how to compare ckiplab/bert-base-chinese with bert-base-chinese?

[Benjamin]

Thanks so much for this excellent model and having it accessible in huggingface.

Would like to know why the ckiplab/bert-base-chinese seems a bit strange to me when compared to the usual bert-base-chinese which I think it mainly trained on simplified chinese. For instance, when I masked the word 風 of the phrase 颱風預測。 in the usual bert-base-chinese it managed to give me back 風 with high probability 0.992; in contrast, in the ckiplab/bert-base-chinese it didn't give back the masked word 風 in the top 5 but giving the word 的 with highest probability albeit only around 0.3 something which I am wondering.

Is it supposed that we have to fine-tune this MLM first? Or perhaps I interpreted it wrongly (as I'm very new in this field). Mind sharing a bit on your thought? Thanks very much and thanks in advance.

[Mu Yang]

Based on the property of the training data and the task objective, this model performs poorly on short or incomplete sentences.
For longer sentences like 新聞報導颱[MASK]預測。, this model managed to give me back 風 in high probability.

Thanks for pointing out the weakness of our model. We will keep working to make it better.