inference_examples.py

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

def demo_sentiment():
    clf = pipeline("sentiment-analysis", model="YOUR-USERNAME/Finance-NLP-Toolkit")
    print(clf("Shares plunged after the weak revenue outlook."))

def demo_ner():
    tok = AutoTokenizer.from_pretrained("YOUR-USERNAME/Finance-NLP-Toolkit", revision="ner")
    ner_model = AutoModelForTokenClassification.from_pretrained("YOUR-USERNAME/Finance-NLP-Toolkit", revision="ner")
    ner = pipeline("token-classification", model=ner_model, tokenizer=tok, aggregation_strategy="simple")
    print(ner("Microsoft will acquire Nuance for $19.7 billion in 2025."))

if __name__ == "__main__":
    demo_sentiment()
    demo_ner()