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--- |
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language: en |
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license: mit |
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tags: |
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- text-classification |
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- text-regression |
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- readability |
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- education |
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- grade-level |
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- modernbert |
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library: transformers |
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widget: |
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- text: >- |
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The sun rises in the east and sets in the west. This is a simple fact that |
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most people learn as children. |
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example_title: Elementary Text |
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- text: >- |
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The quantum mechanical model of atomic structure provides a theoretical |
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framework for understanding the behavior of electrons in atoms. |
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example_title: High School Text |
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base_model: answerdotai/ModernBERT-base |
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pipeline_tag: text-classification |
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--- |
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# Text Readability Grade Predictor |
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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. |
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## Model Details |
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- **Model Type:** ModernBERT fine-tuned for regression |
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- **Language:** English |
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- **Task:** Text Readability Assessment (Regression) |
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- **Framework:** PyTorch |
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- **Base Model:** `answerdotai/ModernBERT-base` |
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- **Training Data:** [CLEAR dataset](https://github.com/scrosseye/CLEAR-Corpus) |
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- **Performance:** |
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- RMSE: 1.4143198236928092 |
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- R²: 0.8125544567620288 |
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- **Output:** Predicted grade level (0-12) |
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## Usage |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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import torch |
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# Load model and tokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("kiddom/modernbert-readability-grade-predictor") |
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tokenizer = AutoTokenizer.from_pretrained("kiddom/modernbert-readability-grade-predictor") |
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# Prepare text |
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text = "Your text goes here." |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512) |
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# Run inference |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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# Get prediction (ensure it's between 0 and 12) |
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pred_grade = outputs.logits.item() |
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pred_grade = max(0, min(pred_grade, 12.0)) |
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print(f"Predicted grade level: {pred_grade:.1f}") |
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``` |
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## Reading Level Categories |
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The predicted grade levels correspond to these educational categories: |
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- **< 1.0:** Pre-Kindergarten |
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- **1.0 - 2.9:** Early Elementary |
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- **3.0 - 5.9:** Elementary |
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- **6.0 - 8.9:** Middle School |
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- **9.0 - 11.9:** High School |
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- **12.0+:** College Level |
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## Example Predictions |
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### Example: Early Elementary |
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``` |
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The cat sat on the mat. It was happy. The sun was shining. |
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``` |
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**Predicted Grade Level:** 1.2 |
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### Example: Middle School |
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``` |
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The water cycle is a continuous process that includes evaporation, condensation, and precipitation. ... |
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``` |
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**Predicted Grade Level:** 8.9 |
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### Example: High School |
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``` |
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The quantum mechanical model of atomic structure provides a theoretical framework for understanding ... |
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``` |
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**Predicted Grade Level:** 11.6 |
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## Limitations |
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- The model is trained on English text only |
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- Performance may vary for specialized or technical content |
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- Very short texts (fewer than 10 words) may not yield accurate predictions |
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- The model is calibrated for US educational grade levels |
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## Training |
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This model was fine-tuned on a custom dataset created by augmenting texts from various grade levels. The training process involved: |
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1. Collecting texts with known Lexile measures and Flesch-Kincaid Grade Levels |
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2. Augmenting the dataset through text chunking |
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3. Averaging grade level metrics for a more reliable target |
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4. Fine-tuning ModernBERT with a regression head |
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5. Optimizing for minimum RMSE and maximum R² |
