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--- |
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license: apache-2.0 |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- semantic-search |
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- feature-extraction |
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- sentence-similarity |
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- cybersecurity |
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pipeline_tag: sentence-similarity |
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--- |
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# MrZaper/LiteModel |
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**MrZaper/LiteModel** is a lightweight [sentence-transformers](https://www.SBERT.net) model fine-tuned for **semantic search and retrieval of academic articles in cybersecurity**. |
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It maps queries and article phrases into a 384-dimensional dense vector space for similarity search, clustering, and semantic matching. |
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This model is specifically trained for the **journal: Cybersecurity: Education, Science, Technology** |
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Website: [https://csecurity.kubg.edu.ua](https://csecurity.kubg.edu.ua/index.php/journal) |
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# What does it do? |
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Given a query in **English, Ukrainian, or any other language**, the model: |
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- Translates the query to English (using Google Translate). |
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- Encodes the query into a dense embedding using Sentence-BERT. |
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- Computes cosine similarity between the query embedding and **precomputed article embeddings**. |
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- Returns the top **unique article codes** with highest similarity scores. |
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Returned article codes can be viewed at: |
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``` |
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https://csecurity.kubg.edu.ua/index.php/journal/article/view/{CODE} |
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``` |
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For example: |
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`560` → [https://csecurity.kubg.edu.ua/index.php/journal/article/view/560](https://csecurity.kubg.edu.ua/index.php/journal/article/view/560) |
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--- |
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# Model Files |
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The repository includes: |
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- `LiteModel` – SBERT-based semantic encoder |
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- `sbert_embeddings.npy` – Precomputed embeddings for articles |
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- `sbert_labels.pkl` – Corresponding article codes (e.g., `560`, `532`) |
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--- |
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# Usage (Sentence-Transformers) |
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Install the required package: |
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```bash |
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pip install -U sentence-transformers deep-translator huggingface-hub scikit-learn |
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``` |
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Example usage: |
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```python |
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from sentence_transformers import SentenceTransformer |
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import numpy as np |
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import pickle |
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from huggingface_hub import snapshot_download |
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from deep_translator import GoogleTranslator |
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import os |
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from sklearn.metrics.pairwise import cosine_similarity |
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# Load model and data from Hugging Face |
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model_name = 'MrZaper/LiteModel' |
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model_dir = snapshot_download(repo_id=model_name) |
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# Load SBERT model |
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sbert_model = SentenceTransformer(model_dir) |
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# Load precomputed article embeddings |
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embeddings = np.load(os.path.join(model_dir, "sbert_embeddings.npy")) |
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# Load article codes (labels) |
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with open(os.path.join(model_dir, "sbert_labels.pkl"), 'rb') as f: |
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labels = pickle.load(f) |
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def preprocess_query(query: str) -> str: |
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"""Translate the query to English using Google Translate.""" |
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try: |
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return GoogleTranslator(source="auto", target="en").translate(query) |
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except Exception as e: |
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print(f"Translation error: {e}") |
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return query |
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def predict_semantic(query, model, embeddings, labels, top_n=5): |
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"""Find top-N most semantically similar unique article codes.""" |
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query_emb = model.encode([preprocess_query(query)]) |
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similarities = cosine_similarity(query_emb, embeddings)[0] |
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seen_keys = set() |
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results = [] |
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# Sort results by similarity (descending) |
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sorted_indices = np.argsort(similarities)[::-1] |
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for idx in sorted_indices: |
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label = labels[idx] |
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sim = similarities[idx] |
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if label not in seen_keys: |
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seen_keys.add(label) |
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results.append({ |
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"article_code": label, |
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"similarity": float(sim) |
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}) |
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print(f"📄 Article {label} – similarity: {sim * 100:.2f}%") |
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if len(results) >= top_n: |
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break |
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return results |
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# Example query |
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query = "sql injection in websites" |
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results = predict_semantic(query, sbert_model, embeddings, labels) |
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print("\nTop article codes:") |
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for res in results: |
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print(f"Article {res['article_code']} – similarity: {res['similarity']*100:.2f}%") |
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``` |
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# Example Output |
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📄 Article 560 – similarity: 92.15% |
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📄 Article 532 – similarity: 89.34% |
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📄 Article 475 – similarity: 85.22% |
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Corresponding links: |
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```bach |
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https://csecurity.kubg.edu.ua/index.php/journal/article/view/560 |
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https://csecurity.kubg.edu.ua/index.php/journal/article/view/532 |
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https://csecurity.kubg.edu.ua/index.php/journal/article/view/475 |
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``` |
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