README.md

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

# Long-Context Attention Regressor (Entropy)

Predicts the **attention entropy** of a text sample - how spread out vs focused the attention patterns are.

## Usage

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

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

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 = more spread attention (uses more context)
```

## Training

- **Base model**: ModernBERT-base (8K context)
- **Target**: Normalized attention entropy
- **Labels**: Generated using Qwen2.5-7B-Instruct attention analysis at layer 14

## Citation

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