updated README.md

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README.md

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 license: apache-2.0
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+# Email Classifier
+
+This project implements an email classification model that assigns each email to a specific category using SBERT (all-minilm-l6-v2) for text embeddings, followed by a sequential neural network for final classification.
+## Model Description
+- **Architecture:** SBERT (384‑d) → Dense(128, ReLU) → Dense(64, ReLU) → Softmax(3)  
+- **Frameworks:** TensorFlow2.17, sentence‑transformer 
+
+## Training Data & Preprocessing
+- **Emails:** 4954 college emails, manually labeled into `[Academics, Clubs, Internships, Others, Talks]`
+- **Split:** 80% train / 20% test 
+- **Embedding & Labeling:**  
+  1. Each email was embedded with `all‑MiniLM‑L6‑v2` (SBERT).  
+  2. We created a small “prototype” set of example sentences for each category.  
+  3. For every email, we computed cosine similarities between its SBERT embedding and each prototype embedding.  
+  4. The email was assigned to the category whose prototype had the **highest** cosine score (threshold ≥ 0.4).  
+
+## Evaluation
+
+The model was tested on **991** college‑email samples. Below are the per‑class precision, recall, F1‑score and support:
+
+| Class | label       | Support | Precision | Recall | F1‑Score |
+|:-----:|-------------|--------:|----------:|-------:|---------:|
+| 0     | Academics   |     200 |     0.92  |  0.97  |   0.94   |
+| 1     | Clubs       |     236 |     0.94  |  0.96  |   0.95   |
+| 2     | Internships |     143 |     0.95  |  0.98  |   0.97   |
+| 3     | Others      |     200 |     0.95  |  0.83  |   0.89   |
+| 4     | Takls       |     212 |     0.93  |  0.94  |   0.93   |
+
+\
+**Aggregate metrics**
+
+| Metric       | Accuracy | Precision | Recall | F1‑Score |
+|:-------------|---------:|----------:|-------:|---------:|
+| Overall      |     0.94 |       —   |    —   |      —   |
+| Macro avg    |      —   |     0.94  |  0.94  |   0.94   |
+| Weighted avg |      —   |     0.94  |  0.94  |   0.93   |
+
+### Confusion Matrix
+
+![Confusion Matrix](cm.png)
+
+## Usage
+
+```bash
+pip install tensorflow sentence-transformers