Instructions to use MahmutCanBoran/bertbank with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MahmutCanBoran/bertbank with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MahmutCanBoran/bertbank")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MahmutCanBoran/bertbank") model = AutoModelForSequenceClassification.from_pretrained("MahmutCanBoran/bertbank") - Notebooks
- Google Colab
- Kaggle
bertbank
This model is a fine-tuned version of dbmdz/bert-base-turkish-cased on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2
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Model tree for MahmutCanBoran/bertbank
Base model
dbmdz/bert-base-turkish-cased