readme: ---
  language: tr
  widget:
  - text: "Mustafa Kemal Atatürk 19 Mayıs 1919'da Samsun'a çıktı."
  ---
  # Turkish Named Entity Recognition (NER) Model
  This model is the fine-tuned version of "microsoft/mDeBERTa-v3-base"
  (a multilingual version of DeBERTa V3) 
  using a reviewed version of well known Turkish NER dataset 
  (https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt).
  # Fine-tuning parameters:
  ```
  task = "ner"
  model_checkpoint = "microsoft/mdeberta-v3-base"
  batch_size = 8 
  label_list = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']
  max_length = 512 
  learning_rate = 2e-5 
  num_train_epochs = 2 
  weight_decay = 0.01 
  ```
  # How to use: 
  ```
  model = AutoModelForTokenClassification.from_pretrained("akdeniz27/mDeBERTa-v3-base-turkish-ner")
  tokenizer = AutoTokenizer.from_pretrained("akdeniz27/mDeBERTa-v3-base-turkish-ner")
  ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
  ner("<your text here>")
  ```
  Pls refer "https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html" for entity grouping with aggregation_strategy parameter.
  # Reference test results:
  * f1: 0.95
  * precision: 0.94
  * recall: 0.96