Model description

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.

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Wolfgang and I live in Berlin"

ner_results = nlp(example)

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.

Training data

This model was fine-tuned on English version of the standard CoNLL-2003 Named Entity Recognition dataset.

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

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

  author    = {Jacob Devlin and
               Ming{-}Wei Chang and
               Kenton Lee and
               Kristina Toutanova},
  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
  journal   = {CoRR},
  volume    = {abs/1810.04805},
  year      = {2018},
  url       = {},
  archivePrefix = {arXiv},
  eprint    = {1810.04805},
  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
  biburl    = {},
  bibsource = {dblp computer science bibliography,}
    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 = "",
    pages = "142--147",
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