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--- |
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license: apache-2.0 |
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datasets: |
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- alibayram/turkish_mmlu |
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language: |
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- tr |
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base_model: |
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- google-t5/t5-small |
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--- |
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# fine-tuned-t5-small-turkish-mmlu |
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<!-- Provide a quick summary of what the model is/does. --> |
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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. |
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### Training Data |
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<!-- 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. --> |
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@dataset{bayram_2024_13378019, |
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author = {Bayram, M. Ali}, |
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title = {{Turkish MMLU: Yapay Zeka ve Akademik Uygulamalar |
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İçin En Kapsamlı ve Özgün Türkçe Veri Seti}}, |
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month = aug, |
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year = 2024, |
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publisher = {Zenodo}, |
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version = {v1.2}, |
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doi = {10.5281/zenodo.13378019}, |
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url = {https://doi.org/10.5281/zenodo.13378019} |
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} |
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#### Training Hyperparameters |
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learning_rate=5e-5 |
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per_device_train_batch_size=8 |
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per_device_eval_batch_size=8 |
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num_train_epochs=3 |
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weight_decay=0.01 |
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#### Training Results |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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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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