Updated README

README.md

@@ -0,0 +1,66 @@
+---
+language: 
+- tr
+thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
+tags:
+- text-classification
+- emotion
+- pytorch
+datasets:
+- emotion (Translated to Turkish)
+metrics:
+- Accuracy, F1 Score
+---
+# distilbert-base-turkish-cased-emotion
+
+