Experimenting with few-shot classification.

app.py

@@ -67,9 +67,9 @@ model = ORTModelForSequenceClassification.from_pretrained(model_name, file_name=
 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-m3', model_max_length=512) # 'BAAI/bge-small-en-v1.5'
-ort_model = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-m3', file_name="onnx/model.onnx") # 'BAAI/bge-small-en-v1.5'
-few_shot_model = SetFitModel.from_pretrained("BAAI/bge-m3") # "moshew/bge-small-en-v1.5_setfit-sst2-english"
+few_shot_tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-small-en-v1.5', model_max_length=512) # 'BAAI/bge-small-en-v1.5'
+ort_model = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-small-en-v1.5', file_name="onnx/model.onnx") # '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"]