---
library_name: transformers
license: mit
base_model: agentlans/deberta-v3-xsmall-zyda-2
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-xsmall-zyda-2-transformed-readability-new
  results: []
---

# deberta-v3-xsmall-zyda-2-transformed-readability-new

## Model Overview

This model is a fine-tuned version of [agentlans/deberta-v3-xsmall-zyda-2](https://huggingface.co/agentlans/deberta-v3-xsmall-zyda-2) designed to predict text readability. It achieves the following results on the evaluation set:
- Loss: 0.0273
- MSE: 0.0273

## Dataset Description

The [dataset used for training](https://huggingface.co/datasets/agentlans/readability) comprises approximately 800 000 paragraphs with corresponding readability metrics from four diverse sources:

1. HuggingFace's Fineweb-Edu
2. Ronen Eldan's TinyStories
3. Wikipedia-2023-11-embed-multilingual-v3 (English only)
4. ArXiv Abstracts-2021

- **Text Length**: 50 to 2000 characters per paragraph
- **Readability Grade**: Median of six readability metrics (Flesch-Kincaid, Gunning Fog, SMOG, Automated Readability Index, Coleman-Liau, Linsear Write)

### [Data Transformation](https://huggingface.co/datasets/agentlans/text-stats#readability-score-calculation)
- U.S. reading grade levels were transformed using the Box-Cox method (λ = 0.8766912)
- Standardization and scale inversion were applied to generate 'readability' scores
- Higher scores indicate easier readability

### Transformation Statistics
- λ (lambda) = 0.8766912
- Mean (before standardization) = 7.908629
- Standard deviation (before standardization) = 3.339119

## Usage Example

```python
import torch
import numpy as np
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Device setup
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load model and tokenizer
model_name = "agentlans/deberta-v3-xsmall-zyda-2-readability"
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=1).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prediction function
def predict_score(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    with torch.no_grad():
        logits = model(**inputs).logits
    return logits.item()

# Grade level conversion function
def grade_level(y):
    lambda_, mean, sd = 0.8766912, 7.908629, 3.339119
    y_unstd = (-y) * sd + mean
    return np.power((y_unstd * lambda_ + 1), (1 / lambda_))

# Example
input_text = "The mitochondria is the powerhouse of the cell."
readability = predict_score(input_text)
grade = grade_level(readability)
print(f"Predicted score: {readability:.2f}\nGrade: {grade:.1f}")
```

## Sample Outputs

| Text | Readability | Grade |
|------|------------:|------:|
| I like to eat apples. | 2.21 | 1.6 |
| The cat is on the mat. | 2.17 | 1.7 |
| Birds are singing in the trees. | 2.05 | 2.1 |
| The sun is shining brightly today. | 1.95 | 2.5 |
| She enjoys reading books in her free time. | 1.84 | 2.9 |
| The quick brown fox jumps over the lazy dog. | 1.75 | 3.2 |
| After a long day at work, he finally relaxed with a cup of tea. | 1.21 | 5.4 |
| As the storm approached, the sky turned a deep shade of gray, casting an eerie shadow over the landscape. | 0.54 | 8.2 |
| Despite the challenges they faced, the team remained resolute in their pursuit of excellence and innovation. | -0.52 | 13.0 |
| In a world increasingly dominated by technology, the delicate balance between human connection and digital interaction has become a focal point of contemporary discourse. | -1.91 | 19.5 |

## Training Procedure

### Hyperparameters
- Learning rate: 5e-05
- Train batch size: 64
- Eval batch size: 8
- Seed: 42
- Optimizer: AdamW (betas=(0.9,0.999), epsilon=1e-08)
- LR scheduler: Linear
- Number of epochs: 3.0

### Training Results

| Training Loss | Epoch | Step  | Validation Loss | MSE    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.0297        | 1.0   | 13589 | 0.0302          | 0.0302 |
| 0.0249        | 2.0   | 27178 | 0.0279          | 0.0279 |
| 0.0218        | 3.0   | 40767 | 0.0273          | 0.0273 |

## Framework Versions
- Transformers: 4.46.3
- PyTorch: 2.5.1+cu124
- Datasets: 3.1.0
- Tokenizers: 0.20.3