---
library_name: transformers
language: tr
license: mit
widget:
- text: Mustafa Kemal Atatürk 19 Mayıs 1919'da Samsun'a çıktı.
base_model:
- artiwise-ai/modernbert-base-tr-uncased
---

# Turkish Named Entity Recognition (NER) Model

This model is the fine-tuned model of "artiwise-ai/modernbert-base-tr-uncased" 
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 = "artiwise-ai/modernbert-base-tr-uncased"
batch_size = 8 
label_list = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']
max_length = 8192
learning_rate = 2e-5 
num_train_epochs = 5 
weight_decay = 0.01 
```

# How to use: 
```
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
model = AutoModelForTokenClassification.from_pretrained("akdeniz27/modernbert-base-tr-uncased-ner")
tokenizer = AutoTokenizer.from_pretrained("akdeniz27/modernbert-base-tr-uncased-ner")
# tokenizer.model_max_length = 512 # Model max_length could be set here (max 8192 as default)
ner = pipeline("token-classification", 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: 0.9910922551637875
* f1: 0.9323197128075177
* precision: 0.9292780467270049
* recall: 0.9353813559322034