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
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.
- 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
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