Experimenting with few-shot classification.

app.py

@@ -60,9 +60,9 @@ class OnnxSetFitModel:
 # "Xenova/distilbert-base-uncased-mnli" "typeform/distilbert-base-uncased-mnli" Bad answers
 # "Xenova/deBERTa-v3-base-mnli" "MoritzLaurer/DeBERTa-v3-base-mnli" Still a bit slow and not great answers
 # "xenova/nli-deberta-v3-small" "cross-encoder/nli-deberta-v3-small" Was using this for a good while and it was...okay
-model_name = "MoritzLaurer/bge-m3-zeroshot-v2.0"
+model_name = "MoritzLaurer/deberta-v3-base-zeroshot-v2.0"
 file_name = "onnx/model.onnx"
-tokenizer_name = "MoritzLaurer/bge-m3-zeroshot-v2.0"
+tokenizer_name = "MoritzLaurer/deberta-v3-base-zeroshot-v2.0"
 model = ORTModelForSequenceClassification.from_pretrained(model_name, file_name=file_name)
 tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, model_max_length=512)
 classifier = pipeline(task="zero-shot-classification", model=model, tokenizer=tokenizer)