Create README.md

README.md

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+---
+language: tr
+widget:
+- text: "Almanya’nın Mainz kentinde Türk profesör Uğur Şahin ile eşi Özlem Türeci’nin kurduğu ve yönettiği biyoteknoloji şirketi BioNTech ile aşı sürecini sürdüren Pfizer’ın corona virüsü aşısı üretmeye başladığı belirtildi."
+---
+# 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/bert-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("text")
+# 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