---
library_name: transformers
license: mit
base_model: agentlans/deberta-v3-base-zyda-2-v2
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-base-zyda-2-v2-text-quality-v3
  results: []
datasets:
- agentlans/text-quality-v3
language:
- en
---
# DeBERTa Text Quality Model

This model rates the **quality of English text** for AI learning. Input a text string, and it outputs a numeric quality score reflecting overall informativeness and usefulness.

## Performance

On the evaluation set, it achieved:  
- Loss: 0.1408  
- MSE: 0.1408  
- Combined Score: 0.1408  
- Tokens processed during training: 102,398,720  

## Usage Example

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))
```

## Limitations

- Works best on non-fiction and general-purpose texts.  
- Scores give an overall quality estimate but don’t explain why.  
- The model is large and slow; for faster results with similar accuracy, try [agentlans/GIST-all-MiniLM-L6-v2-quality-v3](https://huggingface.co/agentlans/GIST-all-MiniLM-L6-v2-quality-v3).  
- Check for biases and suitability before use.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- 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: 10.0

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Mse    | Combined Score | Input Tokens Seen |
|:-------------:|:-----:|:------:|:---------------:|:------:|:--------------:|:-----------------:|
| 0.1635        | 1.0   | 10000  | 0.1854          | 0.1854 | 0.1854         | 10239872          |
| 0.1241        | 2.0   | 20000  | 0.1408          | 0.1408 | 0.1408         | 20479744          |
| 0.0882        | 3.0   | 30000  | 0.1747          | 0.1747 | 0.1747         | 30719616          |
| 0.054         | 4.0   | 40000  | 0.1528          | 0.1528 | 0.1528         | 40959488          |
| 0.0372        | 5.0   | 50000  | 0.1480          | 0.1480 | 0.1480         | 51199360          |
| 0.0263        | 6.0   | 60000  | 0.1524          | 0.1524 | 0.1524         | 61439232          |
| 0.0203        | 7.0   | 70000  | 0.1495          | 0.1495 | 0.1495         | 71679104          |
| 0.0135        | 8.0   | 80000  | 0.1482          | 0.1482 | 0.1482         | 81918976          |
| 0.0098        | 9.0   | 90000  | 0.1450          | 0.1450 | 0.1450         | 92158848          |
| 0.0073        | 10.0  | 100000 | 0.1453          | 0.1453 | 0.1453         | 102398720         |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0