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--- |
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language: |
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- en |
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license: mit |
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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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base_model: microsoft/deberta-v2-xxlarge |
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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-v2-xxlarge-otat-recommened-hp |
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results: |
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- task: |
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type: text-classification |
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name: 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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- type: accuracy |
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value: 0.6741428571428572 |
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name: Accuracy |
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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-v2-xxlarge-otat-recommened-hp |
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This model is a fine-tuned version of [microsoft/deberta-v2-xxlarge](https://huggingface.co/microsoft/deberta-v2-xxlarge) on the DandinPower/review_onlytitleandtext dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7864 |
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- Accuracy: 0.6741 |
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- Macro F1: 0.6719 |
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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: 3e-06 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 64 |
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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: 1 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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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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| 0.9641 | 0.46 | 200 | 0.8451 | 0.6327 | 0.6341 | |
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| 0.8263 | 0.91 | 400 | 0.7768 | 0.6651 | 0.6650 | |
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| 0.7605 | 1.37 | 600 | 0.7842 | 0.667 | 0.6667 | |
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| 0.7496 | 1.83 | 800 | 0.7790 | 0.6659 | 0.6650 | |
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| 0.7034 | 2.29 | 1000 | 0.7738 | 0.67 | 0.6639 | |
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| 0.7134 | 2.74 | 1200 | 0.7671 | 0.6694 | 0.6698 | |
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| 0.6839 | 3.2 | 1400 | 0.7754 | 0.6743 | 0.6770 | |
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| 0.6699 | 3.66 | 1600 | 0.7853 | 0.6711 | 0.6666 | |
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| 0.6502 | 4.11 | 1800 | 0.7789 | 0.671 | 0.6692 | |
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| 0.6431 | 4.57 | 2000 | 0.7864 | 0.6741 | 0.6719 | |
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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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