bert-base-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC).
Specifically, this model is a bert-base-cased model that was fine-tuned on the English version of the standard CoNLL-2003 Named Entity Recognition dataset.
If you'd like to use a larger BERT-large model fine-tuned on the same dataset, a bert-large-NER version is also available.
Intended uses & limitations
How to use
You can use this model with Transformers pipeline for NER.
python
1from transformers import AutoTokenizer, AutoModelForTokenClassification
2from transformers import pipeline
34tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")5model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")67nlp = pipeline("ner", model=model, tokenizer=tokenizer)8example ="My name is Wolfgang and I live in Berlin"910ner_results = nlp(example)11print(ner_results)
Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases.
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation
Description
O
Outside of a named entity
B-MIS
Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS
Miscellaneous entity
B-PER
Beginning of a person’s name right after another person’s name
I-PER
Person’s name
B-ORG
Beginning of an organization right after another organization
I-ORG
organization
B-LOC
Beginning of a location right after another location
I-LOC
Location
CoNLL-2003 English Dataset Statistics
This dataset was derived from the Reuters corpus which consists of Reuters news stories. You can read more about how this dataset was created in the CoNLL-2003 paper.
# of training examples per entity type
Dataset
LOC
MISC
ORG
PER
Train
7140
3438
6321
6600
Dev
1837
922
1341
1842
Test
1668
702
1661
1617
# of articles/sentences/tokens per dataset
Dataset
Articles
Sentences
Tokens
Train
946
14,987
203,621
Dev
216
3,466
51,362
Test
231
3,684
46,435
Training procedure
This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original BERT paper which trained & evaluated the model on CoNLL-2003 NER task.
Eval results
metric
dev
test
f1
95.1
91.3
precision
95.0
90.7
recall
95.3
91.9
The test metrics are a little lower than the official Google BERT results which encoded document context & experimented with CRF. More on replicating the original results here.
BibTeX entry and citation info
@article{DBLP:journals/corr/abs-1810-04805,
author = {Jacob Devlin and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805},
timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
author = "Tjong Kim Sang, Erik F. and
De Meulder, Fien",
booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
year = "2003",
url = "https://www.aclweb.org/anthology/W03-0419",
pages = "142--147",
}
