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--- |
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license: apache-2.0 |
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base_model: jackaduma/SecBERT |
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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: aptner_secbert |
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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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# aptner_secbert |
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This model is a fine-tuned version of [jackaduma/SecBERT](https://huggingface.co/jackaduma/SecBERT) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3230 |
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- Precision: 0.5124 |
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- Recall: 0.5356 |
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- F1: 0.5237 |
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- Accuracy: 0.9142 |
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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: 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: 10.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.6662 | 0.59 | 500 | 0.3587 | 0.4744 | 0.4743 | 0.4744 | 0.9113 | |
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| 0.3128 | 1.19 | 1000 | 0.3230 | 0.5124 | 0.5356 | 0.5237 | 0.9142 | |
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| 0.2374 | 1.78 | 1500 | 0.3429 | 0.4750 | 0.5714 | 0.5188 | 0.9083 | |
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| 0.1904 | 2.37 | 2000 | 0.3650 | 0.4945 | 0.5598 | 0.5251 | 0.9090 | |
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| 0.1521 | 2.97 | 2500 | 0.3765 | 0.4713 | 0.5783 | 0.5193 | 0.9055 | |
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| 0.1101 | 3.56 | 3000 | 0.4023 | 0.4727 | 0.5744 | 0.5186 | 0.9067 | |
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| 0.1019 | 4.15 | 3500 | 0.4322 | 0.4726 | 0.5571 | 0.5114 | 0.9056 | |
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| 0.0764 | 4.74 | 4000 | 0.4595 | 0.4592 | 0.5897 | 0.5163 | 0.9039 | |
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| 0.0619 | 5.34 | 4500 | 0.4755 | 0.4740 | 0.5783 | 0.5210 | 0.9062 | |
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| 0.059 | 5.93 | 5000 | 0.4514 | 0.5055 | 0.5649 | 0.5335 | 0.9126 | |
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| 0.0429 | 6.52 | 5500 | 0.5036 | 0.474 | 0.5666 | 0.5162 | 0.9065 | |
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| 0.0425 | 7.12 | 6000 | 0.5249 | 0.4767 | 0.5726 | 0.5203 | 0.9064 | |
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| 0.0349 | 7.71 | 6500 | 0.5537 | 0.4634 | 0.5744 | 0.5129 | 0.9038 | |
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| 0.0338 | 8.3 | 7000 | 0.5301 | 0.4839 | 0.5672 | 0.5223 | 0.9089 | |
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| 0.0255 | 8.9 | 7500 | 0.5545 | 0.4731 | 0.5735 | 0.5185 | 0.9059 | |
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| 0.0253 | 9.49 | 8000 | 0.5526 | 0.4789 | 0.5702 | 0.5206 | 0.9074 | |
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### Framework versions |
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- Transformers 4.36.0.dev0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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