utils.py

from safetensors.torch import load_model
from transformers import RobertaTokenizer, AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
from transformers import GPT2TokenizerFast, GPT2ForSequenceClassification, Trainer, TrainingArguments, DataCollatorWithPadding
from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification, Trainer, TrainingArguments, DataCollatorWithPadding
import numpy as np



def get_roberta():

    model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-roberta-base', output_attentions=True)
    load_model(model, "roberta.safetensors")

    tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-roberta-base')

    return tokenizer, model

def get_gpt():
    
    model = GPT2ForSequenceClassification.from_pretrained('gpt2', num_labels=3, output_attentions=True)
    model.config.pad_token_id = model.config.eos_token_id
    load_model(model, "gpt.safetensors")

    tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
    tokenizer.pad_token = tokenizer.eos_token

    return tokenizer, model

def get_distilbert():

    model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=3, output_attentions=True)
    load_model(model, "distilbert.safetensors")

    tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')

    return tokenizer, model


def softmax(xx):
    """Compute softmax values for each sets of scores in x."""
    x = xx.detach().numpy()[0]
    return np.exp(x) / np.sum(np.exp(x), axis=0)