Create README.md

README.md

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+---
+language:
+- tr
+base_model:
+- artiwise-ai/modernbert-base-tr-uncased
+pipeline_tag: text-classification
+library_name: transformers
+license: mit
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: ModernBERT-Sentiment-Classifier
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# ModernBERT-Turkish-Sentiment-Classifier
+
+This model is a fine-tuned version of artiwise-ai/modernbert-base-tr-uncased on a Fake News Detection dataset, which is constructed by combining various publicly available datasets, incorporating additional resources curated by me, and translating English-language data into Turkish using large language models.
+It achieves the following results on the evaluation set:
+- Loss: 0.280550
+- Accuracy: 0.956000
+- Precision: 0.956370
+- Recall: 0.956000
+- F1: 0.956175
+
+## Training and evaluation data
+
+The data cannot be shared.
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
+1	0.161400	0.163402	0.945000	0.945220	0.945000	0.945107
+2	0.034400	0.286277	0.942000	0.949040	0.942000	0.944231
+3	0.003100	0.280550	0.956000	0.956370	0.956000	0.95617
+
+
+### Framework versions
+
+- Transformers 4.52.2
+- Pytorch 2.6.0+cu124
+- Datasets 2.14.4
+- Tokenizers 0.21.1
+