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nosql-identifier-bert

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Inference Endpoints
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update model card README.md

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@@ -17,7 +17,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3487
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  - Accuracy: 0.925
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  ## Model description
@@ -43,52 +43,112 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 40
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 40 | 0.5340 | 0.725 |
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- | No log | 2.0 | 80 | 0.3454 | 0.825 |
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- | No log | 3.0 | 120 | 0.2534 | 0.925 |
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- | No log | 4.0 | 160 | 0.2940 | 0.925 |
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- | No log | 5.0 | 200 | 0.2071 | 0.925 |
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- | No log | 6.0 | 240 | 0.2847 | 0.9 |
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- | No log | 7.0 | 280 | 0.6074 | 0.8 |
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- | No log | 8.0 | 320 | 0.3713 | 0.9 |
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- | No log | 9.0 | 360 | 0.3344 | 0.9 |
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- | No log | 10.0 | 400 | 0.2685 | 0.95 |
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- | No log | 11.0 | 440 | 0.4511 | 0.9 |
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- | No log | 12.0 | 480 | 0.3239 | 0.925 |
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- | 0.2791 | 13.0 | 520 | 0.2473 | 0.95 |
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- | 0.2791 | 14.0 | 560 | 0.2308 | 0.95 |
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- | 0.2791 | 15.0 | 600 | 0.4361 | 0.925 |
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- | 0.2791 | 16.0 | 640 | 0.3220 | 0.9 |
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- | 0.2791 | 17.0 | 680 | 0.2351 | 0.95 |
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- | 0.2791 | 18.0 | 720 | 0.2369 | 0.925 |
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- | 0.2791 | 19.0 | 760 | 0.2604 | 0.925 |
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- | 0.2791 | 20.0 | 800 | 0.4832 | 0.875 |
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- | 0.2791 | 21.0 | 840 | 0.3722 | 0.925 |
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- | 0.2791 | 22.0 | 880 | 0.3575 | 0.925 |
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- | 0.2791 | 23.0 | 920 | 0.3696 | 0.9 |
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- | 0.2791 | 24.0 | 960 | 0.4021 | 0.9 |
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- | 0.0855 | 25.0 | 1000 | 0.4134 | 0.9 |
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- | 0.0855 | 26.0 | 1040 | 0.3858 | 0.9 |
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- | 0.0855 | 27.0 | 1080 | 0.3609 | 0.925 |
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- | 0.0855 | 28.0 | 1120 | 0.3435 | 0.925 |
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- | 0.0855 | 29.0 | 1160 | 0.2918 | 0.925 |
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- | 0.0855 | 30.0 | 1200 | 0.3282 | 0.925 |
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- | 0.0855 | 31.0 | 1240 | 0.2552 | 0.925 |
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- | 0.0855 | 32.0 | 1280 | 0.3052 | 0.9 |
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- | 0.0855 | 33.0 | 1320 | 0.3770 | 0.9 |
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- | 0.0855 | 34.0 | 1360 | 0.3040 | 0.9 |
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- | 0.0855 | 35.0 | 1400 | 0.3231 | 0.925 |
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- | 0.0855 | 36.0 | 1440 | 0.3503 | 0.925 |
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- | 0.0855 | 37.0 | 1480 | 0.3458 | 0.925 |
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- | 0.0462 | 38.0 | 1520 | 0.3553 | 0.925 |
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- | 0.0462 | 39.0 | 1560 | 0.3489 | 0.925 |
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- | 0.0462 | 40.0 | 1600 | 0.3487 | 0.925 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7566
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  - Accuracy: 0.925
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  ## Model description
 
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 100
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 40 | 0.3987 | 0.875 |
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+ | No log | 2.0 | 80 | 0.2198 | 1.0 |
