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README.md
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---
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library_name: transformers
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license: mit
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base_model: microsoft/deberta-v3-large
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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: deberta-pii-masking-augmented-test2
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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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# deberta-pii-masking-augmented-test2
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0248
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- Precision: 0.9565
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- Recall: 0.9663
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- F1: 0.9613
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- Accuracy: 0.9919
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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: 16
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- eval_batch_size: 16
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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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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 1
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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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| 0.5574 | 0.16 | 1000 | 0.0750 | 0.8633 | 0.9081 | 0.8851 | 0.9774 |
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| 0.0572 | 0.32 | 2000 | 0.0455 | 0.9151 | 0.9290 | 0.9220 | 0.9857 |
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| 0.0401 | 0.48 | 3000 | 0.0395 | 0.9294 | 0.9452 | 0.9372 | 0.9873 |
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| 0.0319 | 0.64 | 4000 | 0.0301 | 0.9443 | 0.9548 | 0.9496 | 0.9902 |
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| 0.0277 | 0.8 | 5000 | 0.0264 | 0.9503 | 0.9618 | 0.9560 | 0.9912 |
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| 0.0231 | 0.96 | 6000 | 0.0249 | 0.9538 | 0.9652 | 0.9595 | 0.9920 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.5.0+cu121
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- Datasets 3.1.0
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- Tokenizers 0.19.1
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