---
license: mit
base_model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
library_name: peft
language:
  - en
pipeline_tag: text-classification
tags:
  - entity-matching
  - person-name-matching
  - record-linkage
  - deduplication
  - lora
  - deberta-v3
  - peft
metrics:
  - f1
  - precision
  - recall
  - accuracy
extra_gated_prompt: |-
  Acknowledge the intended use and limitations before downloading.
extra_gated_fields:
  Name: text
  Affiliation: text
  Intended use (one sentence): text
  I have read the Bias, Risks, and Limitations section: checkbox
---

# Person Name Match Likelihood (v6)

> **Author:** Elad Laor  ·  [LinkedIn](https://www.linkedin.com/in/elad-laor-1b1383250/)
>
> A scoring head over [`MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli`](https://huggingface.co/MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli) trained to predict whether two strings refer to the **same person**. Useful for record linkage, deduplication, and KYC-style identity matching where the only signal is a pair of name strings.

## Quick start

```python
from peft import PeftModel
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

BASE = "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli"
ADAPTER = "LessLM/person-name-match-likelihood-v6"

tokenizer = AutoTokenizer.from_pretrained(BASE)
base = AutoModelForSequenceClassification.from_pretrained(BASE, num_labels=2)
model = PeftModel.from_pretrained(base, ADAPTER).eval()

def score(name_a: str, name_b: str) -> float:
    """Return P(same person) in [0, 1]."""
    inputs = tokenizer(name_a, name_b, return_tensors="pt", truncation=True, max_length=128)
    with torch.no_grad():
        logits = model(**inputs).logits
    return torch.softmax(logits, dim=-1)[0, 1].item()

print(score("John A. Smith", "J. Smith"))           # ~0.95 (initial expansion)
print(score("Yitzhak Cohen", "Itzhak Cohen"))       # ~0.99 (transliteration)
print(score("John Smith", "John Smyth"))            # ~0.90 (typo)
print(score("Robert Adams", "Roberta Adams"))       # ~0.05 (similar but different)
```

The model returns a 2-way softmax over `[no_match, match]`. The `match` probability is interpretable as a likelihood score; a temperature scaler (`calibration.pt` in this repo) is fitted on a held-out set if you want calibrated probabilities — load it and apply before the softmax for slightly tighter Expected Calibration Error.

## Headline metrics

Evaluated on a held-out test set of **2,510 name pairs** drawn from real public entity data (OpenSanctions) and a curated synthetic edge-case set.

| Metric | Score |
|---|---|
| F1 | **0.9682** |
| Precision | 0.9568 |
| Recall | 0.9798 |
| Accuracy | 0.9733 |
| Expected Calibration Error | 0.0162 |
| Latency (p95, CPU) | **0.42 ms** |

### Performance by edge case

| Edge case | Accuracy | n |
|---|---|---|
| `nickname` (Bob ↔ Robert) | **100.0%** | 121 |
| `name_order` (Last, First ↔ First Last) | **100.0%** | 112 |
| `transliteration` (Yitzhak ↔ Itzhak) | **100.0%** | 93 |
| `initial` (J. Smith ↔ John Smith) | **100.0%** | 112 |
| `middle_name` add/drop | **100.0%** | 50 |
| `title_suffix` (Dr., Jr.) | **100.0%** | 112 |
| `hyphenation`, `case_variation`, `combined`, `unrelated` | **100.0%** | 86 |
| `tricky_non_match` (similar non-matches) | 97.9% | 331 |
| `partial_overlap` | 97.7% | 353 |
| `unknown` (real-world, no curated label) | 97.5% | 682 |
| `similar_name` (Robert ↔ Roberta) | 95.0% | 341 |
| `typo` | **84.6%** | 117 |

The model is strongest on canonical edge cases (nicknames, initials, transliteration) and weakest on character-level typos where it overlaps with the `similar_name` distribution.

