Updated readme. Base model name corrected.

README.md

@@ -29,7 +29,7 @@ This model achieves top performance on the RAID benchmark at the time of submiss
 
 The model is built upon a fine-tuned **microsoft/deberta-v3-large** transformer architecture. The core components include:
 
-*   **Transformer Base:**  The pre-trained `microsoft/deberta-v3-base` model serves as the foundation. This model utilizes DeBERTa (Decoding-enhanced BERT with disentangled attention), an improved version of BERT and RoBERTa, which incorporates disentangled attention and enhanced mask decoder for better performance.
+*   **Transformer Base:**  The pre-trained `microsoft/deberta-v3-large` model serves as the foundation. This model utilizes DeBERTa (Decoding-enhanced BERT with disentangled attention), an improved version of BERT and RoBERTa, which incorporates disentangled attention and enhanced mask decoder for better performance.
 *   **Mean Pooling:**  A mean pooling layer aggregates the hidden states from the transformer, creating a fixed-size representation of the input text. This method averages the token embeddings, weighted by the attention mask, to capture the overall semantic meaning.
 *   **Classifier Head:** A linear layer acts as a classifier, taking the pooled representation and outputting a single logit. This logit represents the model's confidence that the input text is AI-generated. Sigmoid activation is applied to the logit to produce a probability.