Create README.md

README.md

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+---
+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 model of "xlm-roberta-base"
+(a multilingual version of RoBERTa) 
+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 = "xlm-roberta-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 = 4 
+weight_decay = 0.01 
+```
+# How to use: 
+```
+model = AutoModelForTokenClassification.from_pretrained("akdeniz27/xlm-roberta-base-turkish-ner")
+tokenizer = AutoTokenizer.from_pretrained("akdeniz27/xlm-roberta-base-turkish-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: 0.9919343118732742
+* f1: 0.945422814532762
+* precision: 0.9366551398931153
+* recall: 0.9543561819346573