ErfanMoosaviMonazzah/backpack-gpt2-nli

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained('gpt2')
tokenizer.pad_token = tokenizer.eos_token

def tokenize_function(examples):
    concatenated_sentences = [f'{premise.strip(".")}. ^ {hypothesis.strip(".")}.' for premise, hypothesis in zip(examples['premise'], examples['hypothesis'])]

    tokenized_inputs = tokenizer(
        concatenated_sentences,
        padding="max_length",
        truncation=True,
        max_length=512,
        return_tensors="pt",
    )
    return tokenized_inputs

model = AutoModelForSequenceClassification.from_pretrained('ErfanMoosaviMonazzah/backpack-gpt2-nli', trust_remote_code=True)
model.eval()

tokenized_sent = tokenize_function({
    'premise':['A boy is jumping on skateboard in the middle of a red bridge.',
               'Two women who just had lunch hugging and saying goodbye.',
               'Children smiling and waving at camera'],
    'hypothesis':['The boy does a skateboarding trick.',
                  'The friends have just met for the first time in 20 years, and have had a great time catching up.',
                  'The kids are frowning']
})
model.predict(input_ids=tokenized_sent['input_ids'], attention_mask=tokenized_sent['attention_mask'])