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---
license: apache-2.0
datasets:
- alibayram/turkish_mmlu
language:
- tr
base_model:
- google-t5/t5-small
---
# fine-tuned-t5-small-turkish-mmlu
<!-- Provide a quick summary of what the model is/does. -->
The fine-tuned [T5-Small](https://huggingface.co/google-t5/t5-small) model is a question-answering model trained on the [Turkish MMLU](https://huggingface.co/datasets/alibayram/turkish_mmlu) dataset, which consists of questions from various academic and professional exams in Turkey, including KPSS and TUS. The model takes a Turkish question as input and generates the correct answer. It is designed to perform well on Turkish-language question-answering tasks, leveraging the structure of the T5 architecture to handle text-to-text transformations.
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
@dataset{bayram_2024_13378019,
author = {Bayram, M. Ali},
title = {{Turkish MMLU: Yapay Zeka ve Akademik Uygulamalar
İçin En Kapsamlı ve Özgün Türkçe Veri Seti}},
month = aug,
year = 2024,
publisher = {Zenodo},
version = {v1.2},
doi = {10.5281/zenodo.13378019},
url = {https://doi.org/10.5281/zenodo.13378019}
}
#### Training Hyperparameters
learning_rate=5e-5
per_device_train_batch_size=8
per_device_eval_batch_size=8
num_train_epochs=3
weight_decay=0.01
#### Training Results
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
Training loss was monitored to evaluate how well the model is learning and to avoid overfitting. In this case, after 3 epochs, the model achieved a training loss of 0.0749, reflecting its ability to generalize well to the given data.
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