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PassbyGrocer
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hreb-weibo

Token Classification
Transformers
Safetensors
bert
Generated from Trainer
Inference Endpoints
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End of training

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  1. README.md +108 -18
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ 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: 0.1596
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- - Precision: 0.6343
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- - Recall: 0.7201
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- - F1: 0.6745
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- - Accuracy: 0.9670
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  ## Model description
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@@ -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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- | 0.1831 | 1.0 | 85 | 0.1327 | 0.4580 | 0.5660 | 0.5063 | 0.9644 |
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- | 0.101 | 2.0 | 170 | 0.1056 | 0.5725 | 0.7201 | 0.6379 | 0.9700 |
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- | 0.0612 | 3.0 | 255 | 0.1136 | 0.5990 | 0.7233 | 0.6553 | 0.9688 |
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- | 0.0503 | 4.0 | 340 | 0.1315 | 0.5879 | 0.7358 | 0.6536 | 0.9653 |
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- | 0.033 | 5.0 | 425 | 0.1323 | 0.6133 | 0.7233 | 0.6638 | 0.9672 |
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- | 0.019 | 6.0 | 510 | 0.1398 | 0.6198 | 0.7075 | 0.6608 | 0.9655 |
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- | 0.0198 | 7.0 | 595 | 0.1442 | 0.6209 | 0.7107 | 0.6628 | 0.9681 |
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- | 0.0153 | 8.0 | 680 | 0.1511 | 0.6247 | 0.7170 | 0.6676 | 0.9674 |
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- | 0.0133 | 9.0 | 765 | 0.1581 | 0.6212 | 0.7013 | 0.6588 | 0.9656 |
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- | 0.0084 | 10.0 | 850 | 0.1596 | 0.6343 | 0.7201 | 0.6745 | 0.9670 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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21
  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: 0.2691
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+ - Precision: 0.6285
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+ - Recall: 0.7075
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+ - F1: 0.6657
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+ - Accuracy: 0.9667
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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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+ | 0.0218 | 1.0 | 43 | 0.1575 | 0.6190 | 0.7358 | 0.6724 | 0.9645 |
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+ | 0.0159 | 2.0 | 86 | 0.1491 | 0.6105 | 0.7296 | 0.6648 | 0.9653 |
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+ | 0.0157 | 3.0 | 129 | 0.1643 | 0.5995 | 0.7107 | 0.6504 | 0.9647 |
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+ | 0.0147 | 4.0 | 172 | 0.1792 | 0.6103 | 0.7484 | 0.6723 | 0.9637 |
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+ | 0.0058 | 5.0 | 215 | 0.1812 | 0.6332 | 0.7327 | 0.6793 | 0.9685 |
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+ | 0.0103 | 6.0 | 258 | 0.1821 | 0.6188 | 0.7044 | 0.6588 | 0.9645 |
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+ | 0.0116 | 7.0 | 301 | 0.1769 | 0.6207 | 0.7358 | 0.6734 | 0.9664 |
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+ | 0.0112 | 8.0 | 344 | 0.1691 | 0.6334 | 0.7390 | 0.6821 | 0.9678 |
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+ | 0.0067 | 9.0 | 387 | 0.1768 | 0.6166 | 0.7233 | 0.6657 | 0.9669 |
