Update README.md

README.md

@@ -46,21 +46,7 @@ language:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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-# Personal Identifiable Information (PII Model)
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the generator dataset.
-It achieves the following results:
-
-- Training Loss: 0.003900
-- Validation Loss: 0.051071
-- Precision: 95.53%
-- Recall: 96.60%
-- F1: 96%
-- Accuracy:99.11%
-
-## Model description
-
-Meet our digital safeguard, a savvy token classification model with a knack for spotting personally identifiable information (PII) entities. Trained on the illustrious Bert architecture and fine-tuned on a custom dataset, this model is like a superhero for privacy, swiftly detecting names, addresses, dates of birth, and more. With each token it encounters, it acts as a vigilant guardian, ensuring that sensitive information remains shielded from prying eyes, making the digital realm a safer and more secure place to explore.
 
 ## Model can Detect Following Entity Group
 
@@ -99,42 +85,11 @@ Meet our digital safeguard, a savvy token classification model with a knack for
 
 
 
-### Training hyperparameters
-The following hyperparameters were used during training:
-
-| Hyperparameter               | Value         |
-|------------------------------|---------------|
-| Learning Rate                | 5e-5          |
-| Train Batch Size             | 16            |
-| Eval Batch Size              | 16            |
-| Number of Training Epochs    | 7             |
-| Weight Decay                 | 0.01          |
-| Save Strategy                | Epoch         |
-| Load Best Model at End       | True          |
-| Metric for Best Model        | F1            |
-| Push to Hub                  | True          |
-| Evaluation Strategy          | Epoch         |
-| Early Stopping Patience      | 3             |
-
-
-### Training results
-
-| Epoch | Training Loss | Validation Loss | Precision (%) | Recall (%) | F1 Score (%) | Accuracy (%) |
-|-------|---------------|-----------------|---------------|------------|--------------|--------------|
-| 1     | 0.0443        | 0.038108        | 91.88         | 95.17      | 93.50        | 98.80        |
-| 2     | 0.0318        | 0.035728        | 94.13         | 96.15      | 95.13        | 98.90        |
-| 3     | 0.0209        | 0.032016        | 94.81         | 96.42      | 95.61        | 99.01        |
-| 4     | 0.0154        | 0.040221        | 93.87         | 95.80      | 94.82        | 98.88        |
-| 5     | 0.0084        | 0.048183        | 94.21         | 96.06      | 95.13        | 98.93        |
-| 6     | 0.0037        | 0.052281        | 94.49         | 96.60      | 95.53        | 99.07        |
-
 
 
 
 
 
-### Author
-[email protected]
 
 ### Framework versions