modeling_sim.py

import torch
from torch import nn
from transformers import BertPreTrainedModel

class ParagramSPModel(BertPreTrainedModel):
    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, return_dict):
        print(input_ids)
        print(attention_mask)
        embeddings = self.word_embeddings(input_ids)
        masked_embeddings = embeddings * attention_mask[:, :, None]
        mean_pooled_embeddings = masked_embeddings.sum(dim=1) / attention_mask[:, :, None].sum(dim=1)
        return (embeddings, mean_pooled_embeddings, embeddings)