task = "ner"
model_checkpoint = "ytu-ce-cosmos/modernbert-tr-base"
label_list = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']
learning_rate=2e-5,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
gradient_accumulation_steps=2,
num_train_epochs=3,
weight_decay=0.01,

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
model = AutoModelForTokenClassification.from_pretrained("akdeniz27/modenbert-tr-base-ner")
tokenizer = AutoTokenizer.from_pretrained("akdeniz27/modenbert-tr-base-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")