Bert Base NER

🚀 ONNX convert of bert-base-NER

This project focuses on the ONNX conversion of the bert-base-NER model. It provides a ready - to - use solution for Named Entity Recognition (NER) with state - of - the - art performance.

🚀 Quick Start

Conversion of bert-base-NER

The core of this project is the conversion of the bert-base-NER model, enabling it to be used in more scenarios.

✨ Features

Model description

bert-base-NER is a fine - tuned BERT model designed for Named Entity Recognition. It achieves state - of - the - art performance in the NER task. This model has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER), and Miscellaneous (MISC).

Specifically, it is a bert - base - cased model fine - tuned on the English version of the standard [CoNLL - 2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03 - 0419.pdf) dataset.

If you prefer a larger model, the [bert - large - NER](https://huggingface.co/dslim/bert - large - NER/) version, fine - tuned on the same dataset, is also available.

Intended uses & limitations

How to use

You can utilize 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 restricted by its training dataset of entity - annotated news articles from a specific time span. It may not generalize well for all use cases in different domains. Additionally, the model sometimes tags subword tokens as entities, and post - processing of results may be required.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

The basic usage of the model for NER is as follows:

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)

📚 Documentation

Training data

This model was fine - tuned on the English version of the standard [CoNLL - 2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03 - 0419.pdf) dataset.

The training dataset differentiates between the beginning and continuation of an entity. Each token in the dataset is classified into one of the following classes:

CoNLL - 2003 English Dataset Statistics

This dataset is derived from the Reuters corpus, which consists of Reuters news stories. You can find more information about its creation in the CoNLL - 2003 paper.

# of training examples per entity type

# of articles/sentences/tokens per dataset

Training procedure

This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original BERT paper, which trained and evaluated the model on the CoNLL - 2003 NER task.

Eval results

The test metrics are slightly lower than the official Google BERT results, which encoded document context and experimented with CRF. More information on replicating the original results can be found [here](https://github.com/google - research/bert/issues/223).

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",
}

📄 License

This project is licensed under the MIT license.