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Job Description
Analysis Results
Both models analysed the resume against the job description
⭐ WINNER
Baseline Model
TF-IDF + SVM
0%
Score
Combined TF-IDF + SVM score
TF-IDF Cosine Sim
SVM Confidence
Matched Keywords
—
Accuracy
—
F1 Score
70
Train Pairs
✓ Fast inference
✓ Interpretable weights
✗ Keyword matching only
✗ Misses synonyms
⭐ WINNER
Advanced Model
BERT Embeddings
0%
Score
Semantic similarity score
Semantic Similarity
Contextual Depth
Matched Keywords
1B+
Pre-train pairs
384
Embedding dim
MiniLM
Architecture
✓ Understands synonyms
✓ Semantic context
✓ No training data needed
✓ Deeper understanding
📊 Side-by-Side Comparison
| Metric / Property | TF-IDF + SVM | BERT Embeddings |
|---|---|---|
| Match Score | — | — |
| Verdict | — | — |
| Approach | Keyword frequency (TF-IDF) + SVM classifier | Dense semantic embeddings (Transformer) |
| Synonym Handling | ❌ No | ✅ Yes |
| Context Understanding | ❌ Limited | ✅ Deep |
| Training Required | Yes (labelled pairs) | No (pre-trained) |
| Inference Speed | ⚡ Very fast | Moderate (CPU) |
| Model Size | ~few KB | ~90 MB |
| Interpretability | High (feature weights) | Low (black box) |
| Winner | Baseline | 🏆 Better Overall |