Example of creating a Gist using Python

intro-zero_hugging-face-course-automodel-pipelines.py

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification#, AutoModel

checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# model = AutoModel.from_pretrained(checkpoint)
model_seq_classification = AutoModelForSequenceClassification.from_pretrained(checkpoint)

raw_inputs = [
    "I've been waiting for a HuggingFace course my whole life.",
    "I hate this so much!",
]

inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors="pt")
print("Inputs:", inputs)

output = model_seq_classification(**inputs)
print("Logits:", output.logits)

predictions = torch.nn.functional.softmax(output.logits, dim=1)
print("Predictions:", predictions)