Update README.md

README.md

@@ -11,6 +11,9 @@ metrics:
 model-index:
 - name: democracy-sentiment-analysis-turkish-roberta
   results: []
+license: mit
+language:
+- tr
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -28,16 +31,53 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model is fine-tuned from the base model cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual for sentiment analysis in Turkish, specifically focusing on democracy-related text. The model classifies texts into three sentiment categories:
+
+Positive
+Neutral
+Negative
 
 ## Intended uses & limitations
 
-More information needed
+This model is well-suited for analyzing sentiments in Turkish texts that discuss democracy, governance, and related political discourse.
 
 ## Training and evaluation data
 
-More information needed
+The training dataset consists of 30,000 rows gathered from various sources, including: Kaggle, Hugging Face, Ekşi Sözlük, and synthetic data generated using state-of-the-art LLMs.
+The dataset is multilingual in origin, with texts in English, Russian, and Turkish. All non-Turkish texts were translated into Turkish. The data represents a broad spectrum of democratic discourse from 30 different sources.
+
+## How to Use
+
+To use this model for sentiment analysis, you can leverage the Hugging Face `pipeline` for text classification as shown below:
+
+```python
+from transformers import pipeline
+
+# Load the model from Hugging Face
+sentiment_model = pipeline(model="yeniguno/democracy-sentiment-analysis-turkish-roberta", task='text-classification')
+
+# Example text input
+response = sentiment_model("En iyisi devletin tüm gücünü tek bir lidere verelim")
+
+# Print the result
+print(response)
+# [{'label': 'negative', 'score': 0.9617443084716797}]
+
+# Example text input
+response = sentiment_model("Birçok farklı sesin çıkması zaman alıcı ve karmaşık görünebilir, ancak demokrasinin getirdiği özgürlük ve çeşitlilik, toplumun gerçek gücüdür.")
+
+# Print the result
+print(response)
+# [{'label': 'positive', 'score': 0.958978533744812}]
+
+# Example text input
+response = sentiment_model("Bugün hava yağmurlu.")
+
+# Print the result
+print(response)
+# [{'label': 'neutral', 'score': 0.9915837049484253}]
 
+```
 ## Training procedure
 
 ### Training hyperparameters
@@ -67,4 +107,4 @@ The following hyperparameters were used during training:
 - Transformers 4.44.2
 - Pytorch 2.4.0+cu121
 - Datasets 2.21.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1