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@@ -59,8 +59,10 @@ This model relies on a modern, custom-built transformer architecture incorporati
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  | **Normalization** | `RMSNorm` | Root Mean Square Normalization |
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  | **Activation** | `SwiGLU` | Advanced FeedForward Network |
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- ---
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  ## 🚀 Quick Start (Google Colab / Local)
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  Since this model uses a custom architecture, it requires downloading the provided python scripts (`config.py`, `model.py`) to run. You can easily test it in Google Colab in under a minute.
 
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  | **Normalization** | `RMSNorm` | Root Mean Square Normalization |
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  | **Activation** | `SwiGLU` | Advanced FeedForward Network |
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+ ## Achieved an impressive 82.11% accuracy and 82.10%F1-score on the GLUE SST-2 validation benchmark,demonstrating highly balanced precision and recall across classes.
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+ ---
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  ## 🚀 Quick Start (Google Colab / Local)
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  Since this model uses a custom architecture, it requires downloading the provided python scripts (`config.py`, `model.py`) to run. You can easily test it in Google Colab in under a minute.