[{"stream_name":"stdout","time":28.118101395,"data":"Device summary: {'torch': '2.10.0+cu128', 'cuda_available': True, 'mps_available': False, 'cuda_device': 'Tesla T4'}\n"}
,{"stream_name":"stderr","time":28.26626831,"data":"Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"}
,{"stream_name":"stderr","time":31.471690202,"data":"\rGenerating train split:   0%|          | 0/43410 [00:00\u003c?, ? examples/s]\rGenerating train split: 100%|██████████| 43410/43410 [00:00\u003c00:00, 559795.90 examples/s]\n"}
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,{"stream_name":"stderr","time":42.611298908,"data":"eights:  87%|████████▋ | 340/389 [00:00\u003c00:00, 1448.52it/s, Materializing param=roberta.encoder.layer.20.output.dense.bias]      \rLoading weights:  87%|████████▋ | 340/389 [00:00\u003c00:00, 1448.52it/s, Materializing param=roberta.encoder.layer.20.output.dense.bias]\rLoading weights:  88%|████████▊ | 341/389 [00:00\u003c00:00, 1448.52it/s, Materializing param=roberta.encoder.layer.20.output.dense.weight]\rLoading weights:  88%|████████▊ | 341/389 [00:00\u003c00:00, 1448.52it/s, Materializing param=roberta.encoder.layer.20.output.dense.weight]\rLoading weights:  88%|████████▊ | 342/389 [00:00\u003c00:00, 1448.52it/s, Materializing param=roberta.encoder.layer.21.attention.output.LayerNorm.bias]\rLoading weights:  88%|████████▊ | 342/389 [00:00\u003c00:00, 1448.52it/s, Materializing param=roberta.encoder.layer.21.attention.output.LayerNorm.bias]\rLoading weights:  88%|████████▊ | 343/389 [00:00\u003c00:00, 1448.52it/s, 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,{"stream_name":"stderr","time":42.655042499,"data":"RobertaForSequenceClassification LOAD REPORT from: FacebookAI/roberta-large\n"}
,{"stream_name":"stderr","time":42.655062837,"data":"Key                             | Status     | \n"}
,{"stream_name":"stderr","time":42.655067289,"data":"--------------------------------+------------+-\n"}
,{"stream_name":"stderr","time":42.655070948,"data":"lm_head.layer_norm.bias         | UNEXPECTED | \n"}
,{"stream_name":"stderr","time":42.655074507,"data":"lm_head.layer_norm.weight       | UNEXPECTED | \n"}
,{"stream_name":"stderr","time":42.655078258,"data":"roberta.embeddings.position_ids | UNEXPECTED | \n"}
,{"stream_name":"stderr","time":42.655082118,"data":"lm_head.bias                    | UNEXPECTED | \n"}
,{"stream_name":"stderr","time":42.655086099,"data":"lm_head.dense.weight            | UNEXPECTED | \n"}
,{"stream_name":"stderr","time":42.65508982,"data":"lm_head.dense.bias              | UNEXPECTED | \n"}
,{"stream_name":"stderr","time":42.655093414,"data":"classifier.out_proj.weight      | MISSING    | \n"}
,{"stream_name":"stderr","time":42.655096973,"data":"classifier.dense.weight         | MISSING    | \n"}
,{"stream_name":"stderr","time":42.655100453,"data":"classifier.dense.bias           | MISSING    | \n"}
,{"stream_name":"stderr","time":42.655104101,"data":"classifier.out_proj.bias        | MISSING    | \n"}
