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--- |
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base_model: vinai/phobert-base-v2 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: CS505-Classifier-T4_predictLabel_a1_v5 |
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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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# CS505-Classifier-T4_predictLabel_a1_v5 |
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This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0018 |
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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: 5e-05 |
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- train_batch_size: 32 |
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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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- num_epochs: 40 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| No log | 0.98 | 48 | 0.6517 | |
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| No log | 1.96 | 96 | 0.3227 | |
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| No log | 2.94 | 144 | 0.2342 | |
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| No log | 3.92 | 192 | 0.1815 | |
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| No log | 4.9 | 240 | 0.1703 | |
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| No log | 5.88 | 288 | 0.1231 | |
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| No log | 6.86 | 336 | 0.0730 | |
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| No log | 7.84 | 384 | 0.0803 | |
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| No log | 8.82 | 432 | 0.0476 | |
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| No log | 9.8 | 480 | 0.0384 | |
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| 0.2908 | 10.78 | 528 | 0.0281 | |
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| 0.2908 | 11.76 | 576 | 0.0329 | |
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| 0.2908 | 12.73 | 624 | 0.0234 | |
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| 0.2908 | 13.71 | 672 | 0.0119 | |
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| 0.2908 | 14.69 | 720 | 0.0101 | |
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| 0.2908 | 15.67 | 768 | 0.0081 | |
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| 0.2908 | 16.65 | 816 | 0.0137 | |
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| 0.2908 | 17.63 | 864 | 0.0075 | |
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| 0.2908 | 18.61 | 912 | 0.0053 | |
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| 0.2908 | 19.59 | 960 | 0.0035 | |
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| 0.0216 | 20.57 | 1008 | 0.0060 | |
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| 0.0216 | 21.55 | 1056 | 0.0028 | |
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| 0.0216 | 22.53 | 1104 | 0.0027 | |
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| 0.0216 | 23.51 | 1152 | 0.0026 | |
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| 0.0216 | 24.49 | 1200 | 0.0024 | |
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| 0.0216 | 25.47 | 1248 | 0.0023 | |
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| 0.0216 | 26.45 | 1296 | 0.0022 | |
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| 0.0216 | 27.43 | 1344 | 0.0022 | |
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| 0.0216 | 28.41 | 1392 | 0.0021 | |
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| 0.0216 | 29.39 | 1440 | 0.0020 | |
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| 0.0216 | 30.37 | 1488 | 0.0021 | |
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| 0.0043 | 31.35 | 1536 | 0.0020 | |
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| 0.0043 | 32.33 | 1584 | 0.0019 | |
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| 0.0043 | 33.31 | 1632 | 0.0019 | |
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| 0.0043 | 34.29 | 1680 | 0.0019 | |
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| 0.0043 | 35.27 | 1728 | 0.0019 | |
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| 0.0043 | 36.24 | 1776 | 0.0019 | |
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| 0.0043 | 37.22 | 1824 | 0.0019 | |
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| 0.0043 | 38.2 | 1872 | 0.0018 | |
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| 0.0043 | 39.18 | 1920 | 0.0018 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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