- Named Entity Recognition for biomedical data using [Scibert](https://arxiv.org/abs/1903.10676) and [Biobert](https://arxiv.org/abs/1901.08746) .
- Different classification architectures like Bert-Crf, Bert-LSTM-Crf on top of Bert Layer.
- Finetuning script is compatible with the datasets provided by [Scibert git repo](https://github.com/allenai/scibert/tree/master/data/ner) data folder.
- add labels file to the data_dir; a labels file should have all the labels from the data, one label per line.
- Edit the config file of your chosen architecture and save.
- Execute the below command
python finettune_with_logger.py
--model_config_path=/path/to/the/edited/config/file
--data_dir=/path/to/the/data_dir/
--logger_file_dir=/path/to/the/dir/where/logs/should/be/saved/
--labels_file=/path/to/the/labels_file/of/the/dataset/file
| Data | Architecture | Best Test F1 |
|---|---|---|
| JNLPBA | Bert-CRF | - |
| Bert-TokenClassification | - | |
| Bert-LSTM-CRF | - | |
| NCBI-Disease | Bert-CRF | |
| Bert-TokenClassification | - | |
| Bert-LSTM-CRF(no finetuning) | 0.88 | |
| BC5CDR | Bert-CRF | - |
| Bert-TokenClassification | - | |
| Bert-LSTM-CRF | - | |
| SCIIE | Bert-CRF | - |
| Bert-TokenClassification | - | |
| Bert-LSTM-CRF | - |