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--- |
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license: mit |
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base_model: roberta-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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- accuracy |
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- recall |
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model-index: |
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- name: study-dictionary-roberta-base |
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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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# study-dictionary-roberta-base |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0011 |
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- F1: 1.0 |
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- Roc Auc: 1.0 |
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- Accuracy: 1.0 |
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- Recall: 1.0 |
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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: 10 |
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- eval_batch_size: 10 |
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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_ratio: 0.1 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | Recall | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|:------:| |
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| 0.3342 | 1.0 | 778 | 0.1192 | 0.0 | 0.5 | 0.0 | 0.0 | |
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| 0.1099 | 2.0 | 1556 | 0.1040 | 0.0 | 0.5 | 0.0 | 0.0 | |
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| 0.0892 | 3.0 | 2334 | 0.0465 | 0.6835 | 0.7644 | 0.5479 | 0.5293 | |
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| 0.0345 | 4.0 | 3112 | 0.0240 | 0.9147 | 0.9241 | 0.8817 | 0.8485 | |
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| 0.025 | 5.0 | 3890 | 0.0152 | 0.9594 | 0.9650 | 0.9493 | 0.9303 | |
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| 0.0144 | 6.0 | 4668 | 0.0114 | 0.9735 | 0.9811 | 0.9671 | 0.9625 | |
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| 0.0118 | 7.0 | 5446 | 0.0082 | 0.9779 | 0.9848 | 0.9717 | 0.9700 | |
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| 0.0081 | 8.0 | 6224 | 0.0057 | 0.9873 | 0.9887 | 0.9839 | 0.9774 | |
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| 0.0065 | 9.0 | 7002 | 0.0052 | 0.9839 | 0.9860 | 0.9848 | 0.9720 | |
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| 0.0054 | 10.0 | 7780 | 0.0039 | 0.9895 | 0.9904 | 0.9888 | 0.9809 | |
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| 0.0041 | 11.0 | 8558 | 0.0030 | 0.9942 | 0.9949 | 0.9925 | 0.9899 | |
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| 0.0036 | 12.0 | 9336 | 0.0026 | 0.9936 | 0.9940 | 0.9942 | 0.9881 | |
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| 0.0027 | 13.0 | 10114 | 0.0023 | 0.9956 | 0.9964 | 0.9958 | 0.9927 | |
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| 0.0023 | 14.0 | 10892 | 0.0018 | 0.9985 | 0.9986 | 0.9972 | 0.9972 | |
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| 0.0021 | 15.0 | 11670 | 0.0017 | 0.9985 | 0.9994 | 0.9974 | 0.9988 | |
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| 0.0018 | 16.0 | 12448 | 0.0015 | 0.9985 | 0.9992 | 0.9979 | 0.9985 | |
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| 0.0014 | 17.0 | 13226 | 0.0012 | 0.9997 | 0.9998 | 0.9994 | 0.9995 | |
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| 0.0013 | 18.0 | 14004 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0012 | 19.0 | 14782 | 0.0010 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0012 | 20.0 | 15560 | 0.0010 | 1.0 | 1.0 | 1.0 | 1.0 | |
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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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