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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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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: phobert-base-v2-70k-khduoi |
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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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# phobert-base-v2-70k-khduoi |
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This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6366 |
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- Accuracy: 0.9152 |
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- F1: 0.9155 |
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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: 32 |
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- eval_batch_size: 32 |
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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: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:| |
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| No log | 0.2909 | 500 | 0.2524 | 0.8975 | 0.8969 | |
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| No log | 0.5817 | 1000 | 0.2447 | 0.9009 | 0.8999 | |
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| No log | 0.8726 | 1500 | 0.2319 | 0.9018 | 0.9015 | |
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| 0.263 | 1.1635 | 2000 | 0.2539 | 0.9060 | 0.9063 | |
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| 0.263 | 1.4543 | 2500 | 0.2433 | 0.9017 | 0.9029 | |
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| 0.263 | 1.7452 | 3000 | 0.2358 | 0.9100 | 0.9099 | |
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| 0.2084 | 2.0361 | 3500 | 0.2755 | 0.9044 | 0.9059 | |
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| 0.2084 | 2.3269 | 4000 | 0.2547 | 0.9102 | 0.9100 | |
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| 0.2084 | 2.6178 | 4500 | 0.2223 | 0.9109 | 0.9125 | |
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| 0.2084 | 2.9087 | 5000 | 0.2189 | 0.9150 | 0.9150 | |
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| 0.1729 | 3.1995 | 5500 | 0.2825 | 0.9101 | 0.9112 | |
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| 0.1729 | 3.4904 | 6000 | 0.2663 | 0.9110 | 0.9121 | |
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| 0.1729 | 3.7813 | 6500 | 0.2367 | 0.9157 | 0.9165 | |
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| 0.1448 | 4.0721 | 7000 | 0.2891 | 0.9118 | 0.9121 | |
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| 0.1448 | 4.3630 | 7500 | 0.3180 | 0.9042 | 0.9060 | |
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| 0.1448 | 4.6539 | 8000 | 0.2441 | 0.9117 | 0.9126 | |
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| 0.1448 | 4.9447 | 8500 | 0.2638 | 0.9142 | 0.9145 | |
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| 0.1234 | 5.2356 | 9000 | 0.3499 | 0.9130 | 0.9141 | |
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| 0.1234 | 5.5265 | 9500 | 0.3086 | 0.9123 | 0.9135 | |
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| 0.1234 | 5.8173 | 10000 | 0.3203 | 0.9141 | 0.9140 | |
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| 0.1033 | 6.1082 | 10500 | 0.3234 | 0.9170 | 0.9173 | |
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| 0.1033 | 6.3991 | 11000 | 0.3367 | 0.9095 | 0.9105 | |
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| 0.1033 | 6.6899 | 11500 | 0.3402 | 0.9157 | 0.9159 | |
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| 0.1033 | 6.9808 | 12000 | 0.3843 | 0.9107 | 0.9111 | |
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| 0.0904 | 7.2717 | 12500 | 0.3559 | 0.9182 | 0.9182 | |
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| 0.0904 | 7.5625 | 13000 | 0.3646 | 0.9079 | 0.9096 | |
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| 0.0904 | 7.8534 | 13500 | 0.3392 | 0.9130 | 0.9137 | |
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| 0.0785 | 8.1443 | 14000 | 0.4064 | 0.9155 | 0.9164 | |
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| 0.0785 | 8.4351 | 14500 | 0.4013 | 0.9126 | 0.9135 | |
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| 0.0785 | 8.7260 | 15000 | 0.4351 | 0.9124 | 0.9135 | |