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+ | 0.0078 | 10.0 | 430 | 0.1827 | 0.6171 | 0.6792 | 0.6467 | 0.9678 |
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+ | 0.0073 | 11.0 | 473 | 0.1903 | 0.6450 | 0.6855 | 0.6646 | 0.9685 |
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+ | 0.0173 | 12.0 | 516 | 0.1910 | 0.5964 | 0.7390 | 0.6601 | 0.9630 |
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+ | 0.0045 | 13.0 | 559 | 0.1909 | 0.6146 | 0.7170 | 0.6618 | 0.9663 |
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+ | 0.0067 | 14.0 | 602 | 0.1846 | 0.6127 | 0.7264 | 0.6647 | 0.9669 |
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+ | 0.0063 | 15.0 | 645 | 0.1982 | 0.6359 | 0.7138 | 0.6726 | 0.9677 |
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+ | 0.0051 | 16.0 | 688 | 0.1902 | 0.6260 | 0.7264 | 0.6725 | 0.9662 |
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+ | 0.0058 | 17.0 | 731 | 0.1948 | 0.6292 | 0.7044 | 0.6647 | 0.9682 |
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+ | 0.0063 | 18.0 | 774 | 0.2043 | 0.6350 | 0.6730 | 0.6534 | 0.9678 |
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+ | 0.0054 | 19.0 | 817 | 0.2083 | 0.6340 | 0.6918 | 0.6617 | 0.9677 |
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+ | 0.0042 | 20.0 | 860 | 0.2087 | 0.6339 | 0.7296 | 0.6784 | 0.9674 |
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+ | 0.0051 | 21.0 | 903 | 0.2018 | 0.6494 | 0.6698 | 0.6594 | 0.9676 |
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+ | 0.0062 | 22.0 | 946 | 0.1954 | 0.6510 | 0.6981 | 0.6737 | 0.9676 |
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+ | 0.0048 | 23.0 | 989 | 0.2272 | 0.6192 | 0.7107 | 0.6618 | 0.9662 |
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+ | 0.0037 | 24.0 | 1032 | 0.2109 | 0.6501 | 0.7013 | 0.6747 | 0.9682 |
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+ | 0.0079 | 25.0 | 1075 | 0.2061 | 0.6233 | 0.7390 | 0.6763 | 0.9660 |
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+ | 0.004 | 26.0 | 1118 | 0.2104 | 0.6404 | 0.7170 | 0.6766 | 0.9671 |
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+ | 0.0047 | 27.0 | 1161 | 0.2019 | 0.6326 | 0.7201 | 0.6735 | 0.9678 |
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+ | 0.0036 | 28.0 | 1204 | 0.2157 | 0.6369 | 0.7390 | 0.6841 | 0.9671 |
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+ | 0.0038 | 29.0 | 1247 | 0.2115 | 0.6257 | 0.7201 | 0.6696 | 0.9672 |
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+ | 0.0087 | 30.0 | 1290 | 0.2173 | 0.6278 | 0.7107 | 0.6667 | 0.9673 |
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+ | 0.0034 | 31.0 | 1333 | 0.2217 | 0.6185 | 0.7138 | 0.6628 | 0.9665 |
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+ | 0.0046 | 32.0 | 1376 | 0.2051 | 0.6361 | 0.6981 | 0.6657 | 0.9671 |
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+ | 0.0027 | 33.0 | 1419 | 0.2214 | 0.6410 | 0.7075 | 0.6726 | 0.9676 |
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+ | 0.0049 | 34.0 | 1462 | 0.2183 | 0.6543 | 0.7201 | 0.6856 | 0.9675 |
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+ | 0.0028 | 35.0 | 1505 | 0.2200 | 0.6449 | 0.7138 | 0.6776 | 0.9679 |
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+ | 0.0064 | 36.0 | 1548 | 0.2290 | 0.6395 | 0.6918 | 0.6647 | 0.9673 |
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+ | 0.0044 | 37.0 | 1591 | 0.2252 | 0.6526 | 0.6792 | 0.6656 | 0.9673 |
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+ | 0.0034 | 38.0 | 1634 | 0.2364 | 0.675 | 0.6792 | 0.6771 | 0.9670 |
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+ | 0.0066 | 39.0 | 1677 | 0.2254 | 0.6341 | 0.7138 | 0.6716 | 0.9651 |
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+ | 0.0032 | 40.0 | 1720 | 0.2257 | 0.6316 | 0.7170 | 0.6716 | 0.9674 |
