Update README.md

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README.md

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 ---
 license: apache-2.0
+language:
+- tr
+metrics:
+- accuracy
+- f1
+base_model:
+- dbmdz/bert-base-turkish-cased
+pipeline_tag: text-classification
 ---
+# byunal/bert-base-turkish-cased-stance
+
+![Model card](https://huggingface.co/front/assets/huggingface_logo.svg)
+
+This repository contains a fine-tuned BERT model for stance detection in Turkish. The base model for this fine-tuning is [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased). The model has been specifically trained on a uniquely collected Turkish stance detection dataset.
+
+## Model Description
+
+- **Model Name**: byunal/bert-base-turkish-cased-stance
+- **Base Model**: [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased)
+- **Task**: Stance Detection
+- **Language**: Turkish
+
+The model predicts the stance of a given text towards a specific target. Possible stance labels include:
+
+- **Favor**: The text supports the target
+- **Against**: The text opposes the target
+- **Neutral**: The text does not express a clear stance on the target
+
+## Installation
+
+To install the necessary libraries and load the model, run:
+
+```bash
+pip install transformers
+```
+
+## Usage
+Here’s a simple example of how to use the model for stance detection in Turkish:
+
+```bash
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Load the model and tokenizer
+model_name = "byunal/bert-base-turkish-cased-stance"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example text
+text = "Bu konu hakkında kesinlikle karşıyım."
+
+# Tokenize input
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+# Perform prediction
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# Get predicted stance
+predictions = torch.argmax(outputs.logits, dim=-1)
+stance_label = predictions.item()
+
+# Display result
+labels = ["Favor", "Against", "Neutral"]
+print(f"The stance is: {labels[stance_label]}")
+```
+
+## Training
+This model was fine-tuned using a specialized Turkish stance detection dataset that uniquely reflects various text contexts and opinions. The dataset includes diverse examples from social media, news articles, and public comments, ensuring a robust understanding of stance detection in real-world applications.
+
+- Epochs: 10
+- Batch Size: 32
+- Learning Rate: 5e-5
+- Optimizer: AdamW
+
+## Evaluation
+The model was evaluated using Accuracy and Macro F1-score on a validation dataset. The results confirm the model's effectiveness in stance detection tasks in Turkish.
+- Accuracy Score: % 81.0 
+- Macro F1 Score: % 81.0