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.
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?
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