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DandinPower
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deberta-v3-base-otat

Text Classification
Transformers
Safetensors
English
deberta-v2
nycu-112-2-datamining-hw2
Generated from Trainer
Eval Results
Inference Endpoints
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DandinPower commited on
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End of training

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  1. README.md +42 -15
  2. model.safetensors +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.6360357142857143
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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
@@ -32,9 +32,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the DandinPower/review_onlytitleandtext dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.5029
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- - Accuracy: 0.6360
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- - Macro F1: 0.6367
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  ## Model description
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@@ -55,7 +55,7 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 4.5e-05
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  - train_batch_size: 8
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- - eval_batch_size: 16
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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
@@ -64,16 +64,43 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
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- | 0.9961 | 0.57 | 500 | 0.9958 | 0.5675 | 0.5638 |
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- | 0.9267 | 1.14 | 1000 | 0.9776 | 0.5814 | 0.5727 |
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- | 0.9086 | 1.71 | 1500 | 1.1673 | 0.5709 | 0.5355 |
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- | 0.744 | 2.29 | 2000 | 0.9788 | 0.6325 | 0.6267 |
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- | 0.7131 | 2.86 | 2500 | 0.9493 | 0.6219 | 0.6203 |
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- | 0.5815 | 3.43 | 3000 | 0.9966 | 0.6224 | 0.6259 |
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- | 0.5434 | 4.0 | 3500 | 1.1400 | 0.6336 | 0.6326 |
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- | 0.3162 | 4.57 | 4000 | 1.5029 | 0.6360 | 0.6367 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.639
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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 is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the DandinPower/review_onlytitleandtext dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.4437
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+ - Accuracy: 0.639
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+ - Macro F1: 0.6399
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 4.5e-05
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  - train_batch_size: 8
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+ - eval_batch_size: 8
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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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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
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+ | 0.9984 | 0.14 | 500 | 0.9957 | 0.5819 | 0.5794 |
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+ | 1.0009 | 0.29 | 1000 | 0.9064 | 0.6161 | 0.6222 |
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+ | 0.9462 | 0.43 | 1500 | 0.9272 | 0.6047 | 0.5906 |
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+ | 0.9037 | 0.57 | 2000 | 0.9866 | 0.5817 | 0.5750 |
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+ | 0.8923 | 0.71 | 2500 | 0.8666 | 0.6124 | 0.5898 |
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+ | 0.905 | 0.86 | 3000 | 0.8855 | 0.5996 | 0.5745 |
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+ | 0.9017 | 1.0 | 3500 | 0.8521 | 0.6276 | 0.6258 |
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+ | 0.8487 | 1.14 | 4000 | 0.8540 | 0.6309 | 0.6292 |
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+ | 0.8042 | 1.29 | 4500 | 0.8534 | 0.6323 | 0.6294 |
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+ | 0.8165 | 1.43 | 5000 | 0.8350 | 0.6347 | 0.6389 |
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+ | 0.8224 | 1.57 | 5500 | 0.8687 | 0.6321 | 0.6279 |
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+ | 0.7799 | 1.71 | 6000 | 0.8810 | 0.6316 | 0.6298 |
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+ | 0.7354 | 1.86 | 6500 | 0.8719 | 0.639 | 0.6346 |
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+ | 0.8026 | 2.0 | 7000 | 0.8829 | 0.6159 | 0.6154 |
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+ | 0.6818 | 2.14 | 7500 | 0.9274 | 0.6383 | 0.6408 |
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+ | 0.6704 | 2.29 | 8000 | 0.9327 | 0.6401 | 0.6377 |
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+ | 0.6498 | 2.43 | 8500 | 0.8786 | 0.6367 | 0.6414 |
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+ | 0.6956 | 2.57 | 9000 | 0.9165 | 0.6374 | 0.6320 |
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+ | 0.6729 | 2.71 | 9500 | 0.9929 | 0.6116 | 0.6153 |
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+ | 0.6963 | 2.86 | 10000 | 0.8843 | 0.6397 | 0.6418 |
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+ | 0.6795 | 3.0 | 10500 | 0.9204 | 0.6471 | 0.6492 |
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+ | 0.536 | 3.14 | 11000 | 1.0496 | 0.641 | 0.6447 |
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+ | 0.5212 | 3.29 | 11500 | 1.0836 | 0.6466 | 0.6466 |
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+ | 0.5278 | 3.43 | 12000 | 1.0635 | 0.6377 | 0.6420 |
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+ | 0.5631 | 3.57 | 12500 | 1.0144 | 0.6436 | 0.6449 |
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+ | 0.4899 | 3.71 | 13000 | 1.1613 | 0.6416 | 0.6420 |
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+ | 0.509 | 3.86 | 13500 | 1.0841 | 0.6446 | 0.6442 |
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+ | 0.5176 | 4.0 | 14000 | 1.0819 | 0.639 | 0.6426 |
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+ | 0.3587 | 4.14 | 14500 | 1.3046 | 0.6401 | 0.6412 |
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+ | 0.4342 | 4.29 | 15000 | 1.3250 | 0.6371 | 0.6394 |
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+ | 0.3358 | 4.43 | 15500 | 1.4140 | 0.6387 | 0.6395 |
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+ | 0.3773 | 4.57 | 16000 | 1.4286 | 0.6399 | 0.6416 |
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+ | 0.4173 | 4.71 | 16500 | 1.4825 | 0.6393 | 0.6396 |
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+ | 0.4072 | 4.86 | 17000 | 1.4357 | 0.6393 | 0.6405 |
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+ | 0.3743 | 5.0 | 17500 | 1.4437 | 0.639 | 0.6399 |
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  ### Framework versions
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