metadata

language:
  - en
library_name: flair
pipeline_tag: token-classification

English NER model for extraction of named entities from scientific acknowledgement texts using Flair Embeddings

F1-Score: 0.79

Predicts 6 tags:

label	description	precision	recall	f1-score	support
GRNB	grant number	0,93	0,98	0,96	160
IND	person	0,98	0,98	0,98	295
FUND	funding organization	0,70	0,83	0,76	157
UNI	university	0,77	0,74	0,75	99
MISC	miscellaneous	0,65	0,65	0,65	82
COR	corporation	0,75	0,50	0,60	12

Based on Flair embeddings

Usage

Requires: Flair (pip install flair)

#import libraries
from flair.data import Sentence
from flair.models import SequenceTagger

# load the trained model
model = SequenceTagger.load("kalawinka/flair-ner-acknowledgments")

# create example sentence
sentence = Sentence("This work was supported by State Key Lab of Ocean Engineering Shanghai Jiao Tong University and financially supported by China National Scientific and Technology Major Project (Grant No. 2016ZX05028-006-009)")

# predict the tags
model.predict(sentence)
#print output as spans
for entity in sentence.get_spans('ner'):
    print(entity)

This produces the following output:

See our Google colab notebook

Citation

if you use this model, please consider citing this work:

@misc{smirnova2023embedding,
      title={Embedding Models for Supervised Automatic Extraction and Classification of Named Entities in Scientific Acknowledgements}, 
      author={Nina Smirnova and Philipp Mayr},
      year={2023},
      eprint={2307.13377},
      archivePrefix={arXiv},
      primaryClass={cs.DL}
}