,{"stream_name":"stderr","time":42.655107703,"data":"\n"}
,{"stream_name":"stderr","time":42.65511099,"data":"Notes:\n"}
,{"stream_name":"stderr","time":42.655114514,"data":"- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"}
,{"stream_name":"stderr","time":42.655118531,"data":"- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"}
,{"stream_name":"stderr","time":42.926628258,"data":"warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.\n"}
,{"stream_name":"stdout","time":315.523866605,"data":"{'loss': '0.9398', 'grad_norm': '6.616', 'learning_rate': '1.801e-06', 'epoch': '0.07371'}\n"}
,{"stream_name":"stdout","time":586.620481297,"data":"{'loss': '0.3214', 'grad_norm': '1.572', 'learning_rate': '3.64e-06', 'epoch': '0.1474'}\n"}
,{"stream_name":"stdout","time":857.219462163,"data":"{'loss': '0.1862', 'grad_norm': '1.243', 'learning_rate': '5.478e-06', 'epoch': '0.2211'}\n"}
,{"stream_name":"stdout","time":1127.121457314,"data":"{'loss': '0.1648', 'grad_norm': '1.46', 'learning_rate': '7.316e-06', 'epoch': '0.2948'}\n"}
,{"stream_name":"stdout","time":1397.00008371,"data":"{'loss': '0.1429', 'grad_norm': '1.403', 'learning_rate': '9.154e-06', 'epoch': '0.3686'}\n"}
,{"stream_name":"stdout","time":1667.421726567,"data":"{'loss': '0.1297', 'grad_norm': '1.108', 'learning_rate': '9.89e-06', 'epoch': '0.4423'}\n"}
,{"stream_name":"stdout","time":1938.179366537,"data":"{'loss': '0.1267', 'grad_norm': '0.9931', 'learning_rate': '9.685e-06', 'epoch': '0.516'}\n"}
,{"stream_name":"stdout","time":2207.90973086,"data":"{'loss': '0.1204', 'grad_norm': '1.173', 'learning_rate': '9.48e-06', 'epoch': '0.5897'}\n"}
,{"stream_name":"stdout","time":2477.754685617,"data":"{'loss': '0.1171', 'grad_norm': '0.9435', 'learning_rate': '9.276e-06', 'epoch': '0.6634'}\n"}
,{"stream_name":"stdout","time":2748.155665366,"data":"{'loss': '0.1119', 'grad_norm': '1.221', 'learning_rate': '9.071e-06', 'epoch': '0.7371'}\n"}
,{"stream_name":"stdout","time":3018.364110114,"data":"{'loss': '0.1131', 'grad_norm': '1.184', 'learning_rate': '8.867e-06', 'epoch': '0.8108'}\n"}
,{"stream_name":"stdout","time":3288.7722489,"data":"{'loss': '0.112', 'grad_norm': '1.119', 'learning_rate': '8.662e-06', 'epoch': '0.8845'}\n"}
,{"stream_name":"stdout","time":3559.145970205,"data":"{'loss': '0.113', 'grad_norm': '1.189', 'learning_rate': '8.457e-06', 'epoch': '0.9583'}\n"}
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| 392/2716 [35:21\u003c3:30:43,  5.44s/it]\r 14%|█▍        | 393/2716 [35:26\u003c3:29:23,  5.41s/it]\r 15%|█▍        | 394/2716 [35:31\u003c3:28:30,  5.39s/it]\r 15%|█▍        | 395/2716 [35:37\u003c3:27:45,  5.37s/it]\r 15%|█▍        | 396/2716 [35:42\u003c3:27:42,  5.37s/it]\r 15%|█▍        | 397/2716 [35:47\u003c3:27:24,  5.37s/it]\r 15%|█▍        | 398/2716 [35:53\u003c3:26:58,  5.36s/it]\r 15%|█▍        | 399/2716 [35:58\u003c3:26:53,  5.36s/it]\r 15%|█▍        | 400/2716 [36:04\u003c3:29:18,  5.42s/it]\r                                                    \r\r 