Trained only on movie reviews (SST-2); performance may degrade on other domains.
Does not explicitly handle sarcasm or complex sentiment.
Not suitable for multilingual sentiment analysis.

Usage

🤗 Transformers Pipeline

python

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Talip7/bert-base-sst2-finetuned"
)

classifier("I love this project!")

🔥 PyTorch Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("Talip7/bert-base-sst2-finetuned")
model = AutoModelForSequenceClassification.from_pretrained("Talip7/bert-base-sst2-finetuned")

text = "This movie was absolutely fantastic!"

inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

logits = outputs.logits
prediction = torch.argmax(logits, dim=-1).item()

label_map = {0: "Negative", 1: "Positive"}
print(f"Prediction: {label_map[prediction]}")