sentiment_analysis.py

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load pre-trained model and tokenizer
#model_name = "ProsusAI/finbert"
#model_name = "ahmedrachid/FinancialBERT-Sentiment-Analysis"
model_name="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example: Classify a financial text
text = "With the launch of Apple Silicon, Apple shares have increased"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
predictions = outputs.logits.argmax(dim=1).item()

# 'predictions' will contain the sentiment class (e.g., 0 for negative, 1 for neutral, 2 for positive)
print("Predicted sentiment class:", predictions)