Experimenting with few-shot classification.

app.py

@@ -72,10 +72,10 @@ ort_model = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-small-en-v1.5
 few_shot_model = SetFitModel.from_pretrained("moshew/bge-small-en-v1.5_setfit-sst2-english") # "moshew/bge-small-en-v1.5_setfit-sst2-english"
 
 # Train few_shot_model
-candidate_labels = ["correct", "wrong"]
+candidate_labels = ["supported", "refuted"]
 reference_dataset = load_dataset("SetFit/sst2")
 dummy_dataset = Dataset.from_dict({})
-train_dataset = get_templated_dataset(dummy_dataset, candidate_labels=candidate_labels, sample_size=8, template="This conclusion is {}.")
+train_dataset = get_templated_dataset(dummy_dataset, candidate_labels=candidate_labels, sample_size=8, template="The CONCLUSION is {} by the PASSAGE.")
 args = TrainingArguments(
     batch_size=32,
     num_epochs=1