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| 0.0701 | 9.0169 | 15500 | 0.4190 | 0.9158 | 0.9161 | |
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| 0.0701 | 9.3077 | 16000 | 0.4567 | 0.9116 | 0.9126 | |
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| 0.0701 | 9.5986 | 16500 | 0.4230 | 0.9147 | 0.9147 | |
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| 0.0701 | 9.8895 | 17000 | 0.3956 | 0.9148 | 0.9150 | |
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| 0.0599 | 10.1803 | 17500 | 0.4854 | 0.9133 | 0.9135 | |
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| 0.0599 | 10.4712 | 18000 | 0.4958 | 0.9156 | 0.9158 | |
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| 0.0599 | 10.7621 | 18500 | 0.4552 | 0.9148 | 0.9146 | |
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| 0.0536 | 11.0529 | 19000 | 0.4678 | 0.9160 | 0.9163 | |
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| 0.0536 | 11.3438 | 19500 | 0.4802 | 0.9142 | 0.9135 | |
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| 0.0536 | 11.6347 | 20000 | 0.5360 | 0.9130 | 0.9133 | |
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| 0.0536 | 11.9255 | 20500 | 0.5305 | 0.9133 | 0.9137 | |
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| 0.0464 | 12.2164 | 21000 | 0.5413 | 0.9115 | 0.9122 | |
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| 0.0464 | 12.5073 | 21500 | 0.4867 | 0.9150 | 0.9155 | |
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| 0.0464 | 12.7981 | 22000 | 0.5100 | 0.9147 | 0.9153 | |
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| 0.0446 | 13.0890 | 22500 | 0.5750 | 0.9161 | 0.9157 | |
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| 0.0446 | 13.3799 | 23000 | 0.5742 | 0.9174 | 0.9172 | |
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| 0.0446 | 13.6707 | 23500 | 0.5790 | 0.9142 | 0.9146 | |
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| 0.0446 | 13.9616 | 24000 | 0.5476 | 0.9151 | 0.9150 | |
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| 0.0374 | 14.2525 | 24500 | 0.5621 | 0.9160 | 0.9163 | |
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| 0.0374 | 14.5433 | 25000 | 0.5633 | 0.9140 | 0.9146 | |
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| 0.0374 | 14.8342 | 25500 | 0.5496 | 0.9148 | 0.9152 | |
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| 0.0341 | 15.1251 | 26000 | 0.5869 | 0.9138 | 0.9142 | |
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| 0.0341 | 15.4159 | 26500 | 0.5901 | 0.9142 | 0.9141 | |
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| 0.0341 | 15.7068 | 27000 | 0.5548 | 0.9154 | 0.9158 | |
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| 0.0303 | 15.9977 | 27500 | 0.5832 | 0.9141 | 0.9136 | |
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| 0.0303 | 16.2885 | 28000 | 0.6070 | 0.9148 | 0.9157 | |
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| 0.0303 | 16.5794 | 28500 | 0.6208 | 0.9159 | 0.9162 | |
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| 0.0303 | 16.8703 | 29000 | 0.6134 | 0.9137 | 0.9143 | |
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| 0.0273 | 17.1611 | 29500 | 0.6021 | 0.9166 | 0.9168 | |
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| 0.0273 | 17.4520 | 30000 | 0.6063 | 0.9150 | 0.9153 | |
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| 0.0273 | 17.7429 | 30500 | 0.5942 | 0.9135 | 0.9142 | |
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| 0.0254 | 18.0337 | 31000 | 0.6073 | 0.9150 | 0.9155 | |
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| 0.0254 | 18.3246 | 31500 | 0.6304 | 0.9165 | 0.9167 | |
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| 0.0254 | 18.6155 | 32000 | 0.6121 | 0.9155 | 0.9157 | |
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| 0.0254 | 18.9063 | 32500 | 0.6087 | 0.9153 | 0.9156 | |
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| 0.0221 | 19.1972 | 33000 | 0.6234 | 0.9147 | 0.9151 | |
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| 0.0221 | 19.4881 | 33500 | 0.6312 | 0.9145 | 0.9149 | |
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| 0.0221 | 19.7789 | 34000 | 0.6366 | 0.9152 | 0.9155 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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