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log_test.log
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wandb: WARNING W&B installed but not logged in. Run `wandb login` or set the WANDB_API_KEY env variable.
2021-10-13 02:01:28 - INFO - from_pretrained - 125 : loading vocabulary file /data/nfs14/nfs/aisearch/asr/xhsun/CommonModel/chinese-roberta-wwm/vocab.txt
2021-10-13 02:01:30 - INFO - getExamples - 44 : Heads like : text_a text_b label
2021-10-13 02:01:30 - INFO - getExamples - 57 : *****************************Logging some dev examples*****************************
2021-10-13 02:01:30 - INFO - getExamples - 58 : Total dev nums is : 8802
2021-10-13 02:01:30 - INFO - getExamples - 61 : 求初二上册全部英语课堂笔记，急！ 求几套初二上册的数学试卷，谢谢！ 0
2021-10-13 02:01:30 - INFO - getExamples - 61 : 烂桃花是什么意思啊？ 犯桃花是什么意思啊？ 0
2021-10-13 02:01:30 - INFO - getExamples - 61 : 绿菊花什么意思 菊花嘴什么意思 0
2021-10-13 02:01:30 - INFO - getExamples - 61 : 古诗咏柳中的咏字是什么意思 古诗咏柳是什么意思 0
2021-10-13 02:01:30 - INFO - getExamples - 61 : 酷狗音乐好还是百度音乐好 百度音乐好还是酷狗音乐好 1
2021-10-13 02:01:30 - INFO - __call__ - 72 : EmbeddingSimilarityEvaluator: Evaluating the model on dataset:
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2021-10-13 02:01:41 - INFO - __call__ - 103 : Cosine-Similarity : Pearson: 0.5057 Spearman: 0.5759
2021-10-13 02:01:41 - INFO - __call__ - 105 : Manhattan-Distance: Pearson: 0.5592 Spearman: 0.5813
2021-10-13 02:01:41 - INFO - __call__ - 107 : Euclidean-Distance: Pearson: 0.5622 Spearman: 0.5854
2021-10-13 02:01:41 - INFO - __call__ - 109 : Dot-Product-Similarity: Pearson: 0.2944 Spearman: 0.2848
2021-10-13 02:01:41 - INFO - train - 56 : 一个epoch 下，每隔18个step会输出一次loss，每隔47个step会评估一次模型
0it [00:00, ?it/s]2021-10-13 02:01:42 - INFO - __call__ - 72 : EmbeddingSimilarityEvaluator: Evaluating the model on dataset in epoch 0 after 1 steps:
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2021-10-13 02:01:49 - INFO - __call__ - 103 : Cosine-Similarity : Pearson: 0.5057 Spearman: 0.5759
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2021-10-13 02:01:49 - INFO - __call__ - 107 : Euclidean-Distance: Pearson: 0.5622 Spearman: 0.5854
2021-10-13 02:01:49 - INFO - __call__ - 109 : Dot-Product-Similarity: Pearson: 0.2944 Spearman: 0.2848
2021-10-13 02:01:49 - INFO - save - 371 : Save model to /data/nfs14/nfs/aisearch/asr/xhsun/bwbd_recall/unSimCSE_lcqmc
2021-10-13 02:01:50 - INFO - train - 98 : In epoch 0, training_step 0, the eval score is 0.5758500529612859, previous eval score is -9999999, model has been saved in /data/nfs14/nfs/aisearch/asr/xhsun/bwbd_recall/unSimCSE_lcqmc
1it [00:08, 8.56s/it]2it [00:08, 6.04s/it]3it [00:08, 4.28s/it]4it [00:09, 3.08s/it]5it [00:09, 2.22s/it]6it [00:09, 1.60s/it]7it [00:09, 1.16s/it]8it [00:09, 1.17it/s]9it [00:09, 1.56it/s]10it [00:10, 2.02it/s]11it [00:10, 2.52it/s]12it [00:10, 3.04it/s]13it [00:10, 3.65it/s]14it [00:10, 4.14it/s]15it [00:10, 4.64it/s]16it [00:11, 5.26it/s]17it [00:11, 5.39it/s]18it [00:11, 5.41it/s]2021-10-13 02:01:53 - INFO - train - 75 : Epoch : 0, train_step : 18/470, loss_value : 0.18831074982881546
