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PassbyGrocer
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bert-ner-weibo

Token Classification
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Safetensors
bert
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End of training

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@@ -20,10 +20,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1528
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- - Precision: 0.6384
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- - Recall: 0.7172
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- - F1: 0.6755
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  - Accuracy: 0.9680
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  ## Model description
@@ -44,28 +44,118 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 16
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- - eval_batch_size: 16
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 10
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.1492 | 1.0 | 85 | 0.1220 | 0.5329 | 0.6864 | 0.6000 | 0.9675 |
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- | 0.0887 | 2.0 | 170 | 0.0961 | 0.6052 | 0.7249 | 0.6596 | 0.9722 |
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- | 0.0649 | 3.0 | 255 | 0.1009 | 0.5974 | 0.7095 | 0.6486 | 0.9700 |
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- | 0.0544 | 4.0 | 340 | 0.1109 | 0.6016 | 0.7686 | 0.6749 | 0.9701 |
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- | 0.0327 | 5.0 | 425 | 0.1226 | 0.6305 | 0.7326 | 0.6778 | 0.9694 |
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- | 0.022 | 6.0 | 510 | 0.1297 | 0.6040 | 0.7018 | 0.6492 | 0.9661 |
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- | 0.0221 | 7.0 | 595 | 0.1411 | 0.6253 | 0.7378 | 0.6769 | 0.9683 |
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- | 0.0175 | 8.0 | 680 | 0.1468 | 0.6435 | 0.7147 | 0.6772 | 0.9684 |
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- | 0.0146 | 9.0 | 765 | 0.1500 | 0.6479 | 0.7095 | 0.6773 | 0.9684 |
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- | 0.0121 | 10.0 | 850 | 0.1528 | 0.6384 | 0.7172 | 0.6755 | 0.9680 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2292
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+ - Precision: 0.6382
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+ - Recall: 0.7121
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+ - F1: 0.6731
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  - Accuracy: 0.9680
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  ## Model description
 
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 100
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.262 | 1.0 | 43 | 0.1853 | 0.2802 | 0.2442 | 0.2610 | 0.9527 |
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+ | 0.1455 | 2.0 | 86 | 0.1038 | 0.6031 | 0.7069 | 0.6509 | 0.9705 |
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+ | 0.0958 | 3.0 | 129 | 0.0981 | 0.6633 | 0.6787 | 0.6709 | 0.9722 |
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+ | 0.0756 | 4.0 | 172 | 0.1011 | 0.6189 | 0.7558 | 0.6806 | 0.9699 |
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+ | 0.0389 | 5.0 | 215 | 0.1058 | 0.6627 | 0.7172 | 0.6889 | 0.9715 |
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+ | 0.0339 | 6.0 | 258 | 0.1236 | 0.6205 | 0.7147 | 0.6643 | 0.9665 |
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+ | 0.0259 | 7.0 | 301 | 0.1170 | 0.6376 | 0.7326 | 0.6818 | 0.9698 |
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+ | 0.0182 | 8.0 | 344 | 0.1389 | 0.6110 | 0.7429 | 0.6705 | 0.9668 |
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+ | 0.0184 | 9.0 | 387 | 0.1368 | 0.6063 | 0.7404 | 0.6667 | 0.9651 |
