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--- |
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library_name: transformers |
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license: mit |
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base_model: FacebookAI/roberta-large |
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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: fine_tuned_raid_cleaned |
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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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# fine_tuned_raid_cleaned |
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This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1136 |
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- Accuracy: 0.9800 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:--------:| |
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| 0.3826 | 0.0139 | 100 | 0.1739 | 0.9443 | |
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| 0.2556 | 0.0277 | 200 | 0.2700 | 0.9408 | |
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| 0.3383 | 0.0416 | 300 | 0.1667 | 0.9529 | |
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| 0.3672 | 0.0554 | 400 | 0.9354 | 0.7975 | |
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| 0.2223 | 0.0693 | 500 | 0.1584 | 0.9673 | |
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| 0.2197 | 0.0832 | 600 | 0.6363 | 0.8793 | |
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| 0.2873 | 0.0970 | 700 | 0.2169 | 0.9462 | |
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| 0.2201 | 0.1109 | 800 | 0.1366 | 0.9621 | |
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| 0.1695 | 0.1248 | 900 | 0.2912 | 0.9554 | |
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| 0.1912 | 0.1386 | 1000 | 0.2287 | 0.9542 | |
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| 0.131 | 0.1525 | 1100 | 0.1136 | 0.9800 | |
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| 0.1764 | 0.1663 | 1200 | 0.1770 | 0.9645 | |
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| 0.1195 | 0.1802 | 1300 | 0.1255 | 0.9755 | |
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| 0.09 | 0.1941 | 1400 | 0.1285 | 0.9758 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.0+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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