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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- wisesight_sentiment |
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base_model: airesearch/wangchanberta-base-att-spm-uncased |
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model-index: |
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- name: Wangchanberta-Depress-Finetuned |
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results: [] |
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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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# Wangchanberta-Depress-Finetuned |
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This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the wisesight_sentiment dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5910 |
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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: 2e-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: 400 |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 1.0114 | 0.08 | 200 | 0.9538 | |
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| 0.8617 | 0.15 | 400 | 0.8280 | |
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| 0.7882 | 0.23 | 600 | 0.7472 | |
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| 0.7132 | 0.3 | 800 | 0.7264 | |
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| 0.7226 | 0.38 | 1000 | 0.7265 | |
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| 0.6854 | 0.45 | 1200 | 0.6792 | |
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| 0.621 | 0.53 | 1400 | 0.6451 | |
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| 0.6093 | 0.61 | 1600 | 0.6364 | |
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| 0.6099 | 0.68 | 1800 | 0.6128 | |
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| 0.5766 | 0.76 | 2000 | 0.6388 | |
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| 0.6033 | 0.83 | 2200 | 0.6148 | |
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| 0.5966 | 0.91 | 2400 | 0.6440 | |
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| 0.6208 | 0.98 | 2600 | 0.5910 | |
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| 0.5178 | 1.06 | 2800 | 0.6340 | |
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| 0.4863 | 1.13 | 3000 | 0.7177 | |
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| 0.4852 | 1.21 | 3200 | 0.6766 | |
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| 0.4711 | 1.29 | 3400 | 0.6739 | |
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| 0.5203 | 1.36 | 3600 | 0.6429 | |
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| 0.5167 | 1.44 | 3800 | 0.6539 | |
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| 0.5053 | 1.51 | 4000 | 0.6172 | |
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| 0.5076 | 1.59 | 4200 | 0.6053 | |
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| 0.4704 | 1.66 | 4400 | 0.6474 | |
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| 0.4807 | 1.74 | 4600 | 0.6225 | |
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| 0.4792 | 1.82 | 4800 | 0.6282 | |
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| 0.5177 | 1.89 | 5000 | 0.6011 | |
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| 0.4839 | 1.97 | 5200 | 0.6231 | |
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| 0.4155 | 2.04 | 5400 | 0.6668 | |
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| 0.3923 | 2.12 | 5600 | 0.6886 | |
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| 0.3713 | 2.19 | 5800 | 0.6895 | |
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| 0.364 | 2.27 | 6000 | 0.6886 | |
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| 0.3774 | 2.34 | 6200 | 0.7117 | |
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| 0.4001 | 2.42 | 6400 | 0.7081 | |
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| 0.3531 | 2.5 | 6600 | 0.7465 | |
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| 0.3768 | 2.57 | 6800 | 0.7706 | |
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| 0.3324 | 2.65 | 7000 | 0.7456 | |
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| 0.3597 | 2.72 | 7200 | 0.7507 | |
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| 0.3868 | 2.8 | 7400 | 0.7542 | |
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| 0.4141 | 2.87 | 7600 | 0.7223 | |
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| 0.3701 | 2.95 | 7800 | 0.7374 | |
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| 0.3175 | 3.03 | 8000 | 0.7615 | |
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| 0.2951 | 3.1 | 8200 | 0.7880 | |
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| 0.2885 | 3.18 | 8400 | 0.8158 | |
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| 0.2913 | 3.25 | 8600 | 0.8565 | |
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| 0.2815 | 3.33 | 8800 | 0.8649 | |
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| 0.2748 | 3.4 | 9000 | 0.8783 | |
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| 0.2776 | 3.48 | 9200 | 0.8851 | |
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| 0.2982 | 3.56 | 9400 | 0.8922 | |
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| 0.2939 | 3.63 | 9600 | 0.8796 | |
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| 0.2712 | 3.71 | 9800 | 0.8873 | |
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| 0.2918 | 3.78 | 10000 | 0.8973 | |
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| 0.3144 | 3.86 | 10200 | 0.8978 | |
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| 0.2988 | 3.93 | 10400 | 0.8951 | |
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### Framework versions |
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- Transformers 4.11.2 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.1.0 |
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- Tokenizers 0.10.3 |
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