Update README.md

README.md

@@ -66,8 +66,8 @@ from transformers import pipeline
 
 clf = pipeline(
     task="text-classification",
-    model="YOUR_USERNAME/academic-sentiment-classifier",
-    tokenizer="YOUR_USERNAME/academic-sentiment-classifier",
+    model="EvilScript/academic-sentiment-classifier",
+    tokenizer="EvilScript/academic-sentiment-classifier",
     return_all_scores=False,
 )
 
@@ -82,7 +82,7 @@ If you prefer friendly labels, you can map them:
 from transformers import pipeline
 
 id2name = {"LABEL_0": "negative", "LABEL_1": "positive"}
-clf = pipeline("text-classification", model="YOUR_USERNAME/academic-sentiment-classifier")
+clf = pipeline("text-classification", model="EvilScript/academic-sentiment-classifier")
 res = clf("This section lacks clarity and the experiments are inconclusive.")[0]
 res["label"] = id2name.get(res["label"], res["label"])  # map to human-friendly label
 print(res)
@@ -95,8 +95,8 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
 device = 0 if torch.cuda.is_available() else -1
-tok = AutoTokenizer.from_pretrained("YOUR_USERNAME/academic-sentiment-classifier")
-model = AutoModelForSequenceClassification.from_pretrained("YOUR_USERNAME/academic-sentiment-classifier")
+tok = AutoTokenizer.from_pretrained("EvilScript/academic-sentiment-classifier")
+model = AutoModelForSequenceClassification.from_pretrained("EvilScript/academic-sentiment-classifier")
 
 texts = [
     "I recommend acceptance; the methodology is solid and results are convincing.",