metadata
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
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.