---
library_name: transformers
license: mit
base_model: agentlans/deberta-v3-base-zyda-2
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-base-zyda-2-transformed-readability
  results: []
datasets:
- agentlans/text-stats
language:
- en
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# deberta-v3-base-zyda-2-transformed-readability

This model is a fine-tuned version of [agentlans/deberta-v3-base-zyda-2](https://huggingface.co/agentlans/deberta-v3-base-zyda-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0267
- Mse: 0.0267

## Model description

More information needed

## Intended uses & limitations

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

# Load model and tokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

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

# Function to perform inference
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()

# Function to transform the score back to educational grade level
def grade_level(y):
    # Updated parameters
    lambda_ = 0.8766912
    mean = 7.908629
    sd = 3.339119
    
    # Unstandardize the data
    y_unstd = (-y) * sd + mean
    
    # Invert the Box-Cox transformation
    return np.power((y_unstd * lambda_ + 1), (1 / lambda_))

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

Example output:

| Text | Readability | Grade |
|------|---------:|-----:|
| I like to eat apples. | 1.95 | 2.5 |
| The cat is on the mat. | 1.93 | 2.6 |
| The sun is shining brightly today. | 1.85 | 2.9 |
| Birds are singing in the trees. | 1.84 | 2.9 |
| The quick brown fox jumps over the lazy dog. | 1.74 | 3.3 |
| She enjoys reading books in her free time. | 1.69 | 3.5 |
| After a long day at work, he finally relaxed with a cup of tea. | 1.16 | 5.6 |
| 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. | -0.49 | 12.8 |
| In a world increasingly dominated by technology, the delicate balance between human connection and digital interaction has become a focal point of contemporary discourse. | -2.01 | 20.0 |

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Mse    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.0288        | 1.0   | 13589 | 0.0286          | 0.0286 |
| 0.023         | 2.0   | 27178 | 0.0272          | 0.0272 |
| 0.0189        | 3.0   | 40767 | 0.0267          | 0.0267 |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3