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2c2a85d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load the pretrained sentiment model
model_name = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Function to classify sentiment
def analyze_sentiment(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=1)
prediction = torch.argmax(probs).item()
label = "positive" if prediction == 1 else "negative"
return label, probs[0][prediction].item()
# Try it out!
if __name__ == "__main__":
print("🧠 Sentiment Analyzer (type 'exit' to quit)\n")
while True:
sentence = input("Enter a sentence: ").strip()
if sentence.lower() in ["exit", "quit"]:
print("👋 Goodbye!")
break
sentiment, confidence = analyze_sentiment(sentence)
print(f"🧠 Sentiment: {sentiment.capitalize()} (Confidence: {confidence:.2f})\n") |