预训练时加载huggingface T5模型报错

使用经过脚本转换后的huggingface上的mengzi-t5-base模型时报错：
```
RuntimeError: Error(s) in loading state_dict for Model:
size mismatch for embedding.word_embedding.weight: copying a param with shape torch.Size([32128, 768]) from checkpoint, the shape in current model is torch.Size([32028, 768]).
size mismatch for tgt_embedding.word_embedding.weight: copying a param with shape torch.Size([32128, 768]) from checkpoint, the shape in current model is torch.Size([32028, 768]).
size mismatch for target.output_layer.weight: copying a param with shape torch.Size([32128, 768]) from checkpoint, the shape in current model is torch.Size([32028, 768]).
```
参数：
```
python3 pretrain.py --dataset_path data/dataset.pt \
--pretrained_model_path pretrained_model.bin \
--spm_model_path models/Langboat/mengzi-t5-base/spiece.model \
--vocab_path models/Langboat/mengzi-t5-base/spiece.vocab \
--config_path models/t5-v1_1/base_config.json \
--output_model_path output_model.bin \
--world_size 2 --gpu_ranks 0 1 \
--learning_rate 3e-5 --batch_size 8 \
--span_masking --span_geo_prob 0.3 --span_max_length 5 \
--data_processor t5 \
--embedding word --relative_position_embedding --remove_embedding_layernorm --tgt_embedding word --share_embedding \
--encoder transformer --mask fully_visible --decoder transformer \
--layernorm_positioning pre --layernorm t5 --feed_forward gated --remove_attention_scale --remove_transformer_bias \
--target lm
```
请问是哪里的问题呢，感谢