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--- |
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license: mit |
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base_model: beomi/KcELECTRA-base-v2022 |
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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: KcELECTRA-base-v2022-KEmoFact-0925 |
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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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# KcELECTRA-base-v2022-KEmoFact-0925 |
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This model is a fine-tuned version of [beomi/KcELECTRA-base-v2022](https://huggingface.co/beomi/KcELECTRA-base-v2022) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3810 |
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- Precision: 0.2344 |
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- Recall: 0.2824 |
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- F1: 0.2562 |
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- Accuracy: 0.7161 |
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- Jaccard Scores: 0.6958 |
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- Cls Accuracy: 0.6348 |
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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: 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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Jaccard Scores | Cls Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:--------------:|:------------:| |
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| No log | 1.0 | 414 | 1.0972 | 0.2070 | 0.2510 | 0.2269 | 0.7237 | 0.6675 | 0.6273 | |
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| 0.4703 | 2.0 | 828 | 1.1390 | 0.2038 | 0.2510 | 0.2249 | 0.7180 | 0.6755 | 0.6195 | |
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| 0.3437 | 3.0 | 1242 | 1.2184 | 0.1977 | 0.2510 | 0.2212 | 0.7121 | 0.6756 | 0.6340 | |
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| 0.2478 | 4.0 | 1656 | 1.2727 | 0.2035 | 0.2572 | 0.2272 | 0.7167 | 0.6779 | 0.6407 | |
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| 0.1769 | 5.0 | 2070 | 1.3348 | 0.2098 | 0.2572 | 0.2311 | 0.7209 | 0.6772 | 0.6407 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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