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distilbert-NER

Model description

distilbert-NER is the fine-tuned version of DistilBERT, which is a distilled variant of the BERT model. DistilBERT has fewer parameters than BERT, making it smaller, faster, and more efficient. distilbert-NER is specifically fine-tuned for the task of Named Entity Recognition (NER).

This model accurately identifies the same four types of entities as its BERT counterparts: location (LOC), organizations (ORG), person (PER), and Miscellaneous (MISC). Although it is a more compact model, distilbert-NER demonstrates a robust performance in NER tasks, balancing between size, speed, and accuracy.

The model was fine-tuned on the English version of the CoNLL-2003 Named Entity Recognition dataset, which is widely recognized for its comprehensive and diverse range of entity types.

Available NER models

Model Name Description Parameters
distilbert-NER 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

This model can be utilized with the Transformers pipeline for NER, similar to the BERT models.

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

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

The performance of distilbert-NER is linked to its training on the CoNLL-2003 dataset. Therefore, it might show limited effectiveness on text data that significantly differs from this training set. Users should be aware of potential biases inherent in the training data and the possibility of entity misclassification in complex sentences.

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 Score
Loss 0.0710
Precision 0.9202
Recall 0.9232
F1 0.9217
Accuracy 0.9810

The training and validation losses demonstrate a decrease over epochs, signaling effective learning. The precision, recall, and F1 scores are competitive, showcasing the model's robustness in NER tasks.

BibTeX entry and citation info

For DistilBERT:

@article{sanh2019distilbert,
  title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
  author={Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},
  journal={arXiv preprint arXiv:1910.01108},
  year={2019}
}

For the underlying BERT model:

@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 = {db

lp computer science bibliography, https://dblp.org}
}
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