Fix loading custom vocab in transformers style for LM finetuning

[tholor]

LM finetuning with custom vocab was broken after switching to the transformer style of handling custom vocab (adding tokens instead of using "unused tokens").

The complication here is that with a larger vocab we need to adjust both the size of the embedding layer in the LM and the decoder (bias+weights) in the PH. In addition, the decoder shares the weights with the embedding layer.

We therefore need to supply now an extra arg "n_added_tokens" for loading the PH / LM.

Example:

    ...
    tokenizer.add_tokens(["somecustomtoken", "specialrareword"])

    ...
    language_model = LanguageModel.load(lang_model, n_added_tokens=len(tokenizer.added_tokens_decoder))
    lm_prediction_head = BertLMHead.load(lang_model, n_added_tokens=len(tokenizer.added_tokens_decoder))

[brandenchan]

Looks good, I just think a clearer info message would be good