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+ | 0.003 | 41.0 | 1763 | 0.2229 | 0.6461 | 0.7233 | 0.6825 | 0.9678 |
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+ | 0.0018 | 42.0 | 1806 | 0.2315 | 0.6550 | 0.7044 | 0.6788 | 0.9676 |
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+ | 0.0031 | 43.0 | 1849 | 0.2327 | 0.6324 | 0.7358 | 0.6802 | 0.9674 |
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+ | 0.0036 | 44.0 | 1892 | 0.2330 | 0.625 | 0.7075 | 0.6637 | 0.9665 |
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+ | 0.0031 | 45.0 | 1935 | 0.2371 | 0.6449 | 0.7138 | 0.6776 | 0.9667 |
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+ | 0.0039 | 46.0 | 1978 | 0.2379 | 0.6647 | 0.7044 | 0.6840 | 0.9670 |
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+ | 0.004 | 47.0 | 2021 | 0.2398 | 0.6469 | 0.7201 | 0.6815 | 0.9674 |
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+ | 0.0027 | 48.0 | 2064 | 0.2437 | 0.6628 | 0.7107 | 0.6859 | 0.9676 |
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+ | 0.0037 | 49.0 | 2107 | 0.2465 | 0.6638 | 0.7327 | 0.6966 | 0.9681 |
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+ | 0.0047 | 50.0 | 2150 | 0.2452 | 0.6609 | 0.7170 | 0.6878 | 0.9671 |
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+ | 0.0029 | 51.0 | 2193 | 0.2386 | 0.6607 | 0.6981 | 0.6789 | 0.9674 |
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+ | 0.0036 | 52.0 | 2236 | 0.2479 | 0.6402 | 0.7107 | 0.6736 | 0.9676 |
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+ | 0.0047 | 53.0 | 2279 | 0.2440 | 0.6496 | 0.7170 | 0.6816 | 0.9675 |
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+ | 0.0024 | 54.0 | 2322 | 0.2344 | 0.6687 | 0.7044 | 0.6861 | 0.9678 |
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+ | 0.0039 | 55.0 | 2365 | 0.2450 | 0.6247 | 0.7170 | 0.6676 | 0.9669 |
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+ | 0.0026 | 56.0 | 2408 | 0.2404 | 0.6494 | 0.7107 | 0.6787 | 0.9669 |
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+ | 0.005 | 57.0 | 2451 | 0.2472 | 0.6425 | 0.7233 | 0.6805 | 0.9666 |
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+ | 0.0031 | 58.0 | 2494 | 0.2478 | 0.6417 | 0.7264 | 0.6814 | 0.9665 |
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+ | 0.0021 | 59.0 | 2537 | 0.2479 | 0.6356 | 0.7075 | 0.6696 | 0.9665 |
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+ | 0.0021 | 60.0 | 2580 | 0.2457 | 0.6469 | 0.7201 | 0.6815 | 0.9670 |
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+ | 0.0028 | 61.0 | 2623 | 0.2517 | 0.6516 | 0.7233 | 0.6855 | 0.9671 |
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+ | 0.0033 | 62.0 | 2666 | 0.2580 | 0.6512 | 0.7044 | 0.6767 | 0.9668 |
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+ | 0.0023 | 63.0 | 2709 | 0.2546 | 0.6484 | 0.7075 | 0.6767 | 0.9666 |
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+ | 0.0025 | 64.0 | 2752 | 0.2549 | 0.6439 | 0.7107 | 0.6756 | 0.9663 |
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+ | 0.0041 | 65.0 | 2795 | 0.2619 | 0.6311 | 0.7264 | 0.6754 | 0.9664 |
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+ | 0.0036 | 66.0 | 2838 | 0.2583 | 0.6389 | 0.7233 | 0.6785 | 0.9667 |
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+ | 0.0036 | 67.0 | 2881 | 0.2579 | 0.6399 | 0.7264 | 0.6804 | 0.9663 |
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+ | 0.0031 | 68.0 | 2924 | 0.2585 | 0.6425 | 0.7233 | 0.6805 | 0.9662 |
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+ | 0.0031 | 69.0 | 2967 | 0.2529 | 0.6366 | 0.7107 | 0.6716 | 0.9661 |
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+ | 0.0027 | 70.0 | 3010 | 0.2527 | 0.6477 | 0.7170 | 0.6806 | 0.9664 |