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+ | 0.0128 | 10.0 | 430 | 0.1403 | 0.6283 | 0.7301 | 0.6754 | 0.9683 |
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+ | 0.0122 | 11.0 | 473 | 0.1407 | 0.6275 | 0.7404 | 0.6792 | 0.9677 |
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+ | 0.0147 | 12.0 | 516 | 0.1505 | 0.5967 | 0.7455 | 0.6629 | 0.9663 |
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+ | 0.01 | 13.0 | 559 | 0.1406 | 0.6167 | 0.7404 | 0.6729 | 0.9675 |
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+ | 0.0079 | 14.0 | 602 | 0.1527 | 0.6473 | 0.7172 | 0.6805 | 0.9692 |
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+ | 0.0112 | 15.0 | 645 | 0.1549 | 0.6545 | 0.7352 | 0.6925 | 0.9681 |
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+ | 0.0061 | 16.0 | 688 | 0.1585 | 0.6432 | 0.7275 | 0.6828 | 0.9691 |
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+ | 0.0086 | 17.0 | 731 | 0.1598 | 0.6507 | 0.7326 | 0.6892 | 0.9683 |
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+ | 0.0077 | 18.0 | 774 | 0.1677 | 0.6611 | 0.7172 | 0.6880 | 0.9685 |
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+ | 0.0053 | 19.0 | 817 | 0.1674 | 0.6351 | 0.7249 | 0.6771 | 0.9687 |
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+ | 0.0049 | 20.0 | 860 | 0.1777 | 0.6675 | 0.7121 | 0.6891 | 0.9687 |
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+ | 0.0088 | 21.0 | 903 | 0.1579 | 0.6578 | 0.7018 | 0.6791 | 0.9676 |
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+ | 0.0085 | 22.0 | 946 | 0.1729 | 0.6618 | 0.6941 | 0.6775 | 0.9675 |
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+ | 0.0062 | 23.0 | 989 | 0.1788 | 0.6395 | 0.7249 | 0.6795 | 0.9685 |
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+ | 0.0052 | 24.0 | 1032 | 0.1782 | 0.6458 | 0.7172 | 0.6797 | 0.9683 |
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+ | 0.0084 | 25.0 | 1075 | 0.1803 | 0.6345 | 0.7275 | 0.6778 | 0.9670 |
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+ | 0.006 | 26.0 | 1118 | 0.1972 | 0.6154 | 0.7198 | 0.6635 | 0.9651 |
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+ | 0.0045 | 27.0 | 1161 | 0.1852 | 0.625 | 0.7198 | 0.6691 | 0.9674 |
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+ | 0.0035 | 28.0 | 1204 | 0.1847 | 0.6412 | 0.7121 | 0.6748 | 0.9680 |
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+ | 0.0045 | 29.0 | 1247 | 0.1823 | 0.6675 | 0.6915 | 0.6793 | 0.9687 |
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+ | 0.0094 | 30.0 | 1290 | 0.1962 | 0.6362 | 0.7147 | 0.6731 | 0.9682 |
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+ | 0.0036 | 31.0 | 1333 | 0.2092 | 0.6319 | 0.7018 | 0.6650 | 0.9667 |
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+ | 0.0045 | 32.0 | 1376 | 0.1872 | 0.6242 | 0.7301 | 0.6730 | 0.9650 |
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+ | 0.0051 | 33.0 | 1419 | 0.2008 | 0.6112 | 0.7275 | 0.6643 | 0.9649 |
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+ | 0.0057 | 34.0 | 1462 | 0.2018 | 0.6088 | 0.7481 | 0.6713 | 0.9662 |
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+ | 0.003 | 35.0 | 1505 | 0.1941 | 0.6539 | 0.7044 | 0.6782 | 0.9680 |
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+ | 0.0074 | 36.0 | 1548 | 0.1978 | 0.6741 | 0.7018 | 0.6877 | 0.9683 |
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+ | 0.0045 | 37.0 | 1591 | 0.1940 | 0.6563 | 0.7069 | 0.6807 | 0.9674 |
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+ | 0.0031 | 38.0 | 1634 | 0.2075 | 0.6220 | 0.7275 | 0.6706 | 0.9674 |
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+ | 0.0058 | 39.0 | 1677 | 0.1979 | 0.6429 | 0.7172 | 0.6780 | 0.9678 |
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+ | 0.0029 | 40.0 | 1720 | 0.2002 | 0.6447 | 0.7044 | 0.6732 | 0.9689 |
