bert-base-romanian-ner

Updated: 21.01.2022

Model description

bert-base-romanian-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 15 types of entities: persons, geo-political entities, locations, organizations, languages, national_religious_political entities, datetime, period, quantity, money, numeric, ordinal, facilities, works of art and events.

Specifically, this model is a bert-base-romanian-cased-v1 model that was fine-tuned on RONEC version 2.0, which holds 12330 sentences with over 0.5M tokens, to a total of 80.283 distinctly annotated entities. RONECv2 is a BIO2 annotated corpus, meaning this model will generate "B-" and "I-" style labels for entities.

The model will generate labels according to the following list: ['O', 'B-PERSON', 'I-PERSON', 'B-ORG', 'I-ORG', 'B-GPE', 'I-GPE', 'B-LOC', 'I-LOC', 'B-NAT_REL_POL', 'I-NAT_REL_POL', 'B-EVENT', 'I-EVENT', 'B-LANGUAGE', 'I-LANGUAGE', 'B-WORK_OF_ART', 'I-WORK_OF_ART', 'B-DATETIME', 'I-DATETIME', 'B-PERIOD', 'I-PERIOD', 'B-MONEY', 'I-MONEY', 'B-QUANTITY', 'I-QUANTITY', 'B-NUMERIC', 'I-NUMERIC', 'B-ORDINAL', 'I-ORDINAL', 'B-FACILITY', 'I-FACILITY']. Label 'O' represents Other.

How to use

There are 2 ways to use this model:

Directly in Transformers:

You can use this model with Transformers pipeline for NER; you will have to handle word tokenization in multiple subtokens cases with different labels.

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("dumitrescustefan/bert-base-romanian-ner")
model = AutoModelForTokenClassification.from_pretrained("dumitrescustefan/bert-base-romanian-ner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Alex cumpără un bilet pentru trenul 3118 în direcția Cluj cu plecare la ora 13:00."
ner_results = nlp(example)
print(ner_results)

Use in a Python package

pip install roner

Easy, takes care of word-token alignment, long sequences, etc. See details at https://github.com/dumitrescustefan/roner

Don't forget!

Remember to always sanitize your text! Replace s and t cedilla-letters to comma-letters before processing your text with these models, with :

text = text.replace("ţ", "ț").replace("ş", "ș").replace("Ţ", "Ț").replace("Ş", "Ș")

NER evaluation results

 'test/ent_type': 0.9276865720748901,
 'test/exact': 0.9118986129760742,
 'test/partial': 0.9356381297111511,
 'test/strict': 0.8921924233436584

Corpus details

The corpus has the following classes and distribution in the train/valid/test splits:

BibTeX entry and citation info

Please consider citing the following paper as a thank you to the authors of the RONEC, even if it describes v1 of the corpus and you are using a model trained on v2:

Dumitrescu, Stefan Daniel, and Andrei-Marius Avram. "Introducing RONEC--the Romanian Named Entity Corpus." arXiv preprint arXiv:1909.01247 (2019).

or in .bibtex format:

@article{dumitrescu2019introducing,
  title={Introducing RONEC--the Romanian Named Entity Corpus},
  author={Dumitrescu, Stefan Daniel and Avram, Andrei-Marius},
  journal={arXiv preprint arXiv:1909.01247},
  year={2019}
}