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This model is a fine-tuned version of microsoft/deberta-v3-base on the ai4privacy/pii-masking-200k dataset. This model has been finetuned on the World's largest open source privacy dataset. The purpose of the trained models is to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs.

Text analysis
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This model is a fine-tuned version of distilbert-base-german-cased on the German subset of pii-masking-200k dataset. It achieves the following results on the evaluation set: - Loss: 0.0821 - Overall Precision: 0.9086 - Overall Recall: 0.9379 - Overall F1: 0.9230 - Overall Accuracy: 0.9679 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 5

Text analysis
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This model is a fine-tuned version of distilbert-base-uncased on the English Subset of ai4privacy/pii-masking-200k dataset. The purpose of the trained models is to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs. The example texts have 54 PII classes (types of sensitive data), targeting 229 discussion subjects / use cases split across business, education, psychology and legal fields, and 5 interactions styles (e.g. casual conversation, formal document, emails etc...).

Text analysis
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