Update README.md

README.md

@@ -32,6 +32,29 @@ The model was trained on [agentlans/tatoeba-english-translations](https://huggin
 
 ## Usage
 
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name="agentlans/mdeberta-v3-base-quality"
+
+# Put model on GPU or else CPU
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+def quality(text):
+    """Processes the text using the model and returns its logits.
+    In this case, it's interpreted as the the combined quality score for that text."""
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
+    with torch.no_grad():
+        logits = model(**inputs).logits.squeeze().cpu()
+    return logits.tolist()
+
+quality("Your text here.")
+```
+
 ## Results
 
 In this study, 10 English text samples of varying quality were generated and translated into Arabic, Chinese, French, Russian, and Spanish using Google Translate. This resulted in a total of 50 translated samples, which were subsequently analyzed by a trained classifier to predict their quality scores.