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This is a version of multilingual bert (bert-base-multilingual-cased), where the segment embedding of the 1's is copied into the 0's. Yes, that's all there is to it. We have found that this improves performance substantially in low-resource setups for word-level tasks (e.g. average 2.5 LAS on a variety of UD treebanks). More details are to be released in our LREC2022 paper titled: Frustratingly Easy Performance Improvements for Cross-lingual Transfer: A Tale on BERT and Segment Embeddings.

These embeddings are generated by the following code

import AutoModel
baseEmbeddings = AutoModel.from_pretrained("bert-base-multilingual-cased")
tte = baseEmbeddings.embeddings.token_type_embeddings.weight.clone().detach()
baseEmbeddings.embeddings.token_type_embeddings.weight[0,:] = tte[1,:]

More details and other varieties can be found in the repo: https://bitbucket.org/robvanderg/segmentembeds/

Note that when using this model on a single sentence task (or word-level task), the results would be similar as just using token_type_id=1 for all tokens.

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