Update README.md

README.md

@@ -1,9 +1,45 @@
 ---
 library_name: peft
+language: tr
+widget:
+- text: "Mustafa Kemal Atatürk 19 Mayıs 1919'da Samsun'a çıktı."
 ---
 ## Training procedure
 
-### Framework versions
+This is a fine-tuned model of base model "dbmdz/bert-base-turkish-cased" using the Parameter Efficient Fine Tuning (PEFT) with Low-Rank Adaptation (LoRA) technique 
+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 = "dbmdz/bert-base-turkish-cased"
+batch_size = 16 
+label_list = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']
+max_length = 512 
+learning_rate = 1e-3 
+num_train_epochs = 10 
+weight_decay = 0.01
+```
+# PEFT Parameters
+```
+inference_mode=False
+r=16
+lora_alpha=16
+lora_dropout=0.1
+bias="all"
+```
+# How to use: 
+```
+model = AutoModelForTokenClassification.from_pretrained("akdeniz27/bert-base-turkish-cased-ner")
+tokenizer = AutoTokenizer.from_pretrained("akdeniz27/bert-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.
 
-- PEFT 0.4.0
+# Reference test results:
+* accuracy: 0.993104
+* f1: 0.948413
+* precision: 0.939789
+* recall: 0.957196