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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-large |
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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-large-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: 0.6685714285714286 |
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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-large-otat-recommened-hp |
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co./microsoft/deberta-v3-large) on the DandinPower/review_onlytitleandtext dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8169 |
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- Accuracy: 0.6686 |
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- Macro F1: 0.6662 |
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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: 6e-06 |
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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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- gradient_accumulation_steps: 8 |
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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: 50 |
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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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| 0.7726 | 1.14 | 500 | 0.8107 | 0.6613 | 0.6602 | |
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| 0.6983 | 2.29 | 1000 | 0.7739 | 0.669 | 0.6662 | |
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| 0.6504 | 3.43 | 1500 | 0.7891 | 0.6726 | 0.6725 | |
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| 0.6067 | 4.57 | 2000 | 0.8169 | 0.6686 | 0.6662 | |
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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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