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2021-10-13 02:02:04 - INFO - __call__ - 103 : Cosine-Similarity : Pearson: 0.5300 Spearman: 0.5993
2021-10-13 02:02:04 - INFO - __call__ - 105 : Manhattan-Distance: Pearson: 0.6035 Spearman: 0.6305
2021-10-13 02:02:04 - INFO - __call__ - 107 : Euclidean-Distance: Pearson: 0.6016 Spearman: 0.6284
2021-10-13 02:02:04 - INFO - __call__ - 109 : Dot-Product-Similarity: Pearson: 0.3615 Spearman: 0.3438
2021-10-13 02:02:04 - INFO - save - 371 : Save model to /data/nfs14/nfs/aisearch/asr/xhsun/bwbd_recall/unSimCSE_lcqmc
2021-10-13 02:02:05 - INFO - train - 98 : In epoch 0, training_step 47, the eval score is 0.5993234329951954, previous eval score is 0.5758500529612859, model has been saved in /data/nfs14/nfs/aisearch/asr/xhsun/bwbd_recall/unSimCSE_lcqmc
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0it [00:00, ?it/s]2021-10-13 02:02:12 - INFO - __call__ - 72 : EmbeddingSimilarityEvaluator: Evaluating the model on dataset in epoch 1 after 1 steps:
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2021-10-13 02:02:21 - INFO - __call__ - 103 : Cosine-Similarity : Pearson: 0.5265 Spearman: 0.5793
2021-10-13 02:02:21 - INFO - __call__ - 105 : Manhattan-Distance: Pearson: 0.5970 Spearman: 0.6200
2021-10-13 02:02:21 - INFO - __call__ - 107 : Euclidean-Distance: Pearson: 0.5954 Spearman: 0.6183
2021-10-13 02:02:21 - INFO - __call__ - 109 : Dot-Product-Similarity: Pearson: 0.3362 Spearman: 0.3162
2021-10-13 02:02:21 - INFO - train - 102 : No improvement over previous best eval score (0.579297 vs 0.599323), patience = 19
1it [00:08, 8.58s/it]2it [00:08, 6.07s/it]3it [00:08, 4.29s/it]4it [00:09, 3.05s/it]5it [00:09, 2.19s/it]6it [00:09, 1.58s/it]7it [00:09, 1.16s/it]8it [00:09, 1.16it/s]9it [00:09, 1.52it/s]10it [00:10, 2.01it/s]11it [00:10, 2.55it/s]12it [00:10, 3.13it/s]13it [00:10, 3.74it/s]14it [00:10, 4.29it/s]15it [00:10, 4.73it/s]16it [00:11, 5.05it/s]17it [00:11, 5.55it/s]18it [00:11, 5.97it/s]2021-10-13 02:02:23 - INFO - train - 75 : Epoch : 1, train_step : 36/470, loss_value : 0.001809923684858303
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2021-10-13 02:02:35 - INFO - __call__ - 103 : Cosine-Similarity : Pearson: 0.5165 Spearman: 0.5599
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2021-10-13 02:02:35 - INFO - __call__ - 109 : Dot-Product-Similarity: Pearson: 0.3254 Spearman: 0.3064
2021-10-13 02:02:35 - INFO - train - 102 : No improvement over previous best eval score (0.559934 vs 0.599323), patience = 18
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0it [00:00, ?it/s]2021-10-13 02:02:43 - INFO - __call__ - 72 : EmbeddingSimilarityEvaluator: Evaluating the model on dataset in epoch 2 after 1 steps:
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2021-10-13 02:02:52 - INFO - __call__ - 103 : Cosine-Similarity : Pearson: 0.5164 Spearman: 0.5563
2021-10-13 02:02:52 - INFO - __call__ - 105 : Manhattan-Distance: Pearson: 0.5811 Spearman: 0.6000
2021-10-13 02:02:52 - INFO - __call__ - 107 : Euclidean-Distance: Pearson: 0.5791 Spearman: 0.5978
2021-10-13 02:02:52 - INFO - __call__ - 109 : Dot-Product-Similarity: Pearson: 0.3375 Spearman: 0.3221
