---
language:
- en
license: mit
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- recruitment
- job-description
- applai
base_model: sentence-transformers/all-mpnet-base-v2
pipeline_tag: sentence-similarity
---

# AppAI — SBERT 4-Way Pairing

Fine-tuned `sentence-transformers/all-mpnet-base-v2` for job description (JD) embedding generation in the [AppAI](https://github.com/jaimeemanuellucero/applai) recruitment matching pipeline.

The model encodes job descriptions into four **768-dim L2-normalised** span embeddings — **full**, **education**, **experience**, and **leadership** — enabling granular, section-level candidate matching when paired with the LayoutLMv3 resume encoder and the ConFiT alignment model.

---

## Model Details

| Property | Value |
|---|---|
| Base model | `sentence-transformers/all-mpnet-base-v2` |
| Max sequence length | 384 tokens |
| Embedding dimension | 768 |
| Normalisation | L2 (unit norm) |
| Training objective | 4-way paired cosine similarity |
| Batch size | 64 |

### Architecture

```
Input text → MPNet tokenizer → all-mpnet-base-v2 → Mean Pooling → L2 Normalize → 768-dim embedding
```

Training pairs each JD span against its corresponding resume span across four named features. Each feature is encoded independently, giving fine-grained control over education, experience, and leadership matching in addition to the full-text representation.

---

## Intended Use

This model is part of the **AppAI** recruitment intelligence pipeline:

1. **This model** — encodes JD text spans (SBERT)
2. [`Smutypi3/applai-layoutlmv3`](https://huggingface.co/Smutypi3/applai-layoutlmv3) — encodes resume PDF spans (LayoutLMv3)
3. [`Smutypi3/applai-confit`](https://huggingface.co/Smutypi3/applai-confit) — aligns both embedding spaces (ConFiT)

It is intended for **cosine similarity-based candidate ranking** within the AppAI system. It is not designed for general-purpose semantic search.

---

## Usage

### Installation

```bash
pip install sentence-transformers
```

### Encoding a Job Description

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Smutypi3/applai-sbert")
model.max_seq_length = 384

jd_text = "We are looking for a senior software engineer with 5+ years of Python experience..."
embedding = model.encode(jd_text, convert_to_tensor=True, normalize_embeddings=True)
print(embedding.shape)  # torch.Size([768])
```

### Full Pipeline (via AppAI inference service)

```python
from ai_models.preprocessing.jd_preprocessor import preprocess_jd
from ai_models.services.sbert_service import encode_jd_spans

spans = preprocess_jd(raw_jd_html)
# {"full": "...", "education": "...", "experience": "...", "leadership": "..."}

embeddings = encode_jd_spans(spans)
# {"full": [...], "education": [...], "experience": [...], "leadership": [...]}
```

---

## Training Details

### Preprocessing

Job descriptions are preprocessed before encoding to exactly mirror the training pipeline:

- HTML is stripped and entities are unescaped
- **Full span**: `clean_text(preserve_tech=True)` — keeps `.`, `+`, `#` for C++, C#, .NET
- **Section spans**: `clean_text(preserve_tech=False)` + keyword-based sentence filtering
- **Fallback**: `"no specific information available"` when extracted span is ≤ 10 characters

### Training Objective

4-way pairing loss over (JD full, JD education, JD experience, JD leadership) vs (resume full, resume education, resume experience, resume leadership) pairs using cosine similarity.

---

## Limitations

- Designed for English-language job descriptions
- Section extraction relies on keyword matching — niche or unconventional JD formats may not extract cleanly
- Best used together with `Smutypi3/applai-layoutlmv3` and `Smutypi3/applai-confit`; standalone cosine similarity without alignment will not reflect trained performance

---

## Citation

```bibtex
@software{lucero2025applai_sbert,
  author    = {Lucero, Jaime Emmanuel},
  title     = {{AppAI SBERT 4-Way Pairing}: Fine-tuned Sentence Transformer for Job Description Embedding},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/Smutypi3/applai-sbert},
  note      = {Part of the AppAI recruitment intelligence pipeline}
}
```

---

## License

MIT