Update README.md

README.md

@@ -12,6 +12,8 @@ language:
 - tr
 datasets:
 - winvoker/turkish-sentiment-analysis-dataset
+widget:
+  - text: "Bu bir örnek metindir. Lütfen kendi cümlenizi deneyin."
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -33,49 +35,12 @@ It achieves the following results on the evaluation set:
 </ul>
 
 ## Example
-```markdown
+```python
 from transformers import pipeline
 text = "senden nefret ediyorum"
 model_id = "saribasmetehan/bert-base-turkish-sentiment-analysis"
-classifer = pipeline("text-classification",model = model_id)
-preds= classifer(text)
+classifier = pipeline("text-classification", model=model_id)
+preds = classifier(text)
 print(preds)
 
 #[{'label': 'LABEL_2', 'score': 0.7510055303573608}]
-```
-# Load model directly
-```markdown
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-tokenizer = AutoTokenizer.from_pretrained("saribasmetehan/bert-base-turkish-sentiment-analysis")
-model = AutoModelForSequenceClassification.from_pretrained("saribasmetehan/bert-base-turkish-sentiment-analysis")
-```
-## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 4
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.1902        | 1.0   | 625  | 0.1629          | 0.9575   |
-| 0.1064        | 2.0   | 1250 | 0.1790          | 0.96     |
-| 0.0631        | 3.0   | 1875 | 0.2358          | 0.96     |
-| 0.0146        | 4.0   | 2500 | 0.2458          | 0.962    |
-
-
-### Framework versions
-
-- Transformers 4.40.2
-- Pytorch 2.2.1+cu121
-- Datasets 2.19.1
-- Tokenizers 0.19.1