T5 was trained on sst2 as part of it’s multi-task pre-training mixture, so to use T5 for sentiment without fine-tuning use the prefix sst2 sentence:  and pass it to the model. You can do it two ways
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("t5-small")
model = T5ForConditionalGeneration.from_pretrained("t5-small")

text = "sst2 sentence: it confirms fincher ’s status as a film maker who artfully bends technical know-how to the service of psychological insight"

enc = tokenizer(text, return_tensors="pt")
tokens = model.generate(**enc)
tokenizer.batch_decode(tokens)
=> ['positive']

or use the text2text-generation pipeline
t5_sentiment = pipeline("text2text-generation")
text = "sst2 sentence: it confirms fincher ’s status as a film maker who artfully bends technical know-how to the service of psychological insight"
t5_sentiment(text)
=> [{'generated_text': 'positive'}]