This supplementary material contains code for the article: SciNER: A Novel Scientific Named Entity Recognizing Framework
We preset a lot of configurations containing the hyper-parameters we used in config
directory.
Please download the corresponding model parameters or embeddings in advance. Save them in embeddings
directory.
Download the SCIERC dataset and save it to data/scierc
directory.
For other datasets, we use as the same as SciBERT.
For example:
.
├── embeddings
│ └── scibert_scivocab_uncased
│ ├── vocab.txt
│ ├── config.json
│ └── pytorch_model.bin
├── data
│ └── scierc
│ ├── dev.json
│ ├── test.json
│ └── train.json
...
Requirements are listed in requirements.txt
.
python train.py -c config/sept.json -d <gpu>
python test.py -r <saved_checkpoint_path> -d <gpu>
If you use SciNER in your research, please cite our paper:
@InProceedings{10.1007/978-3-030-60450-9_65,
author="Yan, Tan
and Huang, Heyan
and Mao, Xian-Ling",
editor="Zhu, Xiaodan
and Zhang, Min
and Hong, Yu
and He, Ruifang",
title="SciNER: A Novel Scientific Named Entity Recognizing Framework",
booktitle="Natural Language Processing and Chinese Computing",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="828--839",
isbn="978-3-030-60450-9"
}