Experimenting with few-shot classification.

app.py

@@ -56,8 +56,8 @@ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, model_max_length=512)
 classifier = pipeline(task="zero-shot-classification", model=model, tokenizer=tokenizer)
 
 few_shot_tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-small-en-v1.5', model_max_length=512)
-ort_model = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-small-en-v1.5', file_name="onnx/model.onnx", multi_target_strategy="multi-output")
-few_shot_model = SetFitModel.from_pretrained("moshew/bge-small-en-v1.5_setfit-sst2-english", multi_target_strategy="multi-output")
+ort_model = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-small-en-v1.5', file_name="onnx/model.onnx")
+few_shot_model = SetFitModel.from_pretrained("moshew/bge-small-en-v1.5_setfit-sst2-english")
 
 # Train few_shot_model
 candidate_labels = ["supported", "refuted", "neither"]