How to get logits or raw model scores from Hugging Face text classification pipeline?

I'm using Hugging Face's pipeline for sentiment analysis with a model like distilbert-base-uncased-finetuned-sst-2-english.

The pipeline returns labels and scores like:

{'label': 'POSITIVE', 'score': 0.987}

However, I want the raw logits (before softmax) or full class probability distribution (not just the top one), so I can:

1. Calibrate scores
2. Apply temperature scaling
3. Compute confidence intervals

from transformers import pipeline

clf = pipeline("sentiment-analysis")
result = clf("I love this movie!")
print(result)

I tried setting return_all_scores=True, but it still gives only post-softmax values.

This might help

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F

# Load tokenizer and model
model_name = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prepare inputs
text = "I love this movie!"
inputs = tokenizer(text, return_tensors="pt")

# Get logits
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits  # Raw logits before softmax

# Optionally convert to probabilities
probs = F.softmax(logits, dim=1)

# Print
print("Logits:", logits)
print("Probabilities:", probs)