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--- |
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library_name: transformers |
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license: mit |
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base_model: agentlans/deberta-v3-base-zyda-2-v2 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: deberta-v3-base-zyda-2-v2-text-quality-v3 |
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results: [] |
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datasets: |
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- agentlans/text-quality-v3 |
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language: |
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- en |
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--- |
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# DeBERTa Text Quality Model |
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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. |
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## Performance |
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On the evaluation set, it achieved: |
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- Loss: 0.1408 |
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- MSE: 0.1408 |
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- Combined Score: 0.1408 |
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- Tokens processed during training: 102,398,720 |
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## Usage Example |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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model_name = "agentlans/deberta-v3-base-quality-v3" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu") |
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# Higher scores indicate higher text quality. |
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# The sign of the score has no particular meaning. |
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# For example, a negative score doesn't necessarily mean that the text is low quality. |
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def quality(text): |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device) |
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with torch.no_grad(): |
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score = model(**inputs).logits.squeeze().cpu().item() |
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return score |
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print(quality("Your text here.")) |
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``` |
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## Limitations |
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- Works best on non-fiction and general-purpose texts. |
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- Scores give an overall quality estimate but don’t explain why. |
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- 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). |
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- Check for biases and suitability before use. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 10.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mse | Combined Score | Input Tokens Seen | |
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|:-------------:|:-----:|:------:|:---------------:|:------:|:--------------:|:-----------------:| |
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| 0.1635 | 1.0 | 10000 | 0.1854 | 0.1854 | 0.1854 | 10239872 | |
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| 0.1241 | 2.0 | 20000 | 0.1408 | 0.1408 | 0.1408 | 20479744 | |
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| 0.0882 | 3.0 | 30000 | 0.1747 | 0.1747 | 0.1747 | 30719616 | |
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| 0.054 | 4.0 | 40000 | 0.1528 | 0.1528 | 0.1528 | 40959488 | |
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| 0.0372 | 5.0 | 50000 | 0.1480 | 0.1480 | 0.1480 | 51199360 | |
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| 0.0263 | 6.0 | 60000 | 0.1524 | 0.1524 | 0.1524 | 61439232 | |
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| 0.0203 | 7.0 | 70000 | 0.1495 | 0.1495 | 0.1495 | 71679104 | |
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| 0.0135 | 8.0 | 80000 | 0.1482 | 0.1482 | 0.1482 | 81918976 | |
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| 0.0098 | 9.0 | 90000 | 0.1450 | 0.1450 | 0.1450 | 92158848 | |
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| 0.0073 | 10.0 | 100000 | 0.1453 | 0.1453 | 0.1453 | 102398720 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