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+ | No log | 3.0 | 120 | 0.1294 | 1.0 |
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+ | No log | 4.0 | 160 | 0.5029 | 0.775 |
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+ | No log | 5.0 | 200 | 0.1562 | 0.95 |
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+ | No log | 6.0 | 240 | 0.1672 | 0.9 |
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+ | No log | 7.0 | 280 | 0.1928 | 0.9 |
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+ | No log | 8.0 | 320 | 0.1154 | 0.95 |
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+ | No log | 9.0 | 360 | 0.1780 | 0.95 |
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+ | No log | 10.0 | 400 | 0.4642 | 0.9 |
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+ | No log | 11.0 | 440 | 0.2898 | 0.95 |
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+ | No log | 12.0 | 480 | 0.2842 | 0.925 |
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+ | 0.3022 | 13.0 | 520 | 0.3082 | 0.95 |
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+ | 0.3022 | 14.0 | 560 | 0.3127 | 0.95 |
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+ | 0.3022 | 15.0 | 600 | 0.3421 | 0.95 |
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+ | 0.3022 | 16.0 | 640 | 0.1690 | 0.975 |
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+ | 0.3022 | 17.0 | 680 | 0.2002 | 0.95 |
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+ | 0.3022 | 18.0 | 720 | 0.4938 | 0.925 |
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+ | 0.3022 | 19.0 | 760 | 0.2749 | 0.95 |
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+ | 0.3022 | 20.0 | 800 | 0.2013 | 0.975 |
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+ | 0.3022 | 21.0 | 840 | 0.4775 | 0.925 |
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+ | 0.3022 | 22.0 | 880 | 0.2020 | 0.975 |
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+ | 0.3022 | 23.0 | 920 | 0.2081 | 0.975 |
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+ | 0.3022 | 24.0 | 960 | 0.2603 | 0.95 |
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+ | 0.0784 | 25.0 | 1000 | 0.5710 | 0.925 |
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+ | 0.0784 | 26.0 | 1040 | 0.4450 | 0.925 |
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+ | 0.0784 | 27.0 | 1080 | 0.2669 | 0.95 |
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+ | 0.0784 | 28.0 | 1120 | 0.6181 | 0.9 |
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+ | 0.0784 | 29.0 | 1160 | 0.3211 | 0.95 |
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+ | 0.0784 | 30.0 | 1200 | 0.3222 | 0.95 |
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+ | 0.0784 | 31.0 | 1240 | 0.3238 | 0.95 |
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+ | 0.0784 | 32.0 | 1280 | 0.4617 | 0.925 |
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+ | 0.0784 | 33.0 | 1320 | 0.4142 | 0.95 |
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+ | 0.0784 | 34.0 | 1360 | 0.4887 | 0.925 |
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+ | 0.0784 | 35.0 | 1400 | 0.6418 | 0.925 |
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+ | 0.0784 | 36.0 | 1440 | 0.4104 | 0.95 |
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+ | 0.0784 | 37.0 | 1480 | 0.5617 | 0.925 |
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+ | 0.0605 | 38.0 | 1520 | 0.3141 | 0.95 |
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+ | 0.0605 | 39.0 | 1560 | 0.3596 | 0.95 |
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+ | 0.0605 | 40.0 | 1600 | 0.6732 | 0.925 |
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+ | 0.0605 | 41.0 | 1640 | 0.6785 | 0.925 |
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+ | 0.0605 | 42.0 | 1680 | 0.6904 | 0.925 |
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+ | 0.0605 | 43.0 | 1720 | 0.4378 | 0.95 |
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+ | 0.0605 | 44.0 | 1760 | 0.6341 | 0.925 |
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+ | 0.0605 | 45.0 | 1800 | 0.3517 | 0.95 |
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+ | 0.0605 | 46.0 | 1840 | 0.7044 | 0.925 |
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+ | 0.0605 | 47.0 | 1880 | 0.7259 | 0.925 |
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+ | 0.0605 | 48.0 | 1920 | 0.7297 | 0.925 |
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+ | 0.0605 | 49.0 | 1960 | 0.7293 | 0.925 |
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+ | 0.0363 | 50.0 | 2000 | 0.7248 | 0.925 |
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+ | 0.0363 | 51.0 | 2040 | 0.4813 | 0.95 |