15%|█▍        | 400/2716 [36:04\u003c3:29:18,  5.42s/it]\r 15%|█▍        | 401/2716 [36:09\u003c3:28:46,  5.41s/it]\r 15%|█▍        | 402/2716 [36:14\u003c3:28:36,  5.41s/it]\r 15%|█▍        | 403/2716 [36:20\u003c3:27:36,  5.39s/it]\r 15%|█▍        | 404/2716 [36:25\u003c3:29:42,  5.44s/it]\r 15%|█▍        | 405/2716 [36:31\u003c3:29:08,  5.43s/it]\r 15%|█▍        | 406/2716 [36:36\u003c3:28:39,  5.42s/it]\r 15%|█▍        | 407/2716 [36:41\u003c3:29:02,  5.43s/it]\r 15%|█▌        | 408/2716 [36:47\u003c3:28:07,  5.41s/it]\r 15%|█▌        | 409/2716 [36:52\u003c3:28:58,  5.43s/it]\r 15%|█▌        | 410/2716 [36:58\u003c3:27:40,  5.40s/it]\r 15%|█▌        | 411/2716 [37:03\u003c3:26:46,  5.38s/it]\r 15%|█▌        | 412/2716 [37:08\u003c3:26:09,  5.37s/it]\r 15%|█▌        | 413/2716 [37:14\u003c3:28:57,  5.44s/it]\r 15%|█▌        | 414/2716 [37:19\u003c3:27:47,  5.42s/it]\r 15%|█▌        | 415/2716 [37:25\u003c3:27:12,  5.40s/it]\r 15%|█▌        | 416/2716 [37:30\u003c3:27:07,  5.40s/it]\r 15%|█▌        | 417/2716 [37:36\u003c3:28:51,  5.45s/it]\r 15%|█▌        | 418/2716 [37:41\u003c3:28:33,  5.45s/it]\r 15%|█▌        | 419/2716 [37:46\u003c3:27:27,  5.42s/it]\r 15%|█▌        | 420/2716 [37:52\u003c3:26:56,  5.41s/it]\r 16%|█▌        | 421/2716 [37:57\u003c3:26:57,  5.41s/it]\r 16%|█▌        | 422/2716 [38:03\u003c3:26:18,  5.40s/it]\r 16%|█▌        | 423/2716 [38:08\u003c3:25:51,  5.39s/it]\r 16%|█▌        | 424/2716 [38:13\u003c3:25:57,  5.39s/it]\r 16%|█▌        | 425/2716 [38:19\u003c3:28:28,  5.46s/it]\r 16%|█▌        | 426/2716 [38:24\u003c3:27:00,  5.42s/it]\r 16%|█▌        | 427/2716 [38:30\u003c3:27:00,  5.43s/it]\r 16%|█▌        | 428/2716 [38:35\u003c3:26:46,  5.42s/it]\r 16%|█▌        | 429/2716 [38:41\u003c3:26:07,  5.41s/it]\r 16%|█▌        | 430/2716 [38:46\u003c3:28:00,  5.46s/it]\r 16%|█▌        | 431/2716 [38:51\u003c3:26:35,  5.42s/it]\r 16%|█▌        | 432/2716 [38:57\u003c3:25:51,  5.41s/it]\r 16%|█▌        | 433/2716 [39:02\u003c3:25:21,  5.40s/it]\r 16%|█▌        | 434/2716 [39:08\u003c3:25:10,  5.39s/it]\r 16%|█▌        | 435/2716 [39:13\u003c3:24:52,  5.39s/it]\r 16%|█▌        | 436/2716 [39:18\u003c3:24:24,  5.38s/it]\r 16%|█▌        | 437/2716 [39:24\u003c3:24:28,  5.38s/it]\r 16%|█▌        | 438/2716 [39:29\u003c3:24:47,  5.39s/it]\r 16%|█▌        | 439/2716 [39:35\u003c3:24:24,  5.39s/it]\r 16%|█▌        | 440/2716 [39:40\u003c3:24:21,  5.39s/it]\r 16%|█▌        | 441/2716 [39:45\u003c3:24:06,  5.38s/it]\r 16%|█▋        | 442/2716 [39:51\u003c3:23:29,  5.37s/it]\r 16%|█▋        | 443/2716 [39:56\u003c3:23:00,  5.36s/it]\r 16%|█▋        | 444/2716 [40:01\u003c3:22:28,  5.35s/it]\r 16%|█▋        | 445/2716 [40:07\u003c3:22:13,  5.34s/it]\r 16%|█▋        | 446/2716 [40:12\u003c3:22:14,  5.35s/it]\r 16%|█▋        | 447/2716 [40:17\u003c3:22:25,  5.35s/it]\r 16%|█▋        | 448/2716 [40:23\u003c3:22:16,  5.35s/it]\r 17%|█▋        | 449/2716 [40:28\u003c3:21:56,  5.34s/it]\r 17%|█▋        | 450/2716 [40:33\u003c3:22:20,  