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--- |
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license: mit |
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base_model: microsoft/mdeberta-v3-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- precision |
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- recall |
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- accuracy |
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model-index: |
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- name: mdeberta-v3-base_ordinal_5_seed69_multilingual |
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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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# mdeberta-v3-base_ordinal_5_seed69_multilingual |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.2865 |
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- Mse: 2.5355 |
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- Rmse: 1.5923 |
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- Mae: 1.0336 |
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- R2: 0.2365 |
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- F1: 0.7527 |
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- Precision: 0.7544 |
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- Recall: 0.7591 |
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- Accuracy: 0.7591 |
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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-06 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 200 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mse | Rmse | Mae | R2 | F1 | Precision | Recall | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:-------:|:------:|:---------:|:------:|:--------:| |
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| 3.4461 | 0.1449 | 100 | 3.2319 | 3.3564 | 1.8321 | 1.7300 | -0.0820 | 0.5372 | 0.4484 | 0.6697 | 0.6697 | |
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| 3.1709 | 0.2899 | 200 | 2.9852 | 3.1868 | 1.7852 | 1.5359 | -0.0273 | 0.5372 | 0.4484 | 0.6697 | 0.6697 | |
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| 3.0364 | 0.4348 | 300 | 2.8881 | 3.2227 | 1.7952 | 1.5359 | -0.0389 | 0.5372 | 0.4484 | 0.6697 | 0.6697 | |
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| 2.8969 | 0.5797 | 400 | 2.7693 | 3.0057 | 1.7337 | 1.3271 | 0.0311 | 0.5372 | 0.4484 | 0.6697 | 0.6697 | |
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| 2.8529 | 0.7246 | 500 | 2.6898 | 2.8148 | 1.6777 | 1.3434 | 0.0926 | 0.5372 | 0.4484 | 0.6697 | 0.6697 | |
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| 2.781 | 0.8696 | 600 | 2.6252 | 2.7806 | 1.6675 | 1.3010 | 0.1036 | 0.5372 | 0.4484 | 0.6697 | 0.6697 | |
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| 2.6867 | 1.0145 | 700 | 2.5549 | 2.8711 | 1.6944 | 1.2088 | 0.0744 | 0.5893 | 0.6724 | 0.6827 | 0.6827 | |
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| 2.4738 | 1.1594 | 800 | 2.5246 | 2.8687 | 1.6937 | 1.1574 | 0.0752 | 0.7006 | 0.6990 | 0.7129 | 0.7129 | |
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| 2.4723 | 1.3043 | 900 | 2.4213 | 2.6020 | 1.6131 | 1.1126 | 0.1612 | 0.6983 | 0.7216 | 0.7300 | 0.7300 | |
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| 2.5362 | 1.4493 | 1000 | 2.4109 | 2.5506 | 1.5971 | 1.1884 | 0.1778 | 0.7136 | 0.7133 | 0.7259 | 0.7259 | |
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| 2.4032 | 1.5942 | 1100 | 2.4424 | 2.7520 | 1.6589 | 1.0799 | 0.1128 | 0.7040 | 0.7158 | 0.7284 | 0.7284 | |
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| 2.3445 | 1.7391 | 1200 | 2.3788 | 2.6387 | 1.6244 | 1.0954 | 0.1494 | 0.7336 | 0.7315 | 0.7390 | 0.7390 | |
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| 2.364 | 1.8841 | 1300 | 2.3767 | 2.6533 | 1.6289 | 1.0595 | 0.1446 | 0.7150 | 0.7298 | 0.7390 | 0.7390 | |
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| 2.3271 | 2.0290 | 1400 | 2.4050 | 2.7684 | 1.6638 | 1.1060 | 0.1076 | 0.7180 | 0.7161 | 0.7268 | 0.7268 | |
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| 2.1281 | 2.1739 | 1500 | 2.4391 | 2.7920 | 1.6709 | 1.0775 | 0.0999 | 0.7238 | 0.7231 | 0.7341 | 0.7341 | |
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| 2.1314 | 2.3188 | 1600 | 2.3782 | 2.6688 | 1.6337 | 1.0799 | 0.1396 | 0.7338 | 0.7323 | 0.7357 | 0.7357 | |
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| 2.1279 | 2.4638 | 1700 | 2.3962 | 2.7325 | 1.6530 | 1.0538 | 0.1191 | 0.7227 | 0.7335 | 0.7431 | 0.7431 | |
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| 2.0826 | 2.6087 | 1800 | 2.3376 | 2.6142 | 1.6168 | 1.0449 | 0.1573 | 0.7335 | 0.7351 | 0.7455 | 0.7455 | |
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| 2.1644 | 2.7536 | 1900 | 2.3175 | 2.5522 | 1.5976 | 1.0791 | 0.1772 | 0.7312 | 0.7293 | 0.7341 | 0.7341 | |
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| 2.0112 | 2.8986 | 2000 | 2.2738 | 2.5220 | 1.5881 | 1.0261 | 0.1870 | 0.7459 | 0.7452 | 0.7537 | 0.7537 | |
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| 1.9844 | 3.0435 | 2100 | 2.3487 | 2.6860 | 1.6389 | 1.0122 | 0.1341 | 0.7405 | 0.7410 | 0.7504 | 0.7504 | |
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| 1.8639 | 3.1884 | 2200 | 2.3881 | 2.7716 | 1.6648 | 1.0082 | 0.1065 | 0.7447 | 0.7431 | 0.7471 | 0.7471 | |
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| 1.7698 | 3.3333 | 2300 | 2.4045 | 2.8018 | 1.6739 | 0.9927 | 0.0968 | 0.7484 | 0.7465 | 0.7520 | 0.7520 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.1 |
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