Load EncoderDecoderModel from a checkpoint
I am trying to save the model checkpoint withtransformers’save_pretrainedin order for the model to be easily initialized withfrom_pretrainedin the future. I’ve created a model class that inherits fromPreTrainedModelin order for it to have asave_pretrainedmethod. I also created a config as described in one of the huggingface courses.config_encoder = BertConfig()
config_decoder = BertConfig()
config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)

class TransformerShared(PreTrainedModel):
  
    def __init__(self, config):
        super(TransformerShared, self).__init__(config)
        self.shared = EncoderDecoderModel.from_encoder_decoder_pretrained('bert-base-cased',
                                                                          'bert-base-cased',
                                                                          tie_encoder_decoder=True)

    def forward(self, input_ids, attention_mask, labels):
        output = self.shared(input_ids=input_ids,
                             attention_mask=attention_mask,
                             labels=labels)
        return output
        
model = TransformerShared(config)
model.shared.encoder.resize_token_embeddings(len(tokenizer))
model.shared.decoder.resize_token_embeddings(len(tokenizer))
model.shared.config.decoder_start_token_id = tokenizer.cls_token_id
model.shared.config.eos_token_id = tokenizer.sep_token_id
model.shared.config.pad_token_id = tokenizer.pad_token_id
model.shared.config.vocab_size = model.shared.config.encoder.vocab_size
model.shared.config.bos_token_id = tokenizer.bos_token_id

model.load_state_dict(checkpoint['model_state_dict'])Up to this moment, the model works just fine, but after saving and instantiating withfrom_pretrainedthe program gives a warning telling that some weights were not appropriately initialized and set to random. After that, a pre-trained model outputs poor results.Any suggestions would be much appreciated.Thank you!