dslim_bert-base-NER

---

language: en datasets:

• conll2003 license: mit model-index:
• name: dslim/bert-base-NER results:
  • task: type: token-classification name: Token Classification dataset: name: conll2003 type: conll2003 config: conll2003 split: test metrics:
    • name: Accuracy type: accuracy value: 0.9118041001560013 verified: true
    • name: Precision type: precision value: 0.9211550382257732 verified: true
    • name: Recall type: recall value: 0.9306415698281261 verified: true
    • name: F1 type: f1 value: 0.9258740048459675 verified: true
    • name: loss type: loss value: 0.48325642943382263 verified: true

---

bert-base-NER

If my open source models have been useful to you, please consider supporting me in building small, useful AI models for everyone (and help me afford med school / help out my parents financially). Thanks!

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.

Available NER models

Model Name	Description	Parameters
distilbert-NER (NEW!)	Fine-tuned DistilBERT - a smaller, faster, lighter version of BERT	66M
bert-large-NER	Fine-tuned bert-large-cased - larger model with slightly better performance	340M
bert-base-NER-(uncased)	Fine-tuned bert-base, available in both cased and uncased versions	110M

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)
print(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.

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-MISC	Beginning of a miscellaneous entity right after another miscellaneous entity
I-MISC	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",
}