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TinyBERT_SST2

Text Classification
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bert
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Vishnou/TinyBERT_SST2

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  1. README.md +36 -36
  2. model.safetensors +1 -1
README.md CHANGED
@@ -20,7 +20,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8520642201834863
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -30,8 +30,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model was trained from scratch on the sst2 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0322
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- - Accuracy: 0.8521
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  ## Model description
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@@ -62,39 +62,39 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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- | 0.0315 | 0.06 | 500 | 1.2684 | 0.8521 |
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- | 0.0422 | 0.12 | 1000 | 1.2643 | 0.8463 |
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- | 0.0471 | 0.18 | 1500 | 1.0266 | 0.8532 |
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- | 0.0453 | 0.24 | 2000 | 1.1632 | 0.8509 |
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- | 0.0452 | 0.3 | 2500 | 1.1053 | 0.8555 |
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- | 0.0507 | 0.36 | 3000 | 1.1215 | 0.8498 |
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- | 0.0321 | 0.42 | 3500 | 1.2582 | 0.8452 |
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- | 0.055 | 0.48 | 4000 | 1.0535 | 0.8532 |
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- | 0.0513 | 0.53 | 4500 | 1.0714 | 0.8555 |
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- | 0.0548 | 0.59 | 5000 | 1.1435 | 0.8372 |
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- | 0.0604 | 0.65 | 5500 | 1.0509 | 0.8452 |
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- | 0.053 | 0.71 | 6000 | 1.2208 | 0.8521 |
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- | 0.056 | 0.77 | 6500 | 1.1878 | 0.8498 |
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- | 0.0778 | 0.83 | 7000 | 1.0363 | 0.8567 |
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- | 0.0654 | 0.89 | 7500 | 0.9501 | 0.8498 |
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- | 0.0672 | 0.95 | 8000 | 0.9058 | 0.8475 |
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- | 0.0478 | 1.01 | 8500 | 1.1233 | 0.8463 |
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- | 0.0423 | 1.07 | 9000 | 1.1330 | 0.8521 |
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- | 0.0349 | 1.13 | 9500 | 1.1244 | 0.8486 |
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- | 0.0407 | 1.19 | 10000 | 1.2089 | 0.8532 |
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- | 0.0382 | 1.25 | 10500 | 1.2246 | 0.8440 |
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- | 0.0367 | 1.31 | 11000 | 1.2416 | 0.8486 |
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- | 0.0357 | 1.37 | 11500 | 1.2956 | 0.8417 |
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- | 0.0505 | 1.43 | 12000 | 1.0633 | 0.8486 |
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- | 0.0405 | 1.48 | 12500 | 1.1378 | 0.8475 |
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- | 0.0548 | 1.54 | 13000 | 1.1683 | 0.8452 |
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- | 0.0359 | 1.6 | 13500 | 1.1579 | 0.8521 |
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- | 0.0561 | 1.66 | 14000 | 1.0980 | 0.8509 |
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- | 0.0522 | 1.72 | 14500 | 1.1016 | 0.8463 |
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- | 0.0798 | 1.78 | 15000 | 0.9904 | 0.8601 |
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- | 0.053 | 1.84 | 15500 | 1.0238 | 0.8544 |
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- | 0.0681 | 1.9 | 16000 | 1.0269 | 0.8544 |
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- | 0.073 | 1.96 | 16500 | 1.0322 | 0.8521 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8428899082568807
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model was trained from scratch on the sst2 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.1843
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+ - Accuracy: 0.8429
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 0.012 | 0.06 | 500 | 1.4602 | 0.8475 |
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+ | 0.0111 | 0.12 | 1000 | 1.4848 | 0.8475 |
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+ | 0.0277 | 0.18 | 1500 | 1.5532 | 0.8452 |
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+ | 0.0291 | 0.24 | 2000 | 1.4006 | 0.8440 |
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+ | 0.0283 | 0.3 | 2500 | 1.4589 | 0.8406 |
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+ | 0.0361 | 0.36 | 3000 | 1.2831 | 0.8429 |
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+ | 0.0261 | 0.42 | 3500 | 1.3951 | 0.8417 |
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+ | 0.04 | 0.48 | 4000 | 1.3990 | 0.8245 |
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+ | 0.0333 | 0.53 | 4500 | 1.1859 | 0.8463 |
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+ | 0.0475 | 0.59 | 5000 | 1.1699 | 0.8486 |
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+ | 0.0304 | 0.65 | 5500 | 1.2672 | 0.8394 |
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+ | 0.0323 | 0.71 | 6000 | 1.3541 | 0.8440 |
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+ | 0.0482 | 0.77 | 6500 | 1.2858 | 0.8417 |
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+ | 0.0393 | 0.83 | 7000 | 1.2595 | 0.8463 |
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+ | 0.0371 | 0.89 | 7500 | 1.2028 | 0.8314 |
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+ | 0.0444 | 0.95 | 8000 | 1.1606 | 0.8440 |
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+ | 0.0407 | 1.01 | 8500 | 1.2363 | 0.8406 |
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+ | 0.0238 | 1.07 | 9000 | 1.2556 | 0.8475 |
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+ | 0.0253 | 1.13 | 9500 | 1.2557 | 0.8475 |
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+ | 0.0234 | 1.19 | 10000 | 1.2927 | 0.8521 |
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+ | 0.0293 | 1.25 | 10500 | 1.3345 | 0.8383 |
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+ | 0.0235 | 1.31 | 11000 | 1.3742 | 0.8349 |
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+ | 0.026 | 1.37 | 11500 | 1.3648 | 0.8337 |
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+ | 0.0359 | 1.43 | 12000 | 1.3063 | 0.8337 |
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+ | 0.0225 | 1.48 | 12500 | 1.3475 | 0.8360 |
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+ | 0.0274 | 1.54 | 13000 | 1.3568 | 0.8337 |
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+ | 0.0304 | 1.6 | 13500 | 1.3533 | 0.8372 |
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+ | 0.0534 | 1.66 | 14000 | 1.2560 | 0.8417 |
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+ | 0.0379 | 1.72 | 14500 | 1.2770 | 0.8417 |
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+ | 0.0678 | 1.78 | 15000 | 1.1950 | 0.8429 |
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+ | 0.0488 | 1.84 | 15500 | 1.1796 | 0.8440 |
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+ | 0.0598 | 1.9 | 16000 | 1.1650 | 0.8452 |
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+ | 0.0565 | 1.96 | 16500 | 1.1843 | 0.8429 |
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  ### Framework versions
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