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+ | 0.0041 | 41.0 | 1763 | 0.1962 | 0.6222 | 0.7069 | 0.6619 | 0.9678 |
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+ | 0.0028 | 42.0 | 1806 | 0.2035 | 0.6298 | 0.7172 | 0.6707 | 0.9672 |
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+ | 0.0033 | 43.0 | 1849 | 0.2208 | 0.6144 | 0.7249 | 0.6651 | 0.9668 |
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+ | 0.0024 | 44.0 | 1892 | 0.2208 | 0.6330 | 0.7095 | 0.6691 | 0.9668 |
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+ | 0.0043 | 45.0 | 1935 | 0.2250 | 0.5872 | 0.7095 | 0.6426 | 0.9647 |
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+ | 0.0043 | 46.0 | 1978 | 0.2151 | 0.6425 | 0.6838 | 0.6625 | 0.9676 |
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+ | 0.0054 | 47.0 | 2021 | 0.2121 | 0.6692 | 0.6761 | 0.6726 | 0.9690 |
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+ | 0.0048 | 48.0 | 2064 | 0.1978 | 0.6231 | 0.7224 | 0.6690 | 0.9671 |
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+ | 0.0049 | 49.0 | 2107 | 0.1963 | 0.6453 | 0.7249 | 0.6828 | 0.9689 |
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+ | 0.0043 | 50.0 | 2150 | 0.2090 | 0.6683 | 0.7095 | 0.6883 | 0.9691 |
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+ | 0.0032 | 51.0 | 2193 | 0.2017 | 0.6317 | 0.7275 | 0.6762 | 0.9679 |
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+ | 0.0046 | 52.0 | 2236 | 0.2036 | 0.6409 | 0.7249 | 0.6803 | 0.9694 |
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+ | 0.0052 | 53.0 | 2279 | 0.2047 | 0.6210 | 0.7455 | 0.6776 | 0.9676 |
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+ | 0.0027 | 54.0 | 2322 | 0.1953 | 0.6359 | 0.7095 | 0.6707 | 0.9688 |
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+ | 0.0048 | 55.0 | 2365 | 0.1935 | 0.6555 | 0.7044 | 0.6791 | 0.9701 |
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+ | 0.0037 | 56.0 | 2408 | 0.1975 | 0.6212 | 0.7378 | 0.6745 | 0.9688 |
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+ | 0.0064 | 57.0 | 2451 | 0.2016 | 0.6337 | 0.7249 | 0.6763 | 0.9690 |
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+ | 0.0039 | 58.0 | 2494 | 0.2087 | 0.6152 | 0.7275 | 0.6667 | 0.9669 |
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+ | 0.0027 | 59.0 | 2537 | 0.2056 | 0.6388 | 0.7275 | 0.6803 | 0.9679 |
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+ | 0.0028 | 60.0 | 2580 | 0.2067 | 0.6421 | 0.7378 | 0.6866 | 0.9687 |
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+ | 0.0031 | 61.0 | 2623 | 0.1963 | 0.6300 | 0.7352 | 0.6785 | 0.9685 |
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+ | 0.0042 | 62.0 | 2666 | 0.2048 | 0.6207 | 0.7404 | 0.6753 | 0.9670 |
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+ | 0.0034 | 63.0 | 2709 | 0.2000 | 0.6332 | 0.7455 | 0.6848 | 0.9689 |
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+ | 0.004 | 64.0 | 2752 | 0.1914 | 0.6484 | 0.7301 | 0.6868 | 0.9692 |
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+ | 0.0038 | 65.0 | 2795 | 0.1983 | 0.6185 | 0.7378 | 0.6729 | 0.9685 |
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+ | 0.0039 | 66.0 | 2838 | 0.2068 | 0.6214 | 0.7301 | 0.6714 | 0.9683 |
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+ | 0.003 | 67.0 | 2881 | 0.2129 | 0.6236 | 0.7198 | 0.6683 | 0.9685 |
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+ | 0.0036 | 68.0 | 2924 | 0.2118 | 0.6131 | 0.7455 | 0.6729 | 0.9676 |
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+ | 0.0033 | 69.0 | 2967 | 0.1997 | 0.6513 | 0.7249 | 0.6861 | 0.9691 |
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+ | 0.003 | 70.0 | 3010 | 0.2066 | 0.6217 | 0.7224 | 0.6683 | 0.9686 |
