---
license: mit
language:
- en
tags:
- attention-analysis
- long-context
- modernbert
base_model: answerdotai/ModernBERT-base
---

# Long-Context Attention Regressor (Composite)

Predicts a **composite score** combining multiple attention metrics to identify text that benefits from long context.

## Usage

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

model = AutoModelForSequenceClassification.from_pretrained("KevinDavidHayes/regressor-composite")
tokenizer = AutoTokenizer.from_pretrained("KevinDavidHayes/regressor-composite")

text = "Your text here..."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=8192)

with torch.no_grad():
    score = model(**inputs).logits.item()

# Higher score = text benefits more from long-range attention
```

## Training

- **Base model**: ModernBERT-base (8K context)
- **Target**: Weighted combination: 0.2 * mean_distance + 0.4 * inv_local_ratio + 0.4 * entropy
- **Labels**: Generated using Qwen2.5-7B-Instruct attention analysis at layer 14

## Why Composite?

Cross-context correlation analysis showed:
- mean_distance: r=0.71 (4K→32K)
- local_ratio: r=0.92
- entropy: r=0.92

The composite weights metrics by their cross-context stability.

## Citation

Part of research on attention-based data filtering for long-context pretraining.