Update README.md

README.md

@@ -13,29 +13,47 @@ datasets:
 - agentlans/en-translations-quality-v3
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Multilingual Text Quality Model
 
-# multilingual-e5-small-aligned-v2-text-quality-v3
+This model rates the **quality of non-English text** for AI learning.
+Input a text string, and it outputs a numeric quality score reflecting overall informativeness and usefulness.
 
-This model is a fine-tuned version of [agentlans/multilingual-e5-small-aligned-v2](https://huggingface.co/agentlans/multilingual-e5-small-aligned-v2) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0641
-- Mse: 0.0641
-- Combined Score: 0.0641
-- Num Input Tokens Seen: 1109813760
+## Performance
 
-## Model description
+On the evaluation set, it achieved:  
+- Loss: 0.0641  
+- MSE: 0.0641  
+- Combined Score: 0.0641  
+- Tokens processed during training: 1,109,813,760  
 
-More information needed
+## Usage Example
 
-## Intended uses & limitations
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-More information needed
+model_name = "agentlans/multilingual-e5-small-quality-v3"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")
 
-## Training and evaluation data
+# 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
 
-More information needed
+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.
+- Unlike the other `quality-v3` models, this model is only trained on short non-English sentences.
+- Check for biases and suitability before use.
 
 ## Training procedure