2021-10-13 02:02:52 - INFO - train - 102 : No improvement over previous best eval score (0.556288 vs 0.599323), patience = 17
1it [00:09, 9.32s/it]2it [00:09, 6.57s/it]3it [00:09, 4.65s/it]4it [00:09, 3.29s/it]5it [00:09, 2.34s/it]6it [00:10, 1.69s/it]7it [00:10, 1.23s/it]8it [00:10, 1.11it/s]9it [00:10, 1.46it/s]10it [00:10, 1.93it/s]11it [00:10, 2.42it/s]12it [00:11, 2.95it/s]13it [00:11, 3.54it/s]14it [00:11, 4.04it/s]15it [00:11, 4.64it/s]16it [00:11, 5.12it/s]17it [00:11, 5.46it/s]18it [00:11, 5.91it/s]2021-10-13 02:02:54 - INFO - train - 75 : Epoch : 2, train_step : 54/470, loss_value : 0.0014475788064171663
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2021-10-13 02:03:06 - INFO - __call__ - 103 : Cosine-Similarity : Pearson: 0.5111 Spearman: 0.5458
2021-10-13 02:03:06 - INFO - __call__ - 105 : Manhattan-Distance: Pearson: 0.5741 Spearman: 0.5911
2021-10-13 02:03:06 - INFO - __call__ - 107 : Euclidean-Distance: Pearson: 0.5721 Spearman: 0.5888
2021-10-13 02:03:06 - INFO - __call__ - 109 : Dot-Product-Similarity: Pearson: 0.3317 Spearman: 0.3156
2021-10-13 02:03:06 - INFO - train - 102 : No improvement over previous best eval score (0.545841 vs 0.599323), patience = 16
48it [00:23, 2.28s/it]49it [00:23, 1.64s/it]50it [00:23, 1.19s/it]51it [00:23, 1.14it/s]52it [00:24, 1.51it/s]53it [00:24, 1.95it/s]54it [00:24, 2.50it/s]2021-10-13 02:03:07 - INFO - train - 75 : Epoch : 2, train_step : 162/470, loss_value : 0.0012185796731500886
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0it [00:00, ?it/s]2021-10-13 02:03:13 - INFO - __call__ - 72 : EmbeddingSimilarityEvaluator: Evaluating the model on dataset in epoch 3 after 1 steps:
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2021-10-13 02:03:18 - INFO - __call__ - 103 : Cosine-Similarity : Pearson: 0.5052 Spearman: 0.5346
2021-10-13 02:03:18 - INFO - __call__ - 105 : Manhattan-Distance: Pearson: 0.5651 Spearman: 0.5799
2021-10-13 02:03:18 - INFO - __call__ - 107 : Euclidean-Distance: Pearson: 0.5633 Spearman: 0.5778
2021-10-13 02:03:18 - INFO - __call__ - 109 : Dot-Product-Similarity: Pearson: 0.3321 Spearman: 0.3167
2021-10-13 02:03:18 - INFO - train - 102 : No improvement over previous best eval score (0.534564 vs 0.599323), patience = 15
1it [00:04, 4.97s/it]2it [00:05, 3.53s/it]3it [00:05, 2.53s/it]4it [00:05, 1.81s/it]5it [00:05, 1.32s/it]6it [00:05, 1.03it/s]7it [00:05, 1.38it/s]8it [00:06, 1.81it/s]9it [00:06, 2.32it/s]10it [00:06, 2.88it/s]11it [00:06, 3.50it/s]12it [00:06, 4.06it/s]13it [00:06, 4.58it/s]14it [00:07, 4.91it/s]15it [00:07, 5.44it/s]16it [00:07, 5.76it/s]17it [00:07, 5.78it/s]18it [00:07, 6.30it/s]2021-10-13 02:03:21 - INFO - train - 75 : Epoch : 3, train_step : 72/470, loss_value : 0.0005203897134278021
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2021-10-13 02:03:30 - INFO - __call__ - 103 : Cosine-Similarity : Pearson: 0.5044 Spearman: 0.5325
2021-10-13 02:03:30 - INFO - __call__ - 105 : Manhattan-Distance: Pearson: 0.5641 Spearman: 0.5787
2021-10-13 02:03:30 - INFO - __call__ - 107 : Euclidean-Distance: Pearson: 0.5624 Spearman: 0.5767
