Dhanachandra Ningthoujam

@bradfox2 , @peregilk You can use a modified version of Tensor2Tensor/text_encoder_build_subword.py code to generate BERT compatible vocab. https://github.com/kwonmha/bert-vocab-builder

> @irhallac it is the [unusedXXX]-tokens that can be replaced with any word you like. I am running some experiments on how effective this really is, but from my understanding...

> Hi everyone, > > I am lead author on this paper. Apologies for the radio silence on this request. We are currently working on a revision to the paper/approach...

I don't get this point "CUDA error due to limitations with ram". I trained the model using google colab. Please have a look at this example [here](https://github.com/Dhanachandra/bert_crf/blob/master/bert-crf4NER/bert_crf_4_ner_training.ipynb).

Please check if the bert pretrained model is available for your language. If so please use it. *--* *With Regards,* *Dhanachandra | Research Engineer* *+91-7600776547| 8014289629* *Dhanachandra * *ezDI* *Healthcare...