 29, batch 12600, loss[loss=2.233, over 21200.00 frames., ppl: 9.32963027196154] tot_loss[loss=2.249, over 31919794.60 frames., ppl: 9.481206415308678], batch size: 400
+2022-06-19 10:04:31,058 INFO [checkpoint.py:75] Saving checkpoint to models/rnn_tied_2048_3/epoch-29.pt
+2022-06-19 10:04:36,368 INFO [train.py:600] Done!