NEZHA: Neural Contextualized Representation for Chinese Language Understanding
Table 4: Results of pre-trained models on downstream Chinese NLU tasks.
['Model', 'CMRC EM', 'CMRC F1', 'XNLI Dev', 'XNLI Test', 'LCQMC Dev', 'LCQMC Test', 'PD-NER Dev', 'PD-NER Test', 'ChnSenti Dev', 'ChnSenti Test']
Among the groups of both base models and large models, either ERNIE-Baidu 2.0 or NEZHA achieves the best performances. Note that the part of the results are directly copied from the original papers [cui2019pre, sun2019ernie2]. Due to the possible differences in the experimental setting or finetuning methods, the comparison may not be entirely fair. We notice that there is consistent gaps between our implementation and the results reported in [cui2019pre, sun2019ernie2] on the CMRC task. Once the ERNIE-Baidu 2.0 Chinese models are released, we will evaluate them under the same setting and update this report.