---
license: mit
library_name: transformers
pipeline_tag: text-classification
base_model: microsoft/deberta-v3-large
language:
- en
tags:
- cross-encoder
- reward-model
- forecasting
---

# REM matcher

A DeBERTa-v3-large cross-encoder that decides whether a predicted move by a named actor is the same
response-equivalent move (REM) as a realized one. It is the learned matcher used as the reinforcement
learning reward in *Toward Simulating Real-World Actors: Training Small Language Models to Predict
Their Next Moves*.

## Input

The model reads a text pair. Each side is the actor's name followed by a move:

```
Actor context: {actor}
Action: {move}
```

During training, actors with known aliases were written as `{actor}; aliases: {alias1}; {alias2}`.
The predicted move goes first and the realized (target) move second. Pairs are truncated to 512 tokens.
Labels are `no` (0) and `yes` (1).

## Calibrated probability

Raw softmax scores are not calibrated. Use the Platt scaling in `calibration.json` on the logit margin:

```
p_match = sigmoid((logit_yes - logit_no) / temperature + bias)
```

```python
import json, torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForSequenceClassification, AutoTokenizer

repo = "HiddenSystemsAI/rem-matcher"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSequenceClassification.from_pretrained(repo).eval()
cal = json.load(open(hf_hub_download(repo, "calibration.json")))

def side(actor, move):
    return f"Actor context: {actor}\nAction: {move}"

actor = "Bitget"
pairs = [
    (side(actor, "Bitget resumes withdrawals"), side(actor, "Bitget reopens withdrawals for users")),
    (side(actor, "Bitget offers a bounty for the stolen funds"), side(actor, "Bitget reopens withdrawals for users")),
]
batch = tokenizer([a for a, _ in pairs], [b for _, b in pairs], padding=True,
                  truncation=True, max_length=512, return_tensors="pt")
with torch.inference_mode():
    logits = model(**batch).logits.float()
p = torch.sigmoid((logits[:, 1] - logits[:, 0]) / cal["temperature"] + cal["bias"])
print(p.tolist())
```

In the paper's reward a predicted move credits a target when `p_match >= 0.75`, and two predicted moves
count as duplicates when `p_match >= 0.8`.

## Training

The model was initialized from an earlier ordinal matcher of the same architecture, with a new two-class
head trained by cross-entropy for one epoch (learning rate 1e-5, effective batch 32). All pair labels
come from Claude models (Sonnet labels, a blind second pass on borderline pairs, Opus adjudication).
Splits are disjoint by scenario; the test split holds only real pairs of model-predicted and realized
moves from held-out stories.

| Split | Pairs | Positives | Scenarios |
|---|---:|---:|---:|
| Train | 81,439 | 43,904 | 397 |
| Development | 7,148 | 3,709 | 37 |
| Calibration | 7,937 | 4,367 | 45 |
| Test (real pairs) | 4,048 | 667 | 54 |

## Evaluation

On the 4,048 held-out real test pairs, with calibrated probabilities at a 0.5 threshold:
accuracy 94.4%, precision 83.4%, recall 82.6%, expected calibration error 0.017.
The paper reports precision and recall at the 0.75 training gate and compares the matcher's credits
with a three-judge panel on the benchmark lists.

## Limitations

The matcher was trained and evaluated on news-derived moves of governments, companies and other public
actors, written at the REM level. It undercounts matches that human-style judges accept (it misses
about a fifth to a third of judged matches on the paper's benchmark), and optimizing a policy against
it can raise its score faster than judged recall. Use it as a training signal or for grouping moves,
not as an evaluation metric on its own.