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--- |
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language: ro |
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datasets: |
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- ronec |
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license: mit |
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--- |
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# bert-base-romanian-ner |
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Updated: 21.01.2022 |
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## Model description |
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**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. |
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Specifically, this model is a [bert-base-romanian-cased-v1](https://huggingface.co/dumitrescustefan/bert-base-romanian-cased-v1) model that was fine-tuned on [RONEC version 2.0](https://github.com/dumitrescustefan/ronec), 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. |
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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. |
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### How to use |
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There are 2 ways to use this model: |
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#### Directly in Transformers: |
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You can use this model with Transformers *pipeline* for NER; you will have to handle word tokenization in multiple subtokens cases with different labels. |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("dumitrescustefan/bert-base-romanian-ner") |
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model = AutoModelForTokenClassification.from_pretrained("dumitrescustefan/bert-base-romanian-ner") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "Alex cumpără un bilet pentru trenul 3118 în direcția Cluj cu plecare la ora 13:00." |
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ner_results = nlp(example) |
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print(ner_results) |
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``` |
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#### Use in a Python package |
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``pip install roner`` |
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Easy, takes care of word-token alignment, long sequences, etc. See details at [https://github.com/dumitrescustefan/roner](https://github.com/dumitrescustefan/roner) |
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#### Don't forget! |
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Remember to always sanitize your text! Replace _s_ and _t_ cedilla-letters to comma-letters **before processing your text** with these models, with : |
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``` |
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text = text.replace("ţ", "ț").replace("ş", "ș").replace("Ţ", "Ț").replace("Ş", "Ș") |
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``` |
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## NER evaluation results |
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``` |
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'test/ent_type': 0.9276865720748901, |
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'test/exact': 0.9118986129760742, |
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'test/partial': 0.9356381297111511, |
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'test/strict': 0.8921924233436584 |
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``` |
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## Corpus details |
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The corpus has the following classes and distribution in the train/valid/test splits: |
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| Classes | Total | Train | | Valid | | Test | | |
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|------------- |:------: |:------: |:-------: |:------: |:-------: |:------: |:-------: | |
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| | # | # | % | # | % | # | % | |
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| PERSON | **26130** | 19167 | 73.35 | 2733 | 10.46 | 4230 | 16.19 | |
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| GPE | **11103** | 8193 | 73.79 | 1182 | 10.65 | 1728 | 15.56 | |
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| LOC | **2467** | 1824 | 73.94 | 270 | 10.94 | 373 | 15.12 | |
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| ORG | **7880** | 5688 | 72.18 | 880 | 11.17 | 1312 | 16.65 | |
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| LANGUAGE | **467** | 342 | 73.23 | 52 | 11.13 | 73 | 15.63 | |
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| NAT_REL_POL | **4970** | 3673 | 73.90 | 516 | 10.38 | 781 | 15.71 | |
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| DATETIME | **9614** | 6960 | 72.39 | 1029 | 10.7 | 1625 | 16.9 | |
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| PERIOD | **1188** | 862 | 72.56 | 129 | 10.86 | 197 | 16.58 | |
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| QUANTITY | **1588** | 1161 | 73.11 | 181 | 11.4 | 246 | 15.49 | |
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| MONEY | **1424** | 1041 | 73.10 | 159 | 11.17 | 224 | 15.73 | |
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| NUMERIC | **7735** | 5734 | 74.13 | 814 | 10.52 | 1187 | 15.35 | |
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| ORDINAL | **1893** | 1377 | 72.74 | 212 | 11.2 | 304 | 16.06 | |
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| FACILITY | **1126** | 840 | 74.6 | 113 | 10.04 | 173 | 15.36 | |
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| WORK_OF_ART | **1596** | 1157 | 72.49 | 176 | 11.03 | 263 | 16.48 | |
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| EVENT | **1102** | 826 | 74.95 | 107 | 9.71 | 169 | 15.34 | |
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### BibTeX entry and citation info |
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Please consider citing the following [paper](https://arxiv.org/abs/1909.01247) 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: |
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``` |
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Dumitrescu, Stefan Daniel, and Andrei-Marius Avram. "Introducing RONEC--the Romanian Named Entity Corpus." arXiv preprint arXiv:1909.01247 (2019). |
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``` |
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or in .bibtex format: |
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``` |
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@article{dumitrescu2019introducing, |
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title={Introducing RONEC--the Romanian Named Entity Corpus}, |
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author={Dumitrescu, Stefan Daniel and Avram, Andrei-Marius}, |
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journal={arXiv preprint arXiv:1909.01247}, |
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year={2019} |
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} |
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``` |
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