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TinyBERT_SST2

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Vishnou/TinyBERT_SST2

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  1. README.md +38 -37
  2. model.safetensors +1 -1
README.md CHANGED
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  ---
 
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  tags:
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  - generated_from_trainer
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  datasets:
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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
@@ -28,10 +29,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # TinyBERT_SST2
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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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@@ -62,39 +63,39 @@ 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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- | 0.012 | 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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  ---
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+ base_model: huawei-noah/TinyBERT_General_4L_312D
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  tags:
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  - generated_from_trainer
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  datasets:
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.875
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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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  # TinyBERT_SST2
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+ This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) on the sst2 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5560
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+ - Accuracy: 0.875
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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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+ | 0.4661 | 0.06 | 500 | 0.3888 | 0.8337 |
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+ | 0.3684 | 0.12 | 1000 | 0.3557 | 0.8544 |
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+ | 0.3857 | 0.18 | 1500 | 0.3839 | 0.8544 |
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+ | 0.3616 | 0.24 | 2000 | 0.3700 | 0.8670 |
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+ | 0.3559 | 0.3 | 2500 | 0.3586 | 0.8544 |
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+ | 0.3501 | 0.36 | 3000 | 0.3886 | 0.8498 |
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+ | 0.3232 | 0.42 | 3500 | 0.4819 | 0.8624 |
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+ | 0.3178 | 0.48 | 4000 | 0.5071 | 0.8452 |
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+ | 0.2969 | 0.53 | 4500 | 0.4325 | 0.8578 |
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+ | 0.3162 | 0.59 | 5000 | 0.4296 | 0.8635 |
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+ | 0.2995 | 0.65 | 5500 | 0.5547 | 0.8463 |
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+ | 0.3016 | 0.71 | 6000 | 0.4364 | 0.8670 |
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+ | 0.2973 | 0.77 | 6500 | 0.4595 | 0.8555 |
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+ | 0.3068 | 0.83 | 7000 | 0.4519 | 0.8670 |
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+ | 0.2917 | 0.89 | 7500 | 0.4175 | 0.8716 |
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+ | 0.2819 | 0.95 | 8000 | 0.4741 | 0.8739 |
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+ | 0.2711 | 1.01 | 8500 | 0.5015 | 0.8842 |
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+ | 0.2173 | 1.07 | 9000 | 0.4956 | 0.8830 |
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+ | 0.2259 | 1.13 | 9500 | 0.6080 | 0.8761 |
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+ | 0.2655 | 1.19 | 10000 | 0.5456 | 0.8807 |
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+ | 0.2499 | 1.25 | 10500 | 0.5349 | 0.8796 |
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+ | 0.2291 | 1.31 | 11000 | 0.5214 | 0.8784 |
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+ | 0.2207 | 1.37 | 11500 | 0.5743 | 0.8853 |
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+ | 0.2463 | 1.43 | 12000 | 0.5499 | 0.8761 |
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+ | 0.2214 | 1.48 | 12500 | 0.5270 | 0.8819 |
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+ | 0.2114 | 1.54 | 13000 | 0.5762 | 0.8727 |
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+ | 0.2087 | 1.6 | 13500 | 0.5400 | 0.8819 |
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+ | 0.2123 | 1.66 | 14000 | 0.5719 | 0.8796 |
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+ | 0.2112 | 1.72 | 14500 | 0.5236 | 0.8819 |
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+ | 0.2042 | 1.78 | 15000 | 0.5373 | 0.8807 |
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+ | 0.2176 | 1.84 | 15500 | 0.5504 | 0.8853 |
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+ | 0.2032 | 1.9 | 16000 | 0.5701 | 0.8761 |
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+ | 0.213 | 1.96 | 16500 | 0.5560 | 0.875 |
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  ### Framework versions
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