---
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 dbmdz/bert-base-turkish-cased 
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 = 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-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.
```

# Reference test results:
* accuracy: 0.9933935699477056
* f1: 0.9592969472710453
* precision: 0.9543530277931161
* recall: 0.9642923563325274