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--- |
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library_name: transformers |
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license: mit |
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base_model: Mardiyyah/cellate1.0-tapt_freeze_llrd_ww_mask-LR_2e-05 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: TAPT_CeLLaTe_llrd_only |
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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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# TAPT_CeLLaTe_llrd_only |
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This model is a fine-tuned version of [Mardiyyah/cellate1.0-tapt_freeze_llrd_ww_mask-LR_2e-05](https://huggingface.co/Mardiyyah/cellate1.0-tapt_freeze_llrd_ww_mask-LR_2e-05) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1153 |
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- Precision: 0.8168 |
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- Recall: 0.8404 |
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- F1: 0.8285 |
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- Accuracy: 0.9743 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 3407 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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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 | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 1.5314 | 1.0 | 55 | 0.6843 | 0.0 | 0.0 | 0.0 | 0.8947 | |
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| 0.4343 | 2.0 | 110 | 0.2517 | 0.3447 | 0.3526 | 0.3486 | 0.9195 | |
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| 0.2147 | 3.0 | 165 | 0.1484 | 0.6493 | 0.7204 | 0.6830 | 0.9563 | |
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| 0.1396 | 4.0 | 220 | 0.1172 | 0.7452 | 0.7599 | 0.7524 | 0.9681 | |
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| 0.1112 | 5.0 | 275 | 0.1102 | 0.7370 | 0.8176 | 0.7752 | 0.9660 | |
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| 0.0892 | 6.0 | 330 | 0.0984 | 0.7994 | 0.7994 | 0.7994 | 0.9713 | |
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| 0.0747 | 7.0 | 385 | 0.1059 | 0.8238 | 0.8100 | 0.8169 | 0.9735 | |
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| 0.0643 | 8.0 | 440 | 0.1112 | 0.7768 | 0.8252 | 0.8003 | 0.9703 | |
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| 0.0533 | 9.0 | 495 | 0.1079 | 0.8361 | 0.8298 | 0.8330 | 0.9748 | |
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| 0.0473 | 10.0 | 550 | 0.1082 | 0.8121 | 0.8343 | 0.8231 | 0.9736 | |
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| 0.0445 | 11.0 | 605 | 0.1094 | 0.8468 | 0.8146 | 0.8304 | 0.9750 | |
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| 0.0375 | 12.0 | 660 | 0.1047 | 0.8477 | 0.8374 | 0.8425 | 0.9762 | |
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| 0.0312 | 13.0 | 715 | 0.1052 | 0.8149 | 0.8298 | 0.8223 | 0.9741 | |
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| 0.0299 | 14.0 | 770 | 0.1095 | 0.8070 | 0.8389 | 0.8227 | 0.9727 | |
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| 0.0269 | 15.0 | 825 | 0.1195 | 0.7874 | 0.8389 | 0.8124 | 0.9718 | |
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| 0.0238 | 16.0 | 880 | 0.1096 | 0.8301 | 0.8389 | 0.8345 | 0.9749 | |
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| 0.0218 | 17.0 | 935 | 0.1134 | 0.8070 | 0.8450 | 0.8255 | 0.9741 | |
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| 0.022 | 18.0 | 990 | 0.1174 | 0.8038 | 0.8404 | 0.8217 | 0.9736 | |
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| 0.02 | 19.0 | 1045 | 0.1189 | 0.8151 | 0.8374 | 0.8261 | 0.9741 | |
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| 0.02 | 20.0 | 1100 | 0.1153 | 0.8168 | 0.8404 | 0.8285 | 0.9743 | |
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### Framework versions |
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- Transformers 4.48.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.2 |
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- Tokenizers 0.21.0 |
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