| | ---
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| | license: apache-2.0
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| | ---
|
| | # Email Classifier
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| |
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| | 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.
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| | ## Model Description
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| | - **Architecture:** `SBERT (384‑d) → Dense(256, ReLU) → Dropout(0.4) → Dense(128, ReLU) → Dropout(0.4) → Softmax(5)`
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| | - **Frameworks:** TensorFlow2.17, sentence‑transformer
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| |
|
| | ## Training Data & Preprocessing
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| | - **Emails:** 4954 college emails, manually labeled into `[Academics, Clubs, Internships, Others, Talks]`
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| | - **Split:** 80% train / 20% test
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| | - **Embedding & Labeling:**
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| | 1. Each email was embedded with `all‑MiniLM‑L6‑v2` (SBERT).
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| | 2. We created a small “prototype” set of example sentences for each category.
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| | 3. For every email, we computed cosine similarities between its SBERT embedding and each prototype embedding.
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| | 4. The email was assigned to the category whose prototype had the **highest** cosine score (threshold ≥ 0.4).
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| |
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| | ## Evaluation
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| |
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| | The model was tested on **991** college‑email samples. Below are the per‑class precision, recall, F1‑score and support:
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| |
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| | | Class | label | Support | Precision | Recall | F1‑Score |
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| | |:-----:|-------------|--------:|----------:|-------:|---------:|
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| | | 0 | Academics | 200 | 0.92 | 0.97 | 0.94 |
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| | | 1 | Clubs | 236 | 0.94 | 0.96 | 0.95 |
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| | | 2 | Internships | 143 | 0.95 | 0.98 | 0.97 |
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| | | 3 | Others | 200 | 0.95 | 0.83 | 0.89 |
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| | | 4 | Takls | 212 | 0.93 | 0.94 | 0.93 |
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| |
|
| | \
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| | **Aggregate metrics**
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| |
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| | | Metric | Accuracy | Precision | Recall | F1‑Score |
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| | |:-------------|---------:|----------:|-------:|---------:|
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| | | Overall | 0.94 | — | — | — |
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| | | Macro avg | — | 0.94 | 0.94 | 0.94 |
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| | | Weighted avg | — | 0.94 | 0.94 | 0.93 |
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| |
|
| | ### Confusion Matrix
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| |
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| | 
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| |
|
| | ## Usage
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| |
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| | ### 1. Install dependencies
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| | ```bash
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| | pip install tensorflow sentence-transformers huggingface_hub
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| | ```
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| | ### 2. Load the model & embedder
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| | ``` python
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| | from sentence_transformers import SentenceTransformer
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| | import tensorflow as tf
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| | from huggingface_hub import hf_hub_download
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| |
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| | # 1) Load SBERT embedder
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| | embedder = SentenceTransformer("all-MiniLM-L6-v2")
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| |
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| | # 2) Load your fine‑tuned classifier
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| | model_file = hf_hub_download(
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| | repo_id="skgezhil2005/email_classifier",
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| | filename="model_v2.keras" #replace with your model file
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| | )
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| | model = tf.keras.models.load_model(model_file)
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| |
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| | # 3) Define label names (in the same order used during training)
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| | labels = ["Academics", "Clubs", "Internships", "Others", "Talks"]
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| | ```
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| | ### 3. Inference Helper
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| |
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| | ``` python
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| | def classify_email(text: str) -> str:
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| | # Compute a 1×384 SBERT embedding
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| | emb = embedder.encode(text, convert_to_tensor=False)
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| | emb = emb.reshape(1, -1)
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| | # Predict probabilities and pick the highest‐scoring class
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| | prediction = model.predict(emb)
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| | pred_idx = int(np.argmax(prediction[0]))
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| |
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| | return labels[pred_idx]
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| |
|
| | ``` |
