Update README.md

README.md

@@ -14,7 +14,19 @@ library_name: transformers
 This implementation leverages **BERT (Bidirectional Encoder Representations from Transformers)** for binary classification (Spam / Ham) using sequence classification. The model uses the **`prithivMLmods/Spam-Text-Detect-Analysis` dataset** and integrates **Weights & Biases (wandb)** for comprehensive experiment tracking.
 
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+### Summary of Uploaded Files:
+
+| **File Name**                     | **Size**  | **Description**                                     | **Upload Status** |
+|------------------------------------|-----------|-----------------------------------------------------|-------------------|
+| `.gitattributes`                   | 1.52 kB   | Tracks files stored with Git LFS.                  | Uploaded          |
+| `README.md`                        | 8.78 kB   | Comprehensive documentation for the repository.    | Updated           |
+| `config.json`                      | 727 Bytes | Configuration file related to the model settings.   | Uploaded          |
+| `model.safetensors`                | 438 MB   | Model weights stored in safetensors format.        | Uploaded (LFS)    |
+| `special_tokens_map.json`          | 125 Bytes | Mapping of special tokens for tokenizer handling.  | Uploaded          |
+| `tokenizer_config.json`            | 1.24 kB   | Tokenizer settings for initialization.              | Uploaded          |
+| `vocab.txt`                         | 232 kB   | Vocabulary file for tokenizer use.                 | Uploaded          |
 
+---
 ## **🛠️ Overview**
 
 ### **Core Details:**