Update README.md

README.md

@@ -5,9 +5,35 @@ license: mit
 
 ### Usage
 
-First, install [Sentence-Transformers](https://www.sbert.net/)
+In Transformers
 
-Then,
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from scipy.special import softmax, expit
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model_name="ragarwal/deberta-v3-base-nli-mixer"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+sentence = "During its monthly call, the National Oceanic and Atmospheric Administration warned of \
+increased temperatures and low precipitation" 
+labels = ["Computer", "Climate Change", "Tablet", "Football", "Artificial Intelligence", "Global Warming"] 
+
+features = tokenizer([[sentence, l] for l in labels], padding=True, truncation=True, return_tensors="pt")
+
+model.eval()
+with torch.no_grad():
+    scores = model(**features).logits
+    print(expit(scores)) #Multi-Label Classification
+    print(softmax(scores)) #Single-Label Classification
+    
+```
+
+
+In [Sentence-Transformers](https://www.sbert.net/)
 
 ```python
 from sentence_transformers import CrossEncoder
@@ -15,7 +41,6 @@ from sentence_transformers import CrossEncoder
 model_name="ragarwal/deberta-v3-base-nli-mixer"
 model = CrossEncoder(model_name, max_length=256)
 
-
 sentence = "During its monthly call, the National Oceanic and Atmospheric Administration warned of \
 increased temperatures and low precipitation" 
 labels = ["Computer", "Climate Change", "Tablet", "Football", "Artificial Intelligence", "Global Warming"]