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ankush-003
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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: 0.2163
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  - Accuracy: 0.925
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  ## Model description
@@ -43,32 +43,52 @@ 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: 20
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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 | 0.5778 | 0.75 |
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- | No log | 2.0 | 80 | 0.2385 | 0.9 |
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- | No log | 3.0 | 120 | 0.2347 | 0.925 |
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- | No log | 4.0 | 160 | 0.2441 | 0.95 |
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- | No log | 5.0 | 200 | 0.3622 | 0.875 |
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- | No log | 6.0 | 240 | 0.3678 | 0.9 |
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- | No log | 7.0 | 280 | 0.4873 | 0.8 |
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- | No log | 8.0 | 320 | 0.2847 | 0.95 |
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- | No log | 9.0 | 360 | 0.3917 | 0.9 |
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- | No log | 10.0 | 400 | 0.4760 | 0.9 |
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- | No log | 11.0 | 440 | 0.5594 | 0.875 |
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- | No log | 12.0 | 480 | 0.4868 | 0.9 |
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- | 0.3077 | 13.0 | 520 | 0.3206 | 0.9 |
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- | 0.3077 | 14.0 | 560 | 0.2144 | 0.9 |
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- | 0.3077 | 15.0 | 600 | 0.2380 | 0.925 |
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- | 0.3077 | 16.0 | 640 | 0.2451 | 0.925 |
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- | 0.3077 | 17.0 | 680 | 0.2343 | 0.925 |
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- | 0.3077 | 18.0 | 720 | 0.2093 | 0.9 |
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- | 0.3077 | 19.0 | 760 | 0.2168 | 0.925 |
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- | 0.3077 | 20.0 | 800 | 0.2163 | 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: 0.3487
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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: 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