Hugging Face

pip install transformers sentence-transformers torch

from transformers import pipeline

classifier = pipeline(
    "sentiment-analysis",
    model="distilbert/distilbert-base-uncased-finetuned-sst-2-english",
)

reviews = [
    "Delivery was fast and the product is amazing!",
    "Terrible quality, it broke after two days.",
    "It's okay, nothing special.",
]
for review, result in zip(reviews, classifier(reviews)):
    print(f"{result['label']:8} {result['score']:.2f}  {review}")

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

name = "distilbert/distilbert-base-uncased-finetuned-sst-2-english"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForSequenceClassification.from_pretrained(name)

inputs = tok("The food was cold and the service was slow", return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits           # raw scores
probs = torch.softmax(logits, dim=-1)[0]     # softmax -> probabilities

for label_id, p in enumerate(probs):
    print(f"{model.config.id2label[label_id]:8} {p:.1%}")