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--- |
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language: |
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- en |
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license: mit |
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base_model: prajjwal1/bert-tiny |
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tags: |
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- pytorch |
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- BertForTokenClassification |
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- bert-tiny |
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- generated_from_trainer |
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- named-entity-recognition |
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model-index: |
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- name: bert-tiny-privacy |
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results: [] |
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datasets: |
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- beki/privy |
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library_name: transformers |
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pipeline_tag: token-classification |
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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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# bert-tiny-privacy |
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This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co./prajjwal1/bert-tiny) on the beki/privy dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0235 |
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## Model description |
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This model can be used to detect personal information traces from JSON, SQL, HTML and XML and can be used as a model for redacting such information. |
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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: 4e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 128 |
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- seed: 13434865 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.01 |
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- training_steps: 15000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 0.1891 | 0.19 | 2500 | 0.1369 | |
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| 0.0869 | 0.38 | 5000 | 0.0503 | |
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| 0.0609 | 0.57 | 7500 | 0.0314 | |
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| 0.0512 | 0.76 | 10000 | 0.0259 | |
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| 0.0493 | 0.95 | 12500 | 0.0240 | |
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| 0.048 | 1.14 | 15000 | 0.0237 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.0 |
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- Tokenizers 0.15.0 |
