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README.md

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 ---
 library_name: peft
-language: tr
-widget:
-- text: "Mustafa Kemal Atatürk 19 Mayıs 1919'da Samsun'a çıktı."
 ---
 ## Training procedure
 
-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).
+### Framework versions
 
-# 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
+- PEFT 0.4.0

adapter_model.bin

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