readme: ---
  language: tr
  datasets:
  - SUNLP-NER-Twitter
  ---
  
  # bert-loodos-sunlp-ner-turkish
  
  ## Introduction
  [bert-loodos-sunlp-ner-turkish] is a NER model that was fine-tuned from the loodos/bert-base-turkish-cased model on the SUNLP-NER-Twitter dataset. 
  
  ## Training data
  The model was trained on the SUNLP-NER-Twitter dataset (5000 tweets). The dataset can be found at https://github.com/SU-NLP/SUNLP-Twitter-NER-Dataset
  Named entity types are as follows:
  Person, Location, Organization, Time, Money, Product, TV-Show
  
  
  ## How to use bert-loodos-sunlp-ner-turkish with HuggingFace
  
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  
  tokenizer = AutoTokenizer.from_pretrained("busecarik/bert-loodos-sunlp-ner-turkish")
  model = AutoModelForTokenClassification.from_pretrained("busecarik/bert-loodos-sunlp-ner-turkish")
  ```
  
  ## Model performances on SUNLP-NER-Twitter test set (metric: seqeval)
  Precision|Recall|F1
  -|-|-
  84.66|84.36|84.51
  
  Classification Report
  
  Entity|Precision|Recall|F1
  -|-|-|-
  LOCATION|0.74|0.78|0.76
  MONEY|0.93|0.82|0.87
  ORGANIZATION|0.83|0.81|0.82
  PERSON|0.90|0.92|0.91
  PRODUCT|0.55|0.50|0.52
  TIME|0.91|0.87|0.89
  TVSHOW|0.63|0.58|0.54
  
  
  You can cite the following [paper](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.484.pdf), if you use this model:
  
  ```bibtex
  @InProceedings{ark-yeniterzi:2022:LREC,
    author    = {\c{C}ar\i k, Buse  and  Yeniterzi, Reyyan},
    title     = {A Twitter Corpus for Named Entity Recognition in Turkish},
    booktitle      = {Proceedings of the Language Resources and Evaluation Conference},
    month          = {June},
    year           = {2022},
    address        = {Marseille, France},
    publisher      = {European Language Resources Association},
    pages     = {4546--4551},
    url       = {https://aclanthology.org/2022.lrec-1.484}
  }
  ```
  