Instructions to use Trendyol/tybert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Trendyol/tybert with Transformers:
# 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") - Notebooks
- Google Colab
- Kaggle
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)
- Downloads last month
- 7