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from transformers import AutoTokenizer, AutoModelForSequenceClassification
from scipy.special import softmax
import torch
MODEL = "cardiffnlp/twitter-roberta-base-sentiment-latest"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
text = "Terrible"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
probs = softmax(logits.numpy()[0])
labels = ["negative", "neutral", "positive"]
for label, prob in zip(labels, probs):
print(f"{label}: {prob:.4f}")
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