Create README.md

README.md

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+---
+language: ro
+datasets:
+- ronecv2
+license: mit
+---
+# bert-base-romanian-ner
+
+## 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](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.  
+
+### 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.
+
+```python
+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 = "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
+
+Install package
+Use named_persons_only
+
+
+#### 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
+metric|dev|test
+-|-|-
+f1 |95.1 |91.3
+precision |95.0 |90.7
+recall |95.3 |91.9
+
+## Corpus details
+
+The corpus has the following classes and distribution in the train/valid/test splits:
+
+| Classes      	| Total  	    | Train  	|         	| Valid  	|         	| Test   	|         	|
+|-------------	|:------:	    |:------:	|:-------:	|:------:	|:-------:	|:------:	|:-------:	|
+|            	| #     	    | #     	| %     	| # 	    | % 	    | #     	| %     	|
+| PERSON      	|  **26130** 	| 19167  	|  73.35  	|  2733  	|  10.46  	|  4230  	|  16.19  	|
+| GPE         	|  **11103** 	|  8193  	|  73.79  	|  1182  	|  10.65  	|  1728  	|   15.56 	|
+| LOC         	|  **2467**  	|  1824  	|  73.94  	|  270   	|  10.94  	|  373   	|  15.12  	|
+| ORG         	|  **7880**  	|  5688  	|  72.18  	|   880  	|  11.17  	|  1312  	|  16.65  	|
+| LANGUAGE    	|   **467**  	|   342  	|  73.23  	|   52   	|  11.13  	|   73   	|  15.63  	|
+| NAT_REL_POL 	|  **4970**  	|  3673  	|  73.90  	|   516  	|  10.38  	|   781  	|  15.71  	|
+| DATETIME    	|  **9614**  	|  6960  	|  72.39  	|  1029  	|   10.7  	|  1625  	|   16.9  	|
+| PERIOD      	|  **1188**  	|   862  	|  72.56  	|   129  	|  10.86  	|   197  	|  16.58  	|
+| QUANTITY    	|  **1588**  	|  1161  	|  73.11  	|   181  	|   11.4  	|   246  	|  15.49  	|
+| MONEY       	|  **1424**  	|  1041  	|  73.10  	|   159  	|  11.17  	|   224  	|  15.73  	|
+| NUMERIC     	|  **7735**  	|  5734  	|  74.13  	|   814  	|  10.52  	|  1187  	|  15.35  	|
+| ORDINAL     	|  **1893**  	|  1377  	|   72.74 	|   212  	|   11.2  	|   304  	|  16.06  	|
+| FACILITY    	|  **1126**  	|   840  	|   74.6  	|   113  	|  10.04  	|   173  	|  15.36  	|
+| WORK_OF_ART 	|  **1596**  	|  1157  	|  72.49  	|   176  	|  11.03  	|   263  	|  16.48  	|
+| EVENT       	|  **1102**  	|   826  	|  74.95  	|   107  	|   9.71  	|   169  	|  15.34  	|
+
+
+
+### BibTeX entry and citation info
+
+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: 
+```
+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}
+}
+```