examples; auto

app.py

@@ -1,11 +1,11 @@
 import gradio as gr
 import torch
-from transformers import pipeline, AutoTokenizer, DistilBertForSequenceClassification
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, DistilBertForSequenceClassification
 
 modelName = "Pendrokar/TorchMoji"
 
 distil_tokenizer = AutoTokenizer.from_pretrained(modelName)
-distil_model = DistilBertForSequenceClassification.from_pretrained(modelName, problem_type="multi_label_classification")
+distil_model = AutoModelForSequenceClassification.from_pretrained(modelName, problem_type="multi_label_classification")
 
 pipeline = pipeline(task="text-classification", model=distil_model, tokenizer=distil_tokenizer)
 
@@ -13,7 +13,19 @@ def predict(deepmoji_analysis):
     predictions = pipeline(deepmoji_analysis)
     return deepmoji_analysis, {p["label"]: p["score"] for p in predictions}
 
-gradio_app = gr.Interface(fn=predict, inputs="text", outputs="text")
+gradio_app = gr.Interface(
+    fn=predict,
+    inputs="text",
+    outputs="text"
+    verbose=True,
+    examples=[
+        "This GOT show just remember LOTR times!",
+        "Man, can't believe that my 30 days of training just got a NaN loss",
+        "I couldn't see 3 Tom Hollands coming...",
+        "There is nothing better than a soul-warming coffee in the morning",
+        "I fear the vanishing gradient", "deberta"
+    ]
+)
 
 if __name__ == "__main__":
     gradio_app.launch()