2021-10-13 02:03:30 - INFO - __call__ - 109 : Dot-Product-Similarity: Pearson: 0.3287 Spearman: 0.3134
2021-10-13 02:03:30 - INFO - train - 102 : No improvement over previous best eval score (0.532473 vs 0.599323), patience = 14
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0it [00:00, ?it/s]2021-10-13 02:03:37 - INFO - __call__ - 72 : EmbeddingSimilarityEvaluator: Evaluating the model on dataset in epoch 4 after 1 steps:
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2021-10-13 02:03:42 - INFO - __call__ - 103 : Cosine-Similarity : Pearson: 0.5050 Spearman: 0.5338
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2021-10-13 02:03:42 - INFO - __call__ - 107 : Euclidean-Distance: Pearson: 0.5649 Spearman: 0.5798
2021-10-13 02:03:42 - INFO - __call__ - 109 : Dot-Product-Similarity: Pearson: 0.3229 Spearman: 0.3065
2021-10-13 02:03:42 - INFO - train - 102 : No improvement over previous best eval score (0.533791 vs 0.599323), patience = 13
1it [00:05, 5.52s/it]2it [00:05, 3.92s/it]3it [00:05, 2.79s/it]4it [00:06, 2.00s/it]5it [00:06, 1.45s/it]6it [00:06, 1.06s/it]7it [00:06, 1.27it/s]8it [00:06, 1.66it/s]9it [00:06, 2.14it/s]10it [00:06, 2.69it/s]11it [00:07, 3.38it/s]12it [00:07, 3.95it/s]13it [00:07, 4.57it/s]14it [00:07, 4.94it/s]15it [00:07, 5.31it/s]16it [00:07, 5.79it/s]17it [00:07, 6.27it/s]18it [00:08, 6.37it/s]2021-10-13 02:03:45 - INFO - train - 75 : Epoch : 4, train_step : 90/470, loss_value : 0.0010884119348904481
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2021-10-13 02:03:55 - INFO - __call__ - 103 : Cosine-Similarity : Pearson: 0.5045 Spearman: 0.5325
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2021-10-13 02:03:55 - INFO - __call__ - 107 : Euclidean-Distance: Pearson: 0.5641 Spearman: 0.5787
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2021-10-13 02:03:55 - INFO - train - 102 : No improvement over previous best eval score (0.532455 vs 0.599323), patience = 12
48it [00:17, 1.72s/it]49it [00:18, 1.25s/it]50it [00:18, 1.09it/s]51it [00:18, 1.47it/s]52it [00:18, 1.92it/s]53it [00:18, 2.47it/s]54it [00:18, 3.07it/s]2021-10-13 02:03:56 - INFO - train - 75 : Epoch : 4, train_step : 270/470, loss_value : 0.001684180268461609
55it [00:18, 3.63it/s]56it [00:19, 4.17it/s]57it [00:19, 4.84it/s]58it [00:19, 5.24it/s]59it [00:19, 5.55it/s]60it [00:19, 5.98it/s]61it [00:19, 5.96it/s]62it [00:19, 5.98it/s]63it [00:20, 6.23it/s]64it [00:20, 6.52it/s]65it [00:20, 6.72it/s]66it [00:20, 6.66it/s]67it [00:20, 6.85it/s]68it [00:20, 6.79it/s]69it [00:20, 6.99it/s]70it [00:21, 6.96it/s]71it [00:21, 6.85it/s]72it [00:21, 6.53it/s]2021-10-13 02:03:58 - INFO - train - 75 : Epoch : 4, train_step : 360/470, loss_value : 0.0014376413285693463
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Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex.
238766
['喜欢打篮球的男生喜欢什么样的女生', '我手机丢了，我想换个手机']
12000
['喜欢打篮球的男生喜欢什么样的女生', '我手机丢了，我想换个手机']
<InputExample> label: 1, text pairs : 喜欢打篮球的男生喜欢什么样的女生; 喜欢打篮球的男生喜欢什么样的女生
['开初婚未育证明怎么弄？', '初婚未育情况证明怎么开？'] 1
开初婚未育证明怎么弄？ 初婚未育情况证明怎么开？ 1
谁知道她是网络美女吗？ 爱情这杯酒谁喝都会醉是什么歌 0
人和畜生的区别是什么？ 人与畜生的区别是什么！ 1
男孩喝女孩的尿的故事 怎样才知道是生男孩还是女孩 0
这种图片是用什么软件制作的？ 这种图片制作是用什么软件呢？ 1