Update README.md

README.md

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 # Token-Level Stereotype Classifier
 
-The Token-Level Stereotype Classifier is a transformer-based model developed to detect and classify different types of stereotypes present in text at the token level. It is designed to recognize stereotypical and anti-stereotypical stereotypes towards gender, race, profession, and religion. The model can help in developing applications aimed at mitigating stereotypical language use and promoting fairness and inclusivity in natural language processing tasks.
+The Token-Level Stereotype Classifier is a transformer-based model developed to detect and classify different types of stereotypes present in the text at the token level. It is designed to recognize stereotypical and anti-stereotypical stereotypes towards gender, race, profession, and religion. The model can help in developing applications aimed at mitigating stereotypical language use and promoting fairness and inclusivity in natural language processing tasks.
 
 ## Model Architecture
 
-The model is built using the pretrained model. It is fine-tuned on a custom dataset for the task of sentence-level stereotype classification. The model uses a Sentence Classification architecture, typically used for Text Classification tasks.
-
-## Model Performance
-
-| Metric                 | Value                   |
-|------------------------|-------------------------|
-| eval_loss              | 0.03554883599281311     |
-| eval_precision         | 0.7868185694908753      |
-| eval_recall            | 0.7662314481801649      |
-| eval_f1                | 0.7739129932274338      |
-| eval_balanced accuracy | 0.7662314481801649      |
-| eval_runtime           | 4.5554                  |
-| eval_samples_per_second| 1196.818                |
-| eval_steps_per_second  | 74.856                  |
-| epoch                  | 6.0                     |
+The model is built using the pretrained Distilbert model. It is fine-tuned on MGS Dataset for the task of token-level classification.
 
 
 ## Classes