Update README.md

README.md

@@ -1,10 +1,53 @@
 ---
 library_name: peft
+language: tr
 ---
 ## 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).
 
-- PEFT 0.4.0
+# 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 = 7 
+weight_decay = 0.01
+```
+# PEFT Parameters
+```
+inference_mode=False
+r=16
+lora_alpha=16
+lora_dropout=0.1
+bias="all"
+```
+# How to use: 
+```
+peft_model_id = "akdeniz27/bert-base-turkish-cased-ner-lora"
+config = PeftConfig.from_pretrained(peft_model_id)
+inference_model = AutoModelForTokenClassification.from_pretrained(
+    config.base_model_name_or_path, num_labels=7, id2label=id2label, label2id=label2id
+)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+model = PeftModel.from_pretrained(inference_model, peft_model_id)
+text = "Mustafa Kemal Atatürk 1919 yılında Samsun'a çıktı."
+inputs = tokenizer(text, return_tensors="pt")
+with torch.no_grad():
+    logits = model(**inputs).logits
+tokens = inputs.tokens()
+predictions = torch.argmax(logits, dim=2)
+for token, prediction in zip(tokens, predictions[0].numpy()):
+    print((token, model.config.id2label[prediction]))
+```
 
-- PEFT 0.4.0
+# Reference test results:
+* accuracy: 0.993297
+* f1: 0.949696
+* precision: 0.942554
+* recall: 0.956946