ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

if __name__ == '__main__':
    max_length = 256

    premise = "Two women are embracing while holding to go packages."
    hypothesis = "The men are fighting outside a deli."

    hg_model_hub_name = "ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli"
    tokenizer = AutoTokenizer.from_pretrained(hg_model_hub_name)
    model = AutoModelForSequenceClassification.from_pretrained(hg_model_hub_name)

    tokenized_input_seq_pair = tokenizer.encode_plus(premise, hypothesis,
                                                     max_length=max_length,
                                                     return_token_type_ids=True, truncation=True)

    input_ids = torch.Tensor(tokenized_input_seq_pair['input_ids']).long().unsqueeze(0)
    token_type_ids = torch.Tensor(tokenized_input_seq_pair['token_type_ids']).long().unsqueeze(0)
    attention_mask = torch.Tensor(tokenized_input_seq_pair['attention_mask']).long().unsqueeze(0)

    outputs = model(input_ids,
                    attention_mask=attention_mask,
                    token_type_ids=token_type_ids,
                    labels=None)

    predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()

    print("Premise:", premise)
    print("Hypothesis:", hypothesis)
    print("Entailment:", predicted_probability[0])
    print("Neutral:", predicted_probability[1])
    print("Contradiction:", predicted_probability[2])