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+ | 0.0023 | 71.0 | 3053 | 0.2568 | 0.6524 | 0.7201 | 0.6846 | 0.9667 |
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+ | 0.0025 | 72.0 | 3096 | 0.2587 | 0.6449 | 0.7138 | 0.6776 | 0.9668 |
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+ | 0.0022 | 73.0 | 3139 | 0.2609 | 0.6552 | 0.7170 | 0.6847 | 0.9670 |
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+ | 0.0033 | 74.0 | 3182 | 0.2596 | 0.6542 | 0.7138 | 0.6827 | 0.9669 |
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+ | 0.0038 | 75.0 | 3225 | 0.2608 | 0.6503 | 0.7075 | 0.6777 | 0.9667 |
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+ | 0.0038 | 76.0 | 3268 | 0.2623 | 0.6532 | 0.7107 | 0.6807 | 0.9670 |
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+ | 0.0023 | 77.0 | 3311 | 0.2548 | 0.6459 | 0.7170 | 0.6796 | 0.9666 |
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+ | 0.0029 | 78.0 | 3354 | 0.2588 | 0.6404 | 0.7170 | 0.6766 | 0.9667 |
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+ | 0.0033 | 79.0 | 3397 | 0.2640 | 0.6366 | 0.7327 | 0.6813 | 0.9660 |
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+ | 0.0031 | 80.0 | 3440 | 0.2647 | 0.6419 | 0.7327 | 0.6843 | 0.9654 |
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+ | 0.003 | 81.0 | 3483 | 0.2574 | 0.6476 | 0.7107 | 0.6777 | 0.9670 |
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+ | 0.003 | 82.0 | 3526 | 0.2591 | 0.6412 | 0.7138 | 0.6756 | 0.9669 |
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+ | 0.0046 | 83.0 | 3569 | 0.2605 | 0.6441 | 0.7170 | 0.6786 | 0.9669 |
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+ | 0.0026 | 84.0 | 3612 | 0.2615 | 0.6439 | 0.7107 | 0.6756 | 0.9670 |
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+ | 0.0031 | 85.0 | 3655 | 0.2619 | 0.6277 | 0.7264 | 0.6735 | 0.9664 |
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+ | 0.005 | 86.0 | 3698 | 0.2645 | 0.6417 | 0.7264 | 0.6814 | 0.9667 |
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+ | 0.0038 | 87.0 | 3741 | 0.2646 | 0.6376 | 0.7138 | 0.6736 | 0.9664 |
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+ | 0.0037 | 88.0 | 3784 | 0.2642 | 0.6306 | 0.7138 | 0.6696 | 0.9663 |
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+ | 0.0037 | 89.0 | 3827 | 0.2660 | 0.6343 | 0.7201 | 0.6745 | 0.9665 |
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+ | 0.0027 | 90.0 | 3870 | 0.2670 | 0.6306 | 0.7138 | 0.6696 | 0.9667 |
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+ | 0.002 | 91.0 | 3913 | 0.2675 | 0.6260 | 0.7107 | 0.6657 | 0.9665 |
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+ | 0.0036 | 92.0 | 3956 | 0.2697 | 0.6288 | 0.7138 | 0.6686 | 0.9665 |
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+ | 0.0021 | 93.0 | 3999 | 0.2700 | 0.6260 | 0.7107 | 0.6657 | 0.9665 |
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+ | 0.0029 | 94.0 | 4042 | 0.2693 | 0.6260 | 0.7107 | 0.6657 | 0.9665 |
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+ | 0.0035 | 95.0 | 4085 | 0.2689 | 0.6260 | 0.7107 | 0.6657 | 0.9666 |
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+ | 0.0017 | 96.0 | 4128 | 0.2696 | 0.6260 | 0.7107 | 0.6657 | 0.9667 |
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+ | 0.003 | 97.0 | 4171 | 0.2702 | 0.6260 | 0.7107 | 0.6657 | 0.9665 |
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+ | 0.0031 | 98.0 | 4214 | 0.2699 | 0.6295 | 0.7107 | 0.6677 | 0.9667 |
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+ | 0.0027 | 99.0 | 4257 | 0.2690 | 0.6303 | 0.7075 | 0.6667 | 0.9668 |
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+ | 0.0023 | 100.0 | 4300 | 0.2691 | 0.6285 | 0.7075 | 0.6657 | 0.9667 |
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  ### Framework versions
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