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--- |
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library_name: transformers |
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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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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: results |
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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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# results |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0528 |
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- Accuracy: 0.9885 |
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- Precision: 0.9885 |
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- Recall: 0.9885 |
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- F1: 0.9885 |
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- Roc Auc: 0.9992 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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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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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| |
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| 0.1227 | 0.2 | 50 | 0.2116 | 0.935 | 0.9392 | 0.935 | 0.9338 | 0.9937 | |
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| 0.0744 | 0.4 | 100 | 0.0989 | 0.97 | 0.9705 | 0.97 | 0.9698 | 0.9960 | |
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| 0.0715 | 0.6 | 150 | 0.0651 | 0.982 | 0.9820 | 0.982 | 0.9820 | 0.9977 | |
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| 0.1218 | 0.8 | 200 | 0.1539 | 0.9555 | 0.9590 | 0.9555 | 0.9559 | 0.9961 | |
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| 0.0709 | 1.0 | 250 | 0.0528 | 0.9855 | 0.9855 | 0.9855 | 0.9855 | 0.9989 | |
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| 0.0602 | 1.2 | 300 | 0.0986 | 0.978 | 0.9782 | 0.978 | 0.9779 | 0.9986 | |
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| 0.034 | 1.4 | 350 | 0.0687 | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 0.9986 | |
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| 0.0137 | 1.6 | 400 | 0.0613 | 0.9845 | 0.9845 | 0.9845 | 0.9845 | 0.9989 | |
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| 0.047 | 1.8 | 450 | 0.0472 | 0.9895 | 0.9895 | 0.9895 | 0.9895 | 0.9991 | |
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| 0.0617 | 2.0 | 500 | 0.0497 | 0.9885 | 0.9885 | 0.9885 | 0.9885 | 0.9991 | |
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| 0.0513 | 2.2 | 550 | 0.0534 | 0.987 | 0.9870 | 0.987 | 0.9870 | 0.9992 | |
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| 0.0269 | 2.4 | 600 | 0.0467 | 0.9885 | 0.9885 | 0.9885 | 0.9885 | 0.9993 | |
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| 0.001 | 2.6 | 650 | 0.0509 | 0.987 | 0.9870 | 0.987 | 0.9870 | 0.9994 | |
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| 0.0195 | 2.8 | 700 | 0.0521 | 0.9895 | 0.9895 | 0.9895 | 0.9895 | 0.9992 | |
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| 0.0011 | 3.0 | 750 | 0.0528 | 0.9885 | 0.9885 | 0.9885 | 0.9885 | 0.9992 | |
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### Framework versions |
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- Transformers 4.53.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 4.4.1 |
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- Tokenizers 0.21.2 |
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