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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_mergeallfeaturetotext |
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metrics: |
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- accuracy |
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model-index: |
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- name: deberta-v3-base-maftt |
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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_mergeallfeaturetotext |
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type: DandinPower/review_mergeallfeaturetotext |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.6288571428571429 |
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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-maftt |
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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_mergeallfeaturetotext dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4616 |
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- Accuracy: 0.6289 |
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- Macro F1: 0.6302 |
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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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| 1.0302 | 0.14 | 500 | 1.0771 | 0.5511 | 0.5499 | |
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| 1.0412 | 0.29 | 1000 | 0.9406 | 0.5966 | 0.6030 | |
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| 0.9494 | 0.43 | 1500 | 0.9546 | 0.5949 | 0.5602 | |
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| 0.898 | 0.57 | 2000 | 1.0436 | 0.5957 | 0.5872 | |
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| 0.9171 | 0.71 | 2500 | 0.9004 | 0.622 | 0.6074 | |
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| 0.8856 | 0.86 | 3000 | 0.8741 | 0.6137 | 0.5990 | |
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| 0.9359 | 1.0 | 3500 | 0.8821 | 0.6267 | 0.6245 | |
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| 0.8626 | 1.14 | 4000 | 0.8859 | 0.6213 | 0.6200 | |
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| 0.7953 | 1.29 | 4500 | 0.8606 | 0.6337 | 0.6271 | |
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| 0.8206 | 1.43 | 5000 | 0.8543 | 0.6169 | 0.6202 | |
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| 0.8184 | 1.57 | 5500 | 0.9360 | 0.6266 | 0.6165 | |
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| 0.8044 | 1.71 | 6000 | 0.8606 | 0.6234 | 0.6227 | |
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| 0.7094 | 1.86 | 6500 | 0.8842 | 0.6434 | 0.6387 | |
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| 0.8264 | 2.0 | 7000 | 0.9063 | 0.612 | 0.6128 | |
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| 0.6951 | 2.14 | 7500 | 0.8782 | 0.6386 | 0.6415 | |
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| 0.704 | 2.29 | 8000 | 0.9510 | 0.6326 | 0.6308 | |
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| 0.6806 | 2.43 | 8500 | 0.8709 | 0.6413 | 0.6455 | |
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| 0.6983 | 2.57 | 9000 | 0.8977 | 0.6426 | 0.6436 | |
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| 0.6852 | 2.71 | 9500 | 0.9686 | 0.5984 | 0.6010 | |
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| 0.6761 | 2.86 | 10000 | 0.8961 | 0.6386 | 0.6406 | |
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| 0.6804 | 3.0 | 10500 | 0.9378 | 0.6307 | 0.6332 | |
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| 0.5329 | 3.14 | 11000 | 1.1209 | 0.6341 | 0.6382 | |
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| 0.5461 | 3.29 | 11500 | 1.0323 | 0.6393 | 0.6377 | |
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| 0.5725 | 3.43 | 12000 | 1.0678 | 0.6334 | 0.6366 | |
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| 0.5499 | 3.57 | 12500 | 1.0547 | 0.6374 | 0.6394 | |
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| 0.5218 | 3.71 | 13000 | 1.0524 | 0.6453 | 0.6460 | |
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| 0.5022 | 3.86 | 13500 | 1.1100 | 0.6363 | 0.6358 | |
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| 0.534 | 4.0 | 14000 | 1.0378 | 0.6357 | 0.6386 | |
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| 0.3823 | 4.14 | 14500 | 1.3985 | 0.6357 | 0.6357 | |
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| 0.4518 | 4.29 | 15000 | 1.3265 | 0.6314 | 0.6318 | |
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| 0.4147 | 4.43 | 15500 | 1.3946 | 0.631 | 0.6324 | |
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| 0.3936 | 4.57 | 16000 | 1.4649 | 0.6279 | 0.6308 | |
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| 0.4339 | 4.71 | 16500 | 1.5322 | 0.6286 | 0.6314 | |
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| 0.4448 | 4.86 | 17000 | 1.4890 | 0.629 | 0.6302 | |
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| 0.4006 | 5.0 | 17500 | 1.4616 | 0.6289 | 0.6302 | |
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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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