---
language:
  - zh
license: mit
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - structural-isomorphism
  - cross-domain
  - analogy
base_model: shibing624/text2vec-base-chinese
---

# structural-isomorphism-v2 (expanded)

A sentence-transformer model fine-tuned for **structural similarity across scientific domains** — recognizing that phenomena from completely different fields share the same underlying mathematical or dynamical structure.

This is the **V2 model**, trained on the expanded 5689-sample dataset (original SIBD + 4475 expanded phenomena across physics, biology, ecology, finance, engineering). Compared to V1, V2 is significantly more **selective**: it finds fewer cross-domain matches but with much higher precision, making it ideal for strict isomorphism discovery.

## Model Description

- **Base model**: [shibing624/text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese) (BERT-based, 768-dim)
- **Training data**: 5,689 descriptions (1,214 original SIBD + 4,475 from expanded 4,443-phenomenon knowledge base)
- **Training objective**: MultipleNegativesRankingLoss (positive pairs = same structural type, different domain)
- **Hyperparameters**: 5 epochs | batch 16 | lr 2e-5 | warmup 10% | max 500 pairs per type
- **Training time**: ~3.5 hours on Apple M4 (MPS), 10,985 steps

## Evaluation Results

Evaluated on the expanded 4,443-phenomenon test set (1,000 sampled):

| Metric | V1 | **V2** | Delta |
|---|---|---|---|
| Silhouette Score | -0.17 | **0.55** | **+0.72** |
| Retrieval@5 | 23% | **96%** | **+73%** |

On the original 84-type SIBD test set V2 also matches or exceeds V1 performance.

## Discovery Pipeline Results

Running V2 on the expanded 4,443-phenomenon knowledge base with threshold 0.70:

| Step | Count |
|---|---|
| Cross-domain high-similarity pairs | **4,533** |
| LLM strict screening (50 batches) — 5/5 score | **94** |
| LLM strict screening — 4+/5 score (high potential) | **761** (16.8%) |
| Deep analysis of 94 top pairs → **A-level candidate papers** | **19** |

This represents a **75× stricter** retrieval than the V1 model on the same knowledge base (V1 returned 339,913 high-similarity pairs). V2 and V1 discover different structural isomorphisms and are complementary rather than redundant — their top-tier findings have zero overlap.

Top V2 A-level discoveries (deep analysis score):
1. Permafrost methane delayed feedback × Extinction debt (8.6)
2. Semiconductor laser relaxation oscillation × Algorithmic stablecoin anchoring (8.6)
3. Percolation threshold × Technology adoption chasm (8.5)
4. MHC over-dominant selection × Model ensemble (8.5)
5. Extinction debt × ENSO delayed oscillator (8.4)

## Usage

### With sentence-transformers

```python
from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer("qinghuiwan/structural-isomorphism-v2-expanded")

# Encode two descriptions from different domains
emb1 = model.encode("永冻土融化释放甲烷形成的温度-甲烷-温度正反馈循环")
emb2 = model.encode("生境破坏后物种世代反馈滞后引起的灭绝承诺债务")

similarity = util.cos_sim(emb1, emb2).item()
print(f"Structural similarity: {similarity:.3f}")
# Both share delayed-feedback dynamics → high structural similarity
```

### Discovery pipeline

```python
from sentence_transformers import SentenceTransformer, util
import json

model = SentenceTransformer("qinghuiwan/structural-isomorphism-v2-expanded")

# Load phenomenon knowledge base
kb = [json.loads(l) for l in open("kb-expanded.jsonl")]
descs = [p["description"] for p in kb]
emb = model.encode(descs, convert_to_numpy=True, batch_size=64)

# Find cross-domain high-similarity pairs
from itertools import combinations
for i, j in combinations(range(len(kb)), 2):
    if kb[i]["domain"] == kb[j]["domain"]:
        continue
    sim = float(util.cos_sim(emb[i], emb[j]))
    if sim >= 0.70:
        print(f"{sim:.3f}  {kb[i]['name']} × {kb[j]['name']}")
```

## Links

- **Project homepage**: https://structural.bytedance.city
- **GitHub**: https://github.com/dada8899/structural-isomorphism
- **V1 model**: [qinghuiwan/structural-isomorphism-v1](https://huggingface.co/qinghuiwan/structural-isomorphism-v1)
- **Zenodo (v1.1)**: https://doi.org/10.5281/zenodo.19541416

## Citation

```bibtex
@software{structural_isomorphism_v2_2026,
  author = {Wan, Qinghui},
  title  = {Structural Isomorphism Search Engine — V2 Model (Expanded)},
  year   = {2026},
  url    = {https://github.com/dada8899/structural-isomorphism}
}
```

## License

MIT