5.36s/it]\r                                                    \r\r 17%|█▋        | 450/2716 [40:34\u003c3:22:20,  5.36s/it]\r 17%|█▋        | 451/2716 [40:39\u003c3:22:44,  5.37s/it]\r 17%|█▋        | 452/2716 [40:44\u003c3:22:01,  5.35s/it]\r 17%|█▋        | 453/2716 [40:49\u003c3:22:21,  5.37s/it]\r 17%|█▋        | 454/2716 [40:55\u003c3:22:39,  5.38s/it]\r 17%|█▋        | 455/2716 [41:00\u003c3:25:14,  5.45s/it]\r 17%|█▋     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     | 473/2716 [42:38\u003c3:23:28,  5.44s/it]\r 17%|█▋        | 474/2716 [42:43\u003c3:22:39,  5.42s/it]\r 17%|█▋        | 475/2716 [42:49\u003c3:21:49,  5.40s/it]\r 18%|█▊        | 476/2716 [42:54\u003c3:21:19,  5.39s/it]\r 18%|█▊        | 477/2716 [42:59\u003c3:22:55,  5.44s/it]\r 18%|█▊        | 478/2716 [43:05\u003c3:22:18,  5.42s/it]\r 18%|█▊        | 479/2716 [43:10\u003c3:21:35,  5.41s/it]\r 18%|█▊        | 480/2716 [43:16\u003c3:20:46,  5.39s/it]\r 18%|█▊        | 481/2716 [43:21\u003c3:22:57,  5.45s/it]\r 18%|█▊        | 482/2716 [43:27\u003c3:21:32,  5.41s/it]\r 18%|█▊        | 483/2716 [43:32\u003c3:21:10,  5.41s/it]\r 18%|█▊        | 484/2716 [43:37\u003c3:20:37,  5.39s/it]\r 18%|█▊        | 485/2716 [43:43\u003c3:22:22,  5.44s/it]\r 18%|█▊        | 486/2716 [43:48\u003c3:21:26,  5.42s/it]\r 18%|█▊        | 487/2716 [43:54\u003c3:20:48,  5.41s/it]\r 18%|█▊        | 488/2716 [43:59\u003c3:20:08,  5.39s/it]\r 18%|█▊        | 489/2716 [44:04\u003c3:19:51,  5.38s/it]\r 18%|█▊        | 490/2716 [44:10\u003c3:19:30,  5.38s/it]\r 18%|█▊        | 491/2716 [44:15\u003c3:18:50,  5.36s/it]\r 18%|█▊        | 492/2716 [44:20\u003c3:19:07,  5.37s/it]\r 18%|█▊        | 493/2716 [44:26\u003c3:21:47,  5.45s/it]\r 18%|█▊        | 494/2716 [44:31\u003c3:21:22,  5.44s/it]\r 18%|█▊        | 495/2716 [44:37\u003c3:20:13,  5.41s/it]\r 18%|█▊        | 496/2716 [44:42\u003c3:19:50,  5.40s/it]\r 18%|█▊        | 497/2716 [44:48\u003c3:19:38,  5.40s/it]\r 18%|█▊        | 498/2716 [44:53\u003c3:21:09,  5.44s/it]\r 18%|█▊        | 499/2716 [44:58\u003c3:20:25,  5.42s/it]\r 18%|█▊        | 500/2716 [45:04\u003c3:19:23,  5.40s/it]\r                                                    \r\r 18%|█▊        | 500/2716 [45:04\u003c3:19:23,  5.40s/it]\r 18%|█▊        | 501/2716 [45:09\u003c3:18:59,  5.39s/it]\r 18%|█▊        | 502/2716 [45:14\u003c3:18:23,  5.38s/it]\r 19%|█▊        | 503/2716 [45:20\u003c3:18:37,  5.39s/it]\r 19%|█▊        | 504/2716 [45:25\u003c3:18:25,  5.38s/it]\r 19%|█▊  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[51:22\u003c3:13:28,  5.41s/it]\r 21%|██        | 571/2716 [51:28\u003c3:13:08,  5.40s/it]\r 21%|██        | 572/2716 [51:33\u003c3:12:54,  5.40s/it]\r 21%|██        | 573/2716 [51:38\u003c3:13:05,  5.41s/it]\r 21%|██        | 574/2716 [51:44\u003c3:12:52,  5.40s/it]\r 21%|██        | 575/2716 [51:49\u003c3:12:31,  5.40s/it]\r 21%|██        | 576/2716 [51:55\u003c3:12:39,  5.40s/it]\r 21%|██        | 577/2716 [52:00\u003c3:12:20,  5.40s/it]\r 21%|██▏       | 578/2716 [52:05\u003c3:11:31,  5.37s/it]\r 21%|██▏       | 579/2716 [52:11\u003c3:11:31,  5.38s/it]\r 21%|██▏       | 580/2716 [52:16\u003c3:10:59,  5.37s/it]\r 21%|██▏       | 581/2716 [52:21\u003c3:11:15,  5.38s/it]\r 21%|██▏       | 582/2716 [52:27\u003c3:11:38,  5.39s/it]\r 21%|██▏       | 583/2716 [52:32\u003c3:12:34,  5.42s/it]\r 22%|██▏       | 584/2716 [52:38\u003c3:12:51,  5.43s/it]\r 22%|██▏       | 585/2716 [52:43\u003c3:11:56,  5.40s/it]\r 22%|██▏       | 586/2716 [52:49\u003c3:11:16,  5.39s/it]\r 22%|██▏       | 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[1:20:20\u003c2:45:13,  5.41s/it]\r 33%|███▎      | 884/2716 [1:20:26\u003c2:45:07,  5.41s/it]\r 33%|███▎      | 885/2716 [1:20:31\u003c2:46:46,  5.47s/it]\r 33%|███▎      | 886/2716 [1:20:37\u003c2:45:52,  5.44s/it]\r 33%|███▎      | 887/2716 [1:20:42\u003c2:45:07,  5.42s/it]\r 33%|███▎      | 888/2716 [1:20:47\u003c2:44:18,  5.39s/it]\r 33%|███▎      | 889/2716 [1:20:53\u003c2:43:52,  5.38s/it]\r 33%|███▎      | 890/2716 [1:20:58\u003c2:45:17,  5.43s/it]\r 33%|███▎      | 891/2716 [1:21:04\u003c2:44:32,  5.41s/it]\r 33%|███▎      | 892/2716 [1:21:09\u003c2:43:58,  5.39s/it]\r 33%|███▎      | 893/2716 [1:21:14\u003c2:43:17,  5.37s/it]\r 33%|███▎      | 894/2716 [1:21:20\u003c2:44:45,  5.43s/it]\r 33%|███▎      | 895/2716 [1:21:25\u003c2:44:32,  5.42s/it]\r 33%|███▎      | 896/2716 [1:21:31\u003c2:44:00,  5.41s/it]\r 33%|███▎      | 897/2716 [1:21:36\u003c2:43:21,  5.39s/it]\r 33%|███▎      | 898/2716 [1:21:42\u003c2:45:30,  5.46s/it]\r 33%|███▎      | 899/2716 [1:21:47\u003c2:44:22,  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[1:28:49\u003c2:36:44,  5.41s/it]\r 36%|███▌      | 978/2716 [1:28:54\u003c2:36:09,  5.39s/it]\r 36%|███▌      | 979/2716 [1:29:00\u003c2:35:48,  5.38s/it]\r 36%|███▌      | 980/2716 [1:29:05\u003c2:35:25,  5.37s/it]\r 36%|███▌      | 981/2716 [1:29:10\u003c2:35:35,  5.38s/it]\r 36%|███▌      | 982/2716 [1:29:16\u003c2:35:14,  5.37s/it]\r 36%|███▌      | 983/2716 [1:29:21\u003c2:37:00,  5.44s/it]\r 36%|███▌      | 984/2716 [1:29:27\u003c2:36:38,  5.43s/it]\r 36%|███▋      | 985/2716 [1:29:32\u003c2:35:52,  5.40s/it]\r 36%|███▋      | 986/2716 [1:29:37\u003c2:35:59,  5.41s/it]\r 36%|███▋      | 987/2716 [1:29:43\u003c2:35:27,  5.39s/it]\r 36%|███▋      | 988/2716 [1:29:48\u003c2:34:57,  5.38s/it]\r 36%|███▋      | 989/2716 [1:29:54\u003c2:34:42,  5.37s/it]\r 36%|███▋      | 990/2716 [1:29:59\u003c2:34:14,  5.36s/it]\r 36%|███▋      | 991/2716 [1:30:04\u003c2:33:45,  5.35s/it]\r 37%|███▋      | 992/2716 [1:30:10\u003c2:33:49,  5.35s/it]\r 37%|███▋      | 993/2716 [1:30:15\u003c2:33:31,  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[2:56:57\u003c1:09:23,  5.39s/it]\r 72%|███████▏  | 1945/2716 [2:57:02\u003c1:09:20,  5.40s/it]\r 72%|███████▏  | 1946/2716 [2:57:08\u003c1:09:20,  5.40s/it]\r 72%|███████▏  | 1947/2716 [2:57:13\u003c1:09:15,  5.40s/it]\r 72%|███████▏  | 1948/2716 [2:57:19\u003c1:09:12,  5.41s/it]\r 72%|███████▏  | 1949/2716 [2:57:24\u003c1:09:14,  