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--- |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- job-matching |
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- skill-similarity |
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- embeddings |
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- esco |
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--- |
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# 🛠️ alvperez/skill-sim-model |
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**skill-sim-model** is a fine-tuned [Sentence-Transformers](https://www.sbert.net) checkpoint that maps short *skill phrases* (e.g. `Python`, `Forklift operation`, `Electrical wiring`) into a 768‑D vector space where semantically related skills cluster together. |
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Training pairs come from the public **ESCO** taxonomy plus curated *hard negatives* for job‑matching research. |
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| Use‑case | How to leverage the embeddings | |
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|----------|--------------------------------| |
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| Candidate ↔ vacancy matching | `score = cosine(skill_vec, job_vec)` | |
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| Deduplicating skill taxonomies | cluster the vectors | |
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| Recruiter query‑expansion | nearest‑neighbour search | |
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| Exploratory dashboards | feed to t‑SNE / PCA | |
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--- |
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## 🚀 Quick start |
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```bash |
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pip install -U sentence-transformers |
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``` |
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```python |
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from sentence_transformers import SentenceTransformer, util |
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model = SentenceTransformer("alvperez/skill-sim-model") |
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skills = ["Electrical wiring", |
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"Circuit troubleshooting", |
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"Machine learning"] |
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emb = model.encode(skills, convert_to_tensor=True) |
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print(util.pytorch_cos_sim(emb[0], emb)) # similarity matrix |
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``` |
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```python |
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from transformers import pipeline |
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similarity = pipeline("sentence-similarity", |
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model="alvperez/skill-sim-model") |
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similarity("forklift operation", |
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["pallet jack", "python"]) |
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``` |
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--- |
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## 📊 Benchmark |
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| Metric | Value | |
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|--------------------------------|-------| |
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| Spearman correlation | **0.845** | |
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| ROC AUC | **0.988** | |
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| MAP@all (*cold‑start*) | **0.232** | |
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> *cold‑start = the system sees only skill strings, no historical interactions.* |
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--- |
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## ⚙️ Training recipe (brief) |
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* Base: `sentence-transformers/all-mpnet-base-v2` |
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* Loss: `CosineSimilarityLoss` |
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* Epochs × batch: `5 × 32` |
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* LR / warm‑up: `2 e‑5` / `100` steps |
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* Negatives: random + “hard” pairs from ESCO siblings |
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* Hardware: 1 × A100 40 GB (≈ 45 min) |
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Full code in [`/training_scripts`](training_scripts). |
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--- |
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## 🏹 Intended use |
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* **Employment tech** – rank CVs vs. vacancies |
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* **EdTech / reskilling** – detect skill gaps, suggest learning paths |
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* **HR analytics** – normalise noisy skill fields at scale |
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--- |
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## ✋ Limitations & bias |
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* Vocabulary dominated by ESCO (English); niche jargon may project poorly. |
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* No explicit fairness constraints; downstream systems should audit (e.g. *Disparate Impact*). |
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* In our tests, a threshold of 0.65 marks a “definitely related” cut‑off; tune for your own precision‑recall needs. |
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--- |
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## 🔍 Citation |
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```bibtex |
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@misc{alvperez2025skillsim, |
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title = {Skill-Sim: a Sentence-Transformers model for skill similarity and job matching}, |
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author = {Pérez Amado, Álvaro}, |
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howpublished = {\url{https://huggingface.co/alvperez/skill-sim-model}}, |
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year = {2025} |
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} |
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``` |
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--- |
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### Acknowledgements |
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Built on top of Sentence-Transformers and the public **ESCO** dataset. |
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Feedback & PRs welcome! |
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