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Hi, I just embedded the BERT positional embeddings into the 2D space (with umap) for different BERT models that are trained on different languages (I use “pytorch_transformers”). It’s obvious that the embedded positional embeddings for the german model ist way more unstructred than for the other language models. Why is that? Is the german model not that well trained? I also checked the ... Note that GLUE baselines and most published results on these tasks use word embeddings or count-based word vectors as inputs, while our random BERT was fully random. Thus direct comparison is not entirely fair.

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