distilbert-base-eng-cased-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.
Intended uses & limitations
How to use
This model can be utilized with the Transformers pipeline for NER, similar to the BERT models.
python
1from transformers import AutoTokenizer, AutoModelForTokenClassification
2from transformers import pipeline
34tokenizer = AutoTokenizer.from_pretrained("LukeGPT88/distilbert-base-eng-cased-ner")5model = AutoModelForTokenClassification.from_pretrained("LukeGPT88/distilbert-base-eng-cased-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
The performance of distilbert-base-eng-cased-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.
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.
@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}
}
