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--- |
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license: mit |
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base_model: microsoft/deberta-v3-base |
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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: pii-ner |
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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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# pii-ner |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5816 |
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- Precision: 0.3789 |
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- Recall: 0.2054 |
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- F1: 0.2664 |
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- Accuracy: 0.9580 |
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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: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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: 0.1 |
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- num_epochs: 30 |
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- mixed_precision_training: Native AMP |
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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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| 1.3048 | 1.0 | 14 | 0.7370 | 0.0088 | 0.0438 | 0.0147 | 0.7632 | |
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| 0.7328 | 2.0 | 28 | 0.2553 | 0.0 | 0.0 | 0.0 | 0.9350 | |
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| 0.2676 | 3.0 | 42 | 0.2095 | 0.7353 | 0.1684 | 0.2740 | 0.9658 | |
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| 0.0976 | 4.0 | 56 | 0.2309 | 0.9091 | 0.2020 | 0.3306 | 0.9683 | |
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| 0.0664 | 5.0 | 70 | 0.2471 | 0.9242 | 0.2054 | 0.3361 | 0.9685 | |
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| 0.1703 | 6.0 | 84 | 0.2893 | 0.9091 | 0.2020 | 0.3306 | 0.9683 | |
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| 0.1196 | 7.0 | 98 | 0.2559 | 0.8841 | 0.2054 | 0.3333 | 0.9683 | |
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| 0.1805 | 8.0 | 112 | 0.2691 | 0.9091 | 0.2020 | 0.3306 | 0.9683 | |
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| 0.1669 | 9.0 | 126 | 0.2651 | 0.9242 | 0.2054 | 0.3361 | 0.9685 | |
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| 0.2296 | 10.0 | 140 | 0.3022 | 0.9242 | 0.2054 | 0.3361 | 0.9685 | |
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| 0.1731 | 11.0 | 154 | 0.2879 | 0.9091 | 0.2020 | 0.3306 | 0.9682 | |
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| 0.1983 | 12.0 | 168 | 0.2824 | 0.8824 | 0.2020 | 0.3288 | 0.9682 | |
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| 0.0873 | 13.0 | 182 | 0.3495 | 0.9091 | 0.2020 | 0.3306 | 0.9683 | |
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| 0.4 | 14.0 | 196 | 0.3905 | 0.9104 | 0.2054 | 0.3352 | 0.9683 | |
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| 0.2142 | 15.0 | 210 | 0.4198 | 0.9104 | 0.2054 | 0.3352 | 0.9683 | |
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| 0.2092 | 16.0 | 224 | 0.4395 | 0.9091 | 0.2020 | 0.3306 | 0.9682 | |
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| 0.0803 | 17.0 | 238 | 0.4003 | 0.9104 | 0.2054 | 0.3352 | 0.9682 | |
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| 0.1509 | 18.0 | 252 | 0.4904 | 0.9104 | 0.2054 | 0.3352 | 0.9683 | |
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| 0.1382 | 19.0 | 266 | 0.5018 | 0.9091 | 0.2020 | 0.3306 | 0.9682 | |
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| 0.0554 | 20.0 | 280 | 0.5128 | 0.9104 | 0.2054 | 0.3352 | 0.9682 | |
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| 0.0595 | 21.0 | 294 | 0.5363 | 0.9104 | 0.2054 | 0.3352 | 0.9682 | |
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| 0.0339 | 22.0 | 308 | 0.5518 | 0.9104 | 0.2054 | 0.3352 | 0.9682 | |
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| 0.0292 | 23.0 | 322 | 0.5613 | 0.8133 | 0.2054 | 0.3280 | 0.9674 | |
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| 0.0368 | 24.0 | 336 | 0.5555 | 0.9104 | 0.2054 | 0.3352 | 0.9682 | |
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| 0.0224 | 25.0 | 350 | 0.5709 | 0.3885 | 0.2054 | 0.2687 | 0.9585 | |
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| 0.0122 | 26.0 | 364 | 0.5753 | 0.3789 | 0.2054 | 0.2664 | 0.9580 | |
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| 0.0332 | 27.0 | 378 | 0.5800 | 0.3789 | 0.2054 | 0.2664 | 0.9580 | |
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| 0.02 | 28.0 | 392 | 0.5810 | 0.3789 | 0.2054 | 0.2664 | 0.9580 | |
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| 0.0155 | 29.0 | 406 | 0.5816 | 0.3789 | 0.2054 | 0.2664 | 0.9580 | |
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| 0.0157 | 30.0 | 420 | 0.5816 | 0.3789 | 0.2054 | 0.2664 | 0.9580 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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