Summary of training/finetuning/prediction commands

[NiteshMethani]

Hi, Really impressive work and a nice repository combining all the NER models. I was trying to comprehend the codebase and what all are its capabilities. To that end, I made a list of NER-baselines and a corresponding command. Can authors of this repo @allanj @furkan-celik @yuchenlin help me complete this list?

Model	Embeddings	Sample Command	Notes
BiLSTM	Random
BiLSTM + CharCNN	Random	NA
BiLSTM + CharLSTM	Random	python trainer.py --use_crf_rnn 0
BiLSTM + CharCNN + CRF	Random	NA
BiLSTM + CharLSTM + CRF	Random	python trainer.py --use_crf_rnn 1
BiLSTM + CharLSTM + CRF	FastText
BiLSTM + CharLSTM + CRF	static embedding from ELMo
BiLSTM + CharLSTM + CRF	static embedding from BERT
BiLSTM + CharLSTM + CRF	contextual embedding from ELMo
BiLSTM + CharLSTM + CRF	contextual embedding from BERT
Default bert-base-uncased	-
Default bert-large-uncased	-
Finetuned bert-base-uncased	-
Finetuned bert-base-uncased	concatenated with pretrained FastText embedding
Default roberta-base	-
Finetuned roberta-base	-
Finetuned roberta-base	concatenated with pretrained FastText embedding

I understand some of these configurations might not be supported and there might be additional configurations which this repo supports. So feel free to add/modify the above table. I feel such a one-stop table will help the community and could be a contribution towards documentation.

-Nitesh

[allanj]

Thanks, Nitesh. I will try to do so time by time. Please allows some time for this

[NiteshMethani]

Sure, thanks!