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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_onlytitleandtext |
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metrics: |
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- accuracy |
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model-index: |
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- name: deberta-v3-base-otat |
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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_onlytitleandtext |
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type: DandinPower/review_onlytitleandtext |
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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 |
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should probably proofread and complete it, then remove this comment. --> |
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# deberta-v3-base-otat |
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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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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: 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 |
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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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| 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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- 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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