---
license: apache-2.0
library_name: transformers
pipeline_tag: token-classification
tags:
  - token-classification
  - ner
  - named-entity-recognition
  - grt
  - rotational-transformer
  - givens-rotations
  - clifford-algebra
  - matterwave
base_model: MatterWaveAI/grt-30m
---

# GRT-30M-NER (`MatterWaveAI/grt-30m-ner`)

**GRT-30M-NER** (`GRTForTokenClassification`) is a **100% Native Rotational Named Entity Recognition (NER) Model** (`29,307,847` parameters, **zero `nn.Linear` layers, zero `nn.Conv1d` layers**) built on `MatterWaveAI/grt-30m` for CoNLL-2003 9-class BIO entity tagging (`O, B-PER, I-PER, B-ORG, I-ORG, B-LOC, I-LOC, B-MISC, I-MISC`).

Each token's final `GRTPearlNorm` representation is L2-normalized onto the unit hypersphere and scored against 9 learned planar-pair phase-angle prototypes (`phase_angles` of shape `[9, 128]`) and softplus radial gains (`pair_scales` of shape `[9, 128]`) inside `GRTCosineClassificationHead` (`classifier`), calibrated by `output_scale` and per-class `bias`.

---

## 8-Way Classical Geometric Optimizer Ablation (`GRTForTokenClassification`)

All 8 classical geometric optimizers in `transformers.models.grt.optimization_grt` were benchmarked under an identical 200-step adaptation protocol (`seed = 42`, `batch_size = 24`, `backbone_lr = 1.6e-4`) unfreezing the top rotational transformer block `blocks[-1]` (`811,651` parameters), `final_norm` (`128` parameters), and `classifier` (`2,350` parameters) for `814,257` total trainable parameters (`0` `nn.Linear`):

| Rank | Optimizer (`optimization_grt.py`) | Final Train Loss | Val NLL (`val_loss`) | Token Acc (`%`) | Entity Token F1 (`%`) | Entity Span F1 (`%`) | Span Precision (`%`) | Span Recall (`%`) | Wall Time (`200` Steps) |
| :---: | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **1** | **`hessian_pair_adam`** (HessianPairAdam (Sophia-Torus Cauchy-Riemann Curvature)) | `0.0584` | `0.1851` | `95.38%` | `91.29%` | **`69.16%`** | `62.03%` | `78.12%` | `24.74s` |
| **2** | **`polar_natural_adam`** (PolarNaturalAdam (Riemannian Polar Metric + Cauchy-Riemann Hessian)) | `0.0585` | `0.1854` | `95.38%` | `91.29%` | **`69.16%`** | `62.03%` | `78.12%` | `23.07s` |
| **3** | **`pair_adam`** (PairAdam (Factored Toroidal Baseline)) | `0.0587` | `0.1854` | `95.32%` | `91.19%` | **`69.16%`** | `62.03%` | `78.12%` | `25.32s` |
| **4** | **`gyric_adam`** (GyricAdam (Poinsot-Lande Gyroscopic Projection + Thomas Precession)) | `0.0592` | `0.1863` | `95.32%` | `91.19%` | **`69.16%`** | `62.03%` | `78.12%` | `239.15s` |
| **5** | **`symplectic_adam`** (SymplecticAdam (Gromov-Liouville Non-Squeezing Floor + Casimir Cavity)) | `0.0591` | `0.1863` | `95.27%` | `91.08%` | **`68.78%`** | `61.63%` | `77.81%` | `241.02s` |
| **6** | **`huygens_symplectic_adam`** (HuygensSymplecticAdam (Two-Stage Huygens -> Symplectic Curriculum)) | `0.0591` | `0.1863` | `95.27%` | `91.08%` | **`68.78%`** | `61.63%` | `77.81%` | `218.89s` |
| **7** | **`cyclide_adam`** (CyclideAdam (Dupin Cyclide Conic Metric + Deviatoric Twist)) | `0.0585` | `0.1854` | `95.22%` | `90.98%` | **`68.41%`** | `61.23%` | `77.50%` | `75.52s` |
| **8** | **`huygens_adam`** (HuygensAdam (Circular Order Parameter R_p Splay Torque)) | `0.0584` | `0.1853` | `95.22%` | `90.98%` | **`68.04%`** | `60.84%` | `77.19%` | `189.88s` |
| — | **Step-0 Initialized Rotational Head (Before Adaptation)** | — | `0.9752` | `64.88%` | `51.84%` | `20.11%` | `14.44%` | `33.12%` | — |

---

## Quickstart (`transformers`)

```python
import torch
from transformers import AutoTokenizer, GRTForTokenClassification

model_id = "MatterWaveAI/grt-30m-ner"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = GRTForTokenClassification.from_pretrained(model_id)

text = "Lead researcher Elena Rostova from MatterWave AI presented the Apache License 2.0 in San Francisco yesterday."
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
pred_ids = logits.argmax(dim=-1)[0].tolist()
for tok_id, label_id in zip(inputs.input_ids[0].tolist(), pred_ids):
    print(f"{tokenizer.decode([tok_id]):18s} -> {model.config.id2label[label_id]}")
```

---

## License

Apache 2.0