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--- |
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library_name: transformers |
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license: mit |
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base_model: BRlkl/BingoGuard-bert-large-base-benchmarks |
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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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- f1 |
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model-index: |
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- name: BingoGuard-bert-large-base-plus-custom |
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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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# BingoGuard-bert-large-base-plus-custom |
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This model is a fine-tuned version of [BRlkl/BingoGuard-bert-large-base-benchmarks](https://huggingface.co/BRlkl/BingoGuard-bert-large-base-benchmarks) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6046 |
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- Accuracy: 0.8915 |
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- F1: 0.8884 |
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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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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- optimizer: Use adamw_torch_fused 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: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.3266 | 1.0 | 67 | 0.2604 | 0.8979 | 0.8970 | |
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| 0.2215 | 2.0 | 134 | 0.2803 | 0.8851 | 0.8831 | |
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| 0.096 | 3.0 | 201 | 0.3214 | 0.8894 | 0.8874 | |
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| 0.0439 | 4.0 | 268 | 0.4621 | 0.8915 | 0.8903 | |
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| 0.0324 | 5.0 | 335 | 0.5207 | 0.8936 | 0.8913 | |
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| 0.0119 | 6.0 | 402 | 0.5807 | 0.8915 | 0.8884 | |
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| 0.0076 | 7.0 | 469 | 0.5984 | 0.8936 | 0.8913 | |
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| 0.0032 | 8.0 | 536 | 0.6046 | 0.8915 | 0.8884 | |
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### Framework versions |
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- Transformers 4.55.4 |
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- Pytorch 2.8.0+cu128 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.4 |
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