Text Readability Grade Predictor

This model predicts the reading grade level of text using ModernBERT, trained on a dataset of texts with grade-level annotations. It can be used to estimate the educational reading level of various texts, from elementary school to college level.

Model Details

• Model Type: ModernBERT fine-tuned for regression
• Language: English
• Task: Text Readability Assessment (Regression)
• Framework: PyTorch
• Base Model: answerdotai/ModernBERT-base
• Training Data: CLEAR dataset
• Performance:
  • RMSE: 1.4143198236928092
  • R²: 0.8125544567620288
• Output: Predicted grade level (0-12)

Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("kiddom/modernbert-readability-grade-predictor")
tokenizer = AutoTokenizer.from_pretrained("kiddom/modernbert-readability-grade-predictor")

# Prepare text
text = "Your text goes here."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

# Run inference
with torch.no_grad():
    outputs = model(**inputs)

# Get prediction (ensure it's between 0 and 12)
pred_grade = outputs.logits.item()
pred_grade = max(0, min(pred_grade, 12.0))
print(f"Predicted grade level: {pred_grade:.1f}")

Reading Level Categories

The predicted grade levels correspond to these educational categories:

• < 1.0: Pre-Kindergarten
• 1.0 - 2.9: Early Elementary
• 3.0 - 5.9: Elementary
• 6.0 - 8.9: Middle School
• 9.0 - 11.9: High School
• 12.0+: College Level

Example Predictions

Example: Early Elementary

The cat sat on the mat. It was happy. The sun was shining.

Predicted Grade Level: 1.2

Example: Middle School

The water cycle is a continuous process that includes evaporation, condensation, and precipitation. ...

Predicted Grade Level: 8.9

Example: High School

The quantum mechanical model of atomic structure provides a theoretical framework for understanding ...

Predicted Grade Level: 11.6

Limitations

• The model is trained on English text only
• Performance may vary for specialized or technical content
• Very short texts (fewer than 10 words) may not yield accurate predictions
• The model is calibrated for US educational grade levels

Training

This model was fine-tuned on a custom dataset created by augmenting texts from various grade levels. The training process involved:

1. Collecting texts with known Lexile measures and Flesch-Kincaid Grade Levels
2. Augmenting the dataset through text chunking
3. Averaging grade level metrics for a more reliable target
4. Fine-tuning ModernBERT with a regression head
5. Optimizing for minimum RMSE and maximum R²