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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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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: piidetection |
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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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# piidetection |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0010 |
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- Precision: 0.7422 |
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- Recall: 0.8051 |
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- F1: 0.7724 |
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- Accuracy: 0.9998 |
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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: 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 |
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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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| No log | 1.0 | 171 | 0.0044 | 0.0 | 0.0 | 0.0 | 0.9993 | |
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| No log | 2.0 | 342 | 0.0018 | 0.4084 | 0.3305 | 0.3653 | 0.9996 | |
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| 0.0316 | 3.0 | 513 | 0.0013 | 0.7661 | 0.5551 | 0.6437 | 0.9997 | |
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| 0.0316 | 4.0 | 684 | 0.0010 | 0.7258 | 0.7627 | 0.7438 | 0.9998 | |
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| 0.0316 | 5.0 | 855 | 0.0010 | 0.7991 | 0.7585 | 0.7783 | 0.9998 | |
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| 0.0008 | 6.0 | 1026 | 0.0009 | 0.7317 | 0.7627 | 0.7469 | 0.9998 | |
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| 0.0008 | 7.0 | 1197 | 0.0010 | 0.7449 | 0.7669 | 0.7557 | 0.9998 | |
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| 0.0008 | 8.0 | 1368 | 0.0011 | 0.7965 | 0.7627 | 0.7792 | 0.9998 | |
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| 0.0004 | 9.0 | 1539 | 0.0010 | 0.7520 | 0.7839 | 0.7676 | 0.9998 | |
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| 0.0004 | 10.0 | 1710 | 0.0010 | 0.7422 | 0.8051 | 0.7724 | 0.9998 | |
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### Framework versions |
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- Transformers 4.37.0 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.1 |
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