readme: ---
  language:
  - hu
  tags:
  - token-classification
  license: apache-2.0
  metrics:
  - f1
  widget:
  - text: >-
      A Kovácsné Nagy Erzsébet nagyon jól érzi magát a Nokiánál, azonban a
      Németországból érkezett Kovács Péter nehezen boldogul a beilleszkedéssel.
  ---
  
  # Hungarian Named Entity Recognition Model with huBERT
  
  For further models, scripts and details, see [our demo site](https://juniper.nytud.hu/demo/nlp).
  
    - Pretrained model used: SZTAKI-HLT/hubert-base-cc
    - Finetuned on [NYTK-NerKor](https://github.com/nytud/NYTK-NerKor)
    - NE categories are: PER, LOC, MISC, ORG
    	
  ## Limitations
  
  - max_seq_length = 128
  
  ## Results
  
  F-score: **90.18%**
  
  ## Usage with pipeline
  
  ```python
  from transformers import pipeline
  
  ner = pipeline(task="ner", model="NYTK/named-entity-recognition-nerkor-hubert-hungarian")
  input_text = "A Kovácsné Nagy Erzsébet nagyon jól érzi magát a Nokiánál, azonban a Németországból érkezett Kovács Péter nehezen boldogul a beilleszkedéssel."
  
  print(ner(input_text, aggregation_strategy="simple"))
  ```
  
  ## Citation
  If you use this model, please cite the following paper:
  
  ```
  @inproceedings {yang-language-models,
      title = {Training language models with low resources: RoBERTa, BART and ELECTRA experimental models for Hungarian},
  	booktitle = {Proceedings of 12th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2021)},
  	year = {2021},
  	publisher = {IEEE},
  	address = {Online},
  	author = {Yang, Zijian Győző and Váradi, Tamás},
  	pages = {279--285}
  }
  
  ```