How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("feature-extraction", model="Trendyol/tybert")
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("Trendyol/tybert")
model = AutoModelForMaskedLM.from_pretrained("Trendyol/tybert")
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TyBert Model

This repository provides a pretrained Bert model for Turkish by Trendyol, named TyBert. The model is useful for various natural language understanding tasks, such as text classification, named entity recognition, and more.

How to use

from transformers import BertTokenizer, BertModel

# Load the tokenizer and model
tokenizer = BertTokenizer.from_pretrained("Trendyol/tybert")
model = BertModel.from_pretrained("Trendyol/tybert")

# Define a sample text
text = "Filenin Sultanları ilk maçını 29 Temmuz'da Hollanda'ya karşı oynayacak."

# Tokenize and encode the input text
encoded_input = tokenizer(text, return_tensors='pt')

# Get the model's output
output = model(**encoded_input)

print(output)
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