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+ | 0.0363 | 52.0 | 2080 | 0.4647 | 0.95 |
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+ | 0.0363 | 53.0 | 2120 | 0.4684 | 0.95 |
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+ | 0.0363 | 54.0 | 2160 | 0.6032 | 0.925 |
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+ | 0.0363 | 55.0 | 2200 | 0.4853 | 0.95 |
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+ | 0.0363 | 56.0 | 2240 | 0.4840 | 0.95 |
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+ | 0.0363 | 57.0 | 2280 | 0.4827 | 0.95 |
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+ | 0.0363 | 58.0 | 2320 | 0.4400 | 0.95 |
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+ | 0.0363 | 59.0 | 2360 | 0.4886 | 0.95 |
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+ | 0.0363 | 60.0 | 2400 | 0.4966 | 0.95 |
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+ | 0.0363 | 61.0 | 2440 | 0.4897 | 0.95 |
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+ | 0.0363 | 62.0 | 2480 | 0.4682 | 0.95 |
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+ | 0.0299 | 63.0 | 2520 | 0.4750 | 0.95 |
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+ | 0.0299 | 64.0 | 2560 | 0.4844 | 0.95 |
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+ | 0.0299 | 65.0 | 2600 | 0.7955 | 0.9 |
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+ | 0.0299 | 66.0 | 2640 | 0.5219 | 0.925 |
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+ | 0.0299 | 67.0 | 2680 | 0.4882 | 0.925 |
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+ | 0.0299 | 68.0 | 2720 | 0.4505 | 0.95 |
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+ | 0.0299 | 69.0 | 2760 | 0.2627 | 0.975 |
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+ | 0.0299 | 70.0 | 2800 | 0.2633 | 0.975 |
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+ | 0.0299 | 71.0 | 2840 | 0.2667 | 0.975 |
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+ | 0.0299 | 72.0 | 2880 | 0.2955 | 0.95 |
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+ | 0.0299 | 73.0 | 2920 | 0.4848 | 0.95 |
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+ | 0.0299 | 74.0 | 2960 | 0.7284 | 0.925 |
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+ | 0.0334 | 75.0 | 3000 | 0.4787 | 0.95 |
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+ | 0.0334 | 76.0 | 3040 | 0.4838 | 0.95 |
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+ | 0.0334 | 77.0 | 3080 | 0.4995 | 0.95 |
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+ | 0.0334 | 78.0 | 3120 | 0.2645 | 0.975 |
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+ | 0.0334 | 79.0 | 3160 | 0.4928 | 0.95 |
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+ | 0.0334 | 80.0 | 3200 | 0.6753 | 0.925 |
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+ | 0.0334 | 81.0 | 3240 | 0.6419 | 0.925 |
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+ | 0.0334 | 82.0 | 3280 | 0.4380 | 0.95 |
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+ | 0.0334 | 83.0 | 3320 | 0.4723 | 0.95 |
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+ | 0.0334 | 84.0 | 3360 | 0.4748 | 0.95 |
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+ | 0.0334 | 85.0 | 3400 | 0.4657 | 0.95 |
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+ | 0.0334 | 86.0 | 3440 | 0.4651 | 0.95 |
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+ | 0.0334 | 87.0 | 3480 | 0.4647 | 0.95 |
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+ | 0.0319 | 88.0 | 3520 | 0.4570 | 0.95 |
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+ | 0.0319 | 89.0 | 3560 | 0.4539 | 0.95 |
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+ | 0.0319 | 90.0 | 3600 | 0.7459 | 0.925 |
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+ | 0.0319 | 91.0 | 3640 | 0.7451 | 0.925 |
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+ | 0.0319 | 92.0 | 3680 | 0.7484 | 0.925 |
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+ | 0.0319 | 93.0 | 3720 | 0.7533 | 0.925 |
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+ | 0.0319 | 94.0 | 3760 | 0.7536 | 0.925 |
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+ | 0.0319 | 95.0 | 3800 | 0.7547 | 0.925 |
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+ | 0.0319 | 96.0 | 3840 | 0.7566 | 0.925 |
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+ | 0.0319 | 97.0 | 3880 | 0.7556 | 0.925 |
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+ | 0.0319 | 98.0 | 3920 | 0.7563 | 0.925 |
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+ | 0.0319 | 99.0 | 3960 | 0.7565 | 0.925 |
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+ | 0.0281 | 100.0 | 4000 | 0.7566 | 0.925 |
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  ### Framework versions