## How it was trained

- **Base:** `MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli` (184M params, 12-layer encoder with disentangled attention).
- **Adapter:** LoRA (rank=16, alpha=32, dropout=0.1), targeting `query_proj`, `key_proj`, `value_proj` in every attention block. ~600K trainable parameters (~0.3% of base).
- **Loss:** Focal loss (γ=2.0) — down-weights easy examples, lets the model focus on hard pairs (similar names, typos).
- **Optimizer:** AdamW, LR=2e-4, cosine schedule, 10% warmup, weight decay 0.01.
- **Schedule:** 10 epochs, batch size 32, max sequence length 128 tokens, BF16 mixed precision.
- **Seed:** 42.
- **Data:** ~174K balanced match/no-match pairs (after 2.5× augmentation) — half drawn from OpenSanctions entities (real public records), half from a synthetic generator covering the 15 edge cases in the table above. Train/validation/calibration splits are entity-level (no person appears in more than one split) using a deterministic MD5-based hash so the splits reproduce bit-for-bit.

## Bias, Risks, and Limitations

- **Latin script only.** The model was trained on Latin-script names. It will not work well on Hebrew, Arabic, Chinese, Cyrillic, etc. scripts unless they are first transliterated.
- **OpenSanctions skew.** The real-world half of the training data is drawn from a public sanctions/PEP entity database. Names in that distribution skew toward political, business, and criminal figures, with heavy representation of Russian, Ukrainian, Iranian, Chinese, and Latin American transliterations and a long tail of titles and honorifics. The model may behave differently on, say, US consumer-database names than on this distribution.
- **Pair-level only.** This is a *pairwise* matcher: given two name strings, score their likelihood of being the same person. It does not do blocking, clustering, or one-to-many matching. For dedup over a large list, pair it with a blocking layer (cheap pre-filter on first-letter, soundex, etc.) before invoking the model.
- **Names alone.** No surrounding context (DOB, email, address). Two real-world people with the same name will score as a match. Use this as one signal among several in a real identity-matching pipeline, not as the sole decision.
- **Typo accuracy is the weakest cell.** 84.6% on character-level typos. If your input is OCR output or hand-transcribed names, expect more errors in this category and consider a separate spell-correction step before scoring.
- **No production guarantees.** This is a research/portfolio artifact. Performance on your distribution may differ. Evaluate on a sample of your own data before relying on it.

## Intended use

- Record linkage and dedup of person-name fields in datasets where you have only name strings to work with.
- KYC and identity-matching workflows as one feature among several.
- Benchmarking and research on encoder-based entity matching.

## Out-of-scope use

- Non-Latin scripts (Hebrew, Arabic, Chinese, etc.) without prior transliteration.
- Surveillance, social scoring, or any use that would single out individuals for adverse treatment based on a name-match score alone.
- High-stakes one-shot identity decisions (eligibility, denial, arrest, eviction) — the model gives a likelihood, not a verdict.

## License

[MIT](https://opensource.org/license/mit/). You are free to use, modify, and redistribute, including commercially, provided you keep the attribution and license notice.

## Citation

If you use this model, a backlink to this repo or the author's profile is appreciated.

```bibtex
@misc{laor2026_person_name_match_v6,
  author = {Elad Laor},
  title  = {Person Name Match Likelihood (v6) — a LoRA adapter on DeBERTa-v3 for pairwise person-name matching},
  year   = {2026},
  url    = {https://huggingface.co/LessLM/person-name-match-likelihood-v6}
}
```

## Reproducibility & technical details

- **Framework versions:** `peft==0.18.1`, `transformers>=4.40`, `torch>=2.0`.
- **Training environment:** RunPod RTX 4090, ~3h wall-clock, BF16. Original v6 trained on RTX 3090 Ti (cross-GPU F1 delta: −0.0025).
- **Seed:** All randomness controlled by seed=42 (numpy, torch, transformers, dataloader generators). Re-running the training script with this seed and dataset version produces F1 within ±0.005 across BF16-capable GPUs.
- **Calibration:** `calibration.pt` is a single-parameter temperature scaler (T=0.95) fitted on a 1.5K held-out set. Apply it to logits before the final softmax to slightly reduce Expected Calibration Error from 0.016 to ~0.012.