Update README.md

README.md

@@ -10,27 +10,51 @@ model-index:
 datasets:
 - agentlans/text-quality-v3
 ---
+Sure! Here’s a more concise and natural revision of your model card:
 
 # deberta-v3-base-zyda-2-v2-text-quality-v3
 
-This model is a fine-tuned version of [agentlans/deberta-v3-base-zyda-2-v2](https://huggingface.co/agentlans/deberta-v3-base-zyda-2-v2) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1408
-- Mse: 0.1408
-- Combined Score: 0.1408
-- Num Input Tokens Seen: 102398720
+## Overview
 
-## Model description
+This model rates the **quality of English text** for AI learning. Input a text string, and it outputs a numeric quality score reflecting overall readability, informativeness, and usefulness.
 
-More information needed
+It’s fine-tuned from [agentlans/deberta-v3-base-zyda-2-v2](https://huggingface.co/agentlans/deberta-v3-base-zyda-2-v2) using the same dataset.
 
-## Intended uses & limitations
+## Performance
 
-More information needed
+On the evaluation set, it achieved:  
+- Loss: 0.1408  
+- MSE: 0.1408  
+- Combined Score: 0.1408  
+- Tokens processed during training: 102,398,720  
 
-## Training and evaluation data
+## Usage Example
 
-More information needed
+```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, score < 0 doesn't 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 `MyOtherModel`.  
+- Check for biases and suitability before use.
 
 ## Training procedure