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distilroberta-rbm231k-ep20-op40-phrase5k

Text Classification
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roberta
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  1. README.md +23 -16
  2. model.safetensors +1 -1
README.md CHANGED
@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [judy93536/distilroberta-rbm231k-ep20-op40](https://huggingface.co/judy93536/distilroberta-rbm231k-ep20-op40) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1681
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- - Accuracy: 0.9560
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  ## Model description
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@@ -44,26 +44,33 @@ The following hyperparameters were used during training:
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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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  - lr_scheduler_warmup_ratio: 0.28
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- - num_epochs: 13
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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 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 250 | 0.2748 | 0.9119 |
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- | 0.2834 | 2.0 | 500 | 0.2418 | 0.9219 |
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- | 0.2834 | 3.0 | 750 | 0.2013 | 0.9329 |
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- | 0.2102 | 4.0 | 1000 | 0.1839 | 0.9389 |
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- | 0.2102 | 5.0 | 1250 | 0.1848 | 0.9419 |
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- | 0.1539 | 6.0 | 1500 | 0.1658 | 0.9469 |
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- | 0.1539 | 7.0 | 1750 | 0.1685 | 0.9469 |
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- | 0.1211 | 8.0 | 2000 | 0.1577 | 0.9550 |
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- | 0.1211 | 9.0 | 2250 | 0.1625 | 0.9540 |
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- | 0.1122 | 10.0 | 2500 | 0.1694 | 0.9520 |
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- | 0.1122 | 11.0 | 2750 | 0.1583 | 0.9570 |
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- | 0.1055 | 12.0 | 3000 | 0.1651 | 0.9570 |
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- | 0.1055 | 13.0 | 3250 | 0.1681 | 0.9560 |
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [judy93536/distilroberta-rbm231k-ep20-op40](https://huggingface.co/judy93536/distilroberta-rbm231k-ep20-op40) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1735
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+ - Accuracy: 0.9459
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  ## Model description
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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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  - lr_scheduler_warmup_ratio: 0.28
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+ - num_epochs: 20
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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 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 250 | 1.0641 | 0.6156 |
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+ | 1.0561 | 2.0 | 500 | 0.9262 | 0.6136 |
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+ | 1.0561 | 3.0 | 750 | 0.8124 | 0.6136 |
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+ | 0.8474 | 4.0 | 1000 | 0.7271 | 0.6476 |
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+ | 0.8474 | 5.0 | 1250 | 0.6283 | 0.7237 |
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+ | 0.6495 | 6.0 | 1500 | 0.5066 | 0.7758 |
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+ | 0.6495 | 7.0 | 1750 | 0.3436 | 0.8979 |
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+ | 0.3657 | 8.0 | 2000 | 0.2598 | 0.9189 |
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+ | 0.3657 | 9.0 | 2250 | 0.2117 | 0.9299 |
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+ | 0.2023 | 10.0 | 2500 | 0.2022 | 0.9279 |
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+ | 0.2023 | 11.0 | 2750 | 0.1787 | 0.9329 |
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+ | 0.1518 | 12.0 | 3000 | 0.1632 | 0.9429 |
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+ | 0.1518 | 13.0 | 3250 | 0.1883 | 0.9369 |
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+ | 0.1324 | 14.0 | 3500 | 0.1699 | 0.9409 |
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+ | 0.1324 | 15.0 | 3750 | 0.1704 | 0.9439 |
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+ | 0.1225 | 16.0 | 4000 | 0.1755 | 0.9429 |
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+ | 0.1225 | 17.0 | 4250 | 0.1743 | 0.9439 |
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+ | 0.119 | 18.0 | 4500 | 0.1726 | 0.9459 |
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+ | 0.119 | 19.0 | 4750 | 0.1723 | 0.9459 |
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+ | 0.1145 | 20.0 | 5000 | 0.1735 | 0.9459 |
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  ### Framework versions
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