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--- |
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language: |
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- en |
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license: mit |
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base_model: microsoft/deberta-v3-base |
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tags: |
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- nycu-112-2-datamining-hw2 |
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- generated_from_trainer |
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datasets: |
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- DandinPower/review_cleanonlytitleandtext |
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metrics: |
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- accuracy |
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model-index: |
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- name: deberta-v3-base-cotat |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: DandinPower/review_cleanonlytitleandtext |
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type: DandinPower/review_cleanonlytitleandtext |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.623 |
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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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should probably proofread and complete it, then remove this comment. --> |
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# deberta-v3-base-cotat |
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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_cleanonlytitleandtext dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4985 |
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- Accuracy: 0.623 |
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- Macro F1: 0.6247 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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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- lr_scheduler_warmup_steps: 1500 |
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- num_epochs: 5 |
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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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| 1.0223 | 0.14 | 500 | 0.9610 | 0.592 | 0.5971 | |
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| 1.0108 | 0.29 | 1000 | 0.9378 | 0.6044 | 0.6083 | |
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| 0.9323 | 0.43 | 1500 | 0.9605 | 0.589 | 0.5652 | |
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| 0.9651 | 0.57 | 2000 | 0.9845 | 0.5797 | 0.5687 | |
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| 0.928 | 0.71 | 2500 | 0.9521 | 0.5907 | 0.5656 | |
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| 0.9205 | 0.86 | 3000 | 0.9073 | 0.603 | 0.5740 | |
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| 0.9243 | 1.0 | 3500 | 0.8876 | 0.616 | 0.6113 | |
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| 0.8545 | 1.14 | 4000 | 0.8631 | 0.6267 | 0.6290 | |
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| 0.8267 | 1.29 | 4500 | 0.8908 | 0.624 | 0.6185 | |
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| 0.8175 | 1.43 | 5000 | 0.8771 | 0.6173 | 0.6222 | |
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| 0.8613 | 1.57 | 5500 | 0.9564 | 0.6209 | 0.6081 | |
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| 0.8138 | 1.71 | 6000 | 0.9246 | 0.6089 | 0.6063 | |
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| 0.7314 | 1.86 | 6500 | 0.9030 | 0.6329 | 0.6313 | |
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| 0.8287 | 2.0 | 7000 | 0.8753 | 0.6211 | 0.6235 | |
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| 0.6963 | 2.14 | 7500 | 0.9700 | 0.6247 | 0.6257 | |
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| 0.7034 | 2.29 | 8000 | 0.9592 | 0.6234 | 0.6220 | |
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| 0.679 | 2.43 | 8500 | 0.8994 | 0.6233 | 0.6272 | |
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| 0.7207 | 2.57 | 9000 | 1.0013 | 0.6236 | 0.6183 | |
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| 0.6992 | 2.71 | 9500 | 0.9385 | 0.6169 | 0.6219 | |
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| 0.7032 | 2.86 | 10000 | 0.9247 | 0.6366 | 0.6364 | |
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| 0.6949 | 3.0 | 10500 | 0.9615 | 0.6239 | 0.6281 | |
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| 0.5581 | 3.14 | 11000 | 1.0439 | 0.6217 | 0.6267 | |
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| 0.55 | 3.29 | 11500 | 1.1205 | 0.6259 | 0.6232 | |
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| 0.5496 | 3.43 | 12000 | 1.1122 | 0.6226 | 0.6267 | |
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| 0.5462 | 3.57 | 12500 | 1.0692 | 0.6251 | 0.6263 | |
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| 0.5121 | 3.71 | 13000 | 1.1563 | 0.6197 | 0.6214 | |
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| 0.531 | 3.86 | 13500 | 1.1123 | 0.6261 | 0.6256 | |
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| 0.5256 | 4.0 | 14000 | 1.1194 | 0.6247 | 0.6264 | |
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| 0.3908 | 4.14 | 14500 | 1.3631 | 0.6204 | 0.6210 | |
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| 0.4439 | 4.29 | 15000 | 1.4810 | 0.6204 | 0.6211 | |
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| 0.4252 | 4.43 | 15500 | 1.4454 | 0.6211 | 0.6217 | |
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| 0.3721 | 4.57 | 16000 | 1.5315 | 0.6204 | 0.6231 | |
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| 0.369 | 4.71 | 16500 | 1.4797 | 0.6184 | 0.6190 | |
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| 0.3907 | 4.86 | 17000 | 1.4857 | 0.6219 | 0.6234 | |
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| 0.4022 | 5.0 | 17500 | 1.4985 | 0.623 | 0.6247 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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