Create README.md

README.md

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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 
+
+
+
+#### 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.