Update README.md

README.md

@@ -44,4 +44,19 @@ pipe = pipeline(model=model_name, device=device, truncation=True, max_length=max
 is_it_a_joke = """A nervous passenger is about to book a flight ticket, and he asks the airlines' ticket seller, "I hope your planes are safe. Do they have a good track record for safety?" The airline agent replies, "Sir, I can guarantee you, we've never had a plane that has crashed more than once." """
 result = pipe(is_it_a_joke) # [{'label': 'LABEL_1', 'score': 0.7313136458396912}]
 print('This is a joke') if result[0]['label'] == 'LABEL_1' else print('This is not a joke')
+
+# Or if you don't want to use pipelines
+from transformers import pipeline
+import torch
+device = 0 if torch.cuda.is_available() else -1
+model_name = 'Reggie/muppet-roberta-base-joke_detector'
+max_seq_len = 510
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, model_max_length=510)
+model = AutoModelForSequenceClassification.from_pretrained(model_name).to(device)
+input = tokenizer(is_it_a_joke, '', truncation=True, return_tensors="pt")
+output = model(input["input_ids"].to(device))
+prediction = torch.softmax(output["logits"][0], -1).tolist()
+pred_out = 1 if prediction[0] < prediction[1] else 0
+print('This is a joke') if pred_out == 1 else print('This is not a joke')
 ```