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+ | 0.0042 | 71.0 | 3053 | 0.2064 | 0.6201 | 0.7301 | 0.6706 | 0.9682 |
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+ | 0.0029 | 72.0 | 3096 | 0.2113 | 0.6196 | 0.7326 | 0.6714 | 0.9676 |
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+ | 0.0021 | 73.0 | 3139 | 0.2051 | 0.6341 | 0.7172 | 0.6731 | 0.9685 |
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+ | 0.0035 | 74.0 | 3182 | 0.2059 | 0.6353 | 0.7121 | 0.6715 | 0.9681 |
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+ | 0.0042 | 75.0 | 3225 | 0.2085 | 0.6304 | 0.7147 | 0.6699 | 0.9678 |
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+ | 0.0038 | 76.0 | 3268 | 0.2137 | 0.6284 | 0.7172 | 0.6699 | 0.9676 |
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+ | 0.0023 | 77.0 | 3311 | 0.2134 | 0.6231 | 0.7224 | 0.6690 | 0.9682 |
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+ | 0.003 | 78.0 | 3354 | 0.2149 | 0.6467 | 0.7198 | 0.6813 | 0.9689 |
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+ | 0.0034 | 79.0 | 3397 | 0.2121 | 0.6406 | 0.7147 | 0.6756 | 0.9685 |
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+ | 0.0034 | 80.0 | 3440 | 0.2146 | 0.6407 | 0.7198 | 0.6780 | 0.9685 |
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+ | 0.0033 | 81.0 | 3483 | 0.2162 | 0.6430 | 0.7224 | 0.6804 | 0.9685 |
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+ | 0.0031 | 82.0 | 3526 | 0.2233 | 0.6264 | 0.7198 | 0.6699 | 0.9678 |
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+ | 0.0043 | 83.0 | 3569 | 0.2279 | 0.6355 | 0.7172 | 0.6739 | 0.9678 |
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+ | 0.0032 | 84.0 | 3612 | 0.2247 | 0.6357 | 0.7224 | 0.6763 | 0.9682 |
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+ | 0.0046 | 85.0 | 3655 | 0.2240 | 0.6495 | 0.7147 | 0.6805 | 0.9683 |
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+ | 0.0047 | 86.0 | 3698 | 0.2262 | 0.6284 | 0.7172 | 0.6699 | 0.9684 |
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+ | 0.0036 | 87.0 | 3741 | 0.2214 | 0.6435 | 0.7147 | 0.6772 | 0.9682 |
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+ | 0.0034 | 88.0 | 3784 | 0.2199 | 0.6353 | 0.7121 | 0.6715 | 0.9685 |
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+ | 0.0034 | 89.0 | 3827 | 0.2231 | 0.6414 | 0.7172 | 0.6772 | 0.9682 |
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+ | 0.0024 | 90.0 | 3870 | 0.2239 | 0.6427 | 0.7121 | 0.6756 | 0.9683 |
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+ | 0.0019 | 91.0 | 3913 | 0.2243 | 0.6397 | 0.7121 | 0.6740 | 0.9681 |
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+ | 0.0032 | 92.0 | 3956 | 0.2264 | 0.6333 | 0.7147 | 0.6715 | 0.9680 |
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+ | 0.0021 | 93.0 | 3999 | 0.2276 | 0.6304 | 0.7147 | 0.6699 | 0.9680 |
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+ | 0.0029 | 94.0 | 4042 | 0.2277 | 0.6339 | 0.7121 | 0.6707 | 0.9680 |
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+ | 0.0039 | 95.0 | 4085 | 0.2281 | 0.6353 | 0.7121 | 0.6715 | 0.9680 |
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+ | 0.0021 | 96.0 | 4128 | 0.2289 | 0.6368 | 0.7121 | 0.6723 | 0.9681 |
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+ | 0.0027 | 97.0 | 4171 | 0.2292 | 0.6382 | 0.7121 | 0.6731 | 0.9680 |
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+ | 0.0028 | 98.0 | 4214 | 0.2289 | 0.6382 | 0.7121 | 0.6731 | 0.9682 |
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+ | 0.0027 | 99.0 | 4257 | 0.2291 | 0.6382 | 0.7121 | 0.6731 | 0.9682 |
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+ | 0.002 | 100.0 | 4300 | 0.2292 | 0.6382 | 0.7121 | 0.6731 | 0.9680 |
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  ### Framework versions