Comments (1)
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case里的中文句子打分,用的是单字模型。当然也可以先分词,然后用whole word mask的方式预测概率。
另外看到一些评论说, bert不能作为语言模型对句子合理性打分,记得是bert 官方的回复, 您怎么理解。
是,bert对句子打分不严谨。test.zh.md中在缺陷里也提到了,这里对句子打分是简单的近似,并不符合基于markov的联合概率分布。有一定的参考意义,但不能作为严格意义上的ppl。
BERT is not able to model the joint probability using the product rule as in autoregressive models
from bert-as-language-model.
Related Issues (18)
- TODO: HOT 1
- 为什么要设置max_predictions_per_seq HOT 2
- paper to cite HOT 1
- serving 的问题 HOT 1
- 预训练模型有没有保存cls/predictions下的权重 HOT 1
- 原理是什么? HOT 2
- online service的问题 HOT 4
- 就是按顺序依次mask掉每一个词,然后预测该词的概率。 HOT 4
- 求概率还是用AR model HOT 1
- 推导结果不正常 HOT 2
- 作为language model对句子打分需要finetune吗 HOT 3
- Export bert-as-language-model as online service but failed. HOT 3
- Words outside of vocab HOT 1
- 字prob和句子perplexity计算方法 HOT 2
- 每次执行时提示找不到模型 HOT 10
- predict耗时太久,运行一条语句需要10s,请问怎么改进呢 HOT 3
- Probability of the last word is always too small HOT 2
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from bert-as-language-model.