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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-small |
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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-small-otat-recommened-hp |
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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: 0.6228571428571429 |
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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-small-otat-recommened-hp |
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This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the DandinPower/review_onlytitleandtext dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6500 |
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- Accuracy: 0.6229 |
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- Macro F1: 0.6240 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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: 10 |
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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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| 0.8942 | 1.14 | 500 | 0.8753 | 0.6316 | 0.6330 | |
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| 0.7816 | 2.29 | 1000 | 0.8880 | 0.633 | 0.6216 | |
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| 0.7231 | 3.43 | 1500 | 0.8827 | 0.632 | 0.6322 | |
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| 0.6145 | 4.57 | 2000 | 0.9674 | 0.6369 | 0.6329 | |
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| 0.4694 | 5.71 | 2500 | 1.0903 | 0.6249 | 0.6200 | |
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| 0.3611 | 6.86 | 3000 | 1.2490 | 0.6216 | 0.6249 | |
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| 0.278 | 8.0 | 3500 | 1.4194 | 0.6201 | 0.6230 | |
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| 0.1689 | 9.14 | 4000 | 1.6500 | 0.6229 | 0.6240 | |
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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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