readme: ---
  language: tr
  widget:
  - text: "Almanya, koronavirüs aşısını geliştiren Dr. Özlem Türeci ve eşi Prof. Dr. Uğur Şahin'e liyakat nişanı verdi"
  ---
  # Turkish Named Entity Recognition (NER) Model
  This model is the fine-tuned model of dbmdz/convbert-base-turkish-cased (ConvBERTurk)
  using a reviewed version of well known Turkish NER dataset
   
  (https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt).
  
  The ConvBERT architecture is presented in the ["ConvBERT: Improving BERT with Span-based Dynamic Convolution"](https://arxiv.org/abs/2008.02496) paper.
  
  # Fine-tuning parameters:
  ```
  task = "ner"
  model_checkpoint = "dbmdz/convbert-base-turkish-cased"
  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 = 3 
  weight_decay = 0.01 
  ```
  # How to use: 
  ```
  model = AutoModelForTokenClassification.from_pretrained("akdeniz27/convbert-base-turkish-cased-ner")
  tokenizer = AutoTokenizer.from_pretrained("akdeniz27/convbert-base-turkish-cased-ner")
  ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="first")
  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:
  * accuracy: 0.9937648915431506
  * f1: 0.9610945644080416
  * precision: 0.9619899385131359
  * recall: 0.9602008554956295