5.42s/it]\r 72%|███████▏  | 1950/2716 [2:57:30\u003c1:09:23,  5.44s/it]\r                                                       \r\r 72%|███████▏  | 1950/2716 [2:57:30\u003c1:09:23,  5.44s/it]\r 72%|███████▏  | 1951/2716 [2:57:35\u003c1:09:06,  5.42s/it]\r 72%|███████▏  | 1952/2716 [2:57:40\u003c1:09:09,  5.43s/it]\r 72%|███████▏  | 1953/2716 [2:57:46\u003c1:09:04,  5.43s/it]\r 72%|███████▏  | 1954/2716 [2:57:51\u003c1:09:33,  5.48s/it]\r 72%|███████▏  | 1955/2716 [2:57:57\u003c1:09:08,  5.45s/it]\r 72%|███████▏  | 1956/2716 [2:58:02\u003c1:08:37,  5.42s/it]\r 72%|███████▏  | 1957/2716 [2:58:08\u003c1:08:25,  5.41s/it]\r 72%|███████▏  | 1958/2716 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,{"stream_name":"stderr","time":14938.200037268,"data":"There were missing keys in the checkpoint model loaded: ['roberta.embeddings.LayerNorm.weight', 'roberta.embeddings.LayerNorm.bias', 'roberta.encoder.layer.0.attention.output.LayerNorm.weight', 'roberta.encoder.layer.0.attention.output.LayerNorm.bias', 'roberta.encoder.layer.0.output.LayerNorm.weight', 'roberta.encoder.layer.0.output.LayerNorm.bias', 'roberta.encoder.layer.1.attention.output.LayerNorm.weight', 'roberta.encoder.layer.1.attention.output.LayerNorm.bias', 'roberta.encoder.layer.1.output.LayerNorm.weight', 'roberta.encoder.layer.1.output.LayerNorm.bias', 'roberta.encoder.layer.2.attention.output.LayerNorm.weight', 'roberta.encoder.layer.2.attention.output.LayerNorm.bias', 'roberta.encoder.layer.2.output.LayerNorm.weight', 'roberta.encoder.layer.2.output.LayerNorm.bias', 'roberta.encoder.layer.3.attention.output.LayerNorm.weight', 'roberta.encoder.layer.3.attention.output.LayerNorm.bias', 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,{"stream_name":"stdout","time":14938.24680916,"data":"{'train_runtime': '1.489e+04', 'train_samples_per_second': '11.66', 'train_steps_per_second': '0.182', 'train_loss': '0.1207', 'epoch': '4'}\n"}
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,{"stream_name":"stdout","time":15747.982677447,"data":"Selected thresholds: coordinate; validation macro_f1=0.56586; test micro_f1=0.57665; test macro_f1=0.53302; test samples_f1=0.58594\n"}
,{"stream_name":"stderr","time":15753.119436753,"data":"/usr/local/lib/python3.12/dist-packages/mistune.py:435: SyntaxWarning: invalid escape sequence '\\|'\n"}
,{"stream_name":"stderr","time":15753.119471333,"data":"  cells[i][c] = re.sub('\\\\\\\\\\|', '|', cell)\n"}
,{"stream_name":"stderr","time":15753.368464769,"data":"/usr/local/lib/python3.12/dist-packages/nbconvert/filters/filter_links.py:36: SyntaxWarning: invalid escape sequence '\\_'\n"}
,{"stream_name":"stderr","time":15753.368492194,"data":"  text = re.sub(r'_', '\\_', text) # Escape underscores in display text\n"}
,{"stream_name":"stderr","time":15753.98632152,"data":"[NbConvertApp] Converting notebook __script__.ipynb to html\n"}
,{"stream_name":"stderr","time":15755.271876871,"data":"[NbConvertApp] Writing 428743 bytes to __results__.html\n"}
]