| | --- |
| | 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 |
