modeling_sim.py

import torch
from torch import nn
from transformers import MobileBertPreTrainedModel, MobileBertModel

class SimModel(MobileBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
        # Initialize weights and apply final processing
        self.post_init()

    def forward(self, input_ids, attention_mask, token_type_ids, return_dict):
        print(input_ids, attention_mask, token_type_ids)
        return self.word_embeddings[input_ids]