Create README.md

README.md

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+---
+language: hu
+widget:
+- text: ""
+---
+# Turkish Named Entity Recognition (NER) Model
+This model is the fine-tuned model of SZTAKI-HLT/hubert-base-cc 
+using the famous WikiANN dataset presented
+in the "Cross-lingual Name Tagging and Linking for 282 Languages" paper.
+
+# Fine-tuning parameters:
+```
+task = "ner"
+model_checkpoint = "SZTAKI-HLT/hubert-base-cc"
+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/bert-base-hungarian-cased-ner")
+tokenizer = AutoTokenizer.from_pretrained("akdeniz27/bert-base-hungarian-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.
+```
+# Reference test results:
+* accuracy: 0.9774538310923768
+* f1: 0.9462099085573904
+* precision: 0.9425718667406271
+* recall: 0.9498761426661113