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--- |
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library_name: transformers |
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base_model: FPTAI/velectra-base-discriminator-cased |
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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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model-index: |
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- name: velectra-base_v2 |
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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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# velectra-base_v2 |
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This model is a fine-tuned version of [FPTAI/velectra-base-discriminator-cased](https://huggingface.co/FPTAI/velectra-base-discriminator-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5503 |
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- Accuracy: 0.9242 |
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- Precision Macro: 0.8370 |
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- Recall Macro: 0.7946 |
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- F1 Macro: 0.8125 |
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- F1 Weighted: 0.9222 |
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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: 3e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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- optimizer: Use 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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- 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 | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-----------:| |
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| 0.5452 | 1.0 | 90 | 0.2734 | 0.9071 | 0.8647 | 0.6926 | 0.7190 | 0.8965 | |
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| 0.2546 | 2.0 | 180 | 0.2530 | 0.9198 | 0.8318 | 0.7882 | 0.8059 | 0.9176 | |
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| 0.1788 | 3.0 | 270 | 0.2528 | 0.9223 | 0.8241 | 0.7732 | 0.7929 | 0.9193 | |
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| 0.1323 | 4.0 | 360 | 0.2605 | 0.9261 | 0.8473 | 0.8000 | 0.8197 | 0.9241 | |
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| 0.0901 | 5.0 | 450 | 0.2840 | 0.9305 | 0.8839 | 0.7986 | 0.8303 | 0.9276 | |
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| 0.0682 | 6.0 | 540 | 0.3434 | 0.9210 | 0.8458 | 0.8007 | 0.8197 | 0.9192 | |
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| 0.0482 | 7.0 | 630 | 0.3689 | 0.9191 | 0.7970 | 0.8197 | 0.8073 | 0.9206 | |
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| 0.0443 | 8.0 | 720 | 0.3906 | 0.9223 | 0.8315 | 0.7728 | 0.7952 | 0.9191 | |
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| 0.0275 | 9.0 | 810 | 0.4178 | 0.9210 | 0.8717 | 0.7504 | 0.7861 | 0.9155 | |
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| 0.028 | 10.0 | 900 | 0.4642 | 0.9103 | 0.7837 | 0.7837 | 0.7835 | 0.9103 | |
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| 0.02 | 11.0 | 990 | 0.4823 | 0.9179 | 0.8459 | 0.7694 | 0.7971 | 0.9143 | |
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| 0.0122 | 12.0 | 1080 | 0.5070 | 0.9179 | 0.8594 | 0.7853 | 0.8136 | 0.9151 | |
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| 0.0098 | 13.0 | 1170 | 0.5093 | 0.9248 | 0.8387 | 0.7911 | 0.8106 | 0.9225 | |
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| 0.0108 | 14.0 | 1260 | 0.5309 | 0.9248 | 0.8678 | 0.7783 | 0.8098 | 0.9212 | |
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| 0.0101 | 15.0 | 1350 | 0.5214 | 0.9261 | 0.8623 | 0.7669 | 0.7986 | 0.9216 | |
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| 0.0076 | 16.0 | 1440 | 0.5352 | 0.9242 | 0.8653 | 0.7737 | 0.8054 | 0.9203 | |
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| 0.0042 | 17.0 | 1530 | 0.5533 | 0.9198 | 0.8163 | 0.7870 | 0.8000 | 0.9181 | |
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| 0.0058 | 18.0 | 1620 | 0.5503 | 0.9255 | 0.8574 | 0.7871 | 0.8138 | 0.9225 | |
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| 0.0034 | 19.0 | 1710 | 0.5590 | 0.9248 | 0.8349 | 0.8035 | 0.8173 | 0.9233 | |
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| 0.0029 | 20.0 | 1800 | 0.5503 | 0.9242 | 0.8370 | 0.7946 | 0.8125 | 0.9222 | |
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### Framework versions |
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- Transformers 4.55.0 |
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- Pytorch 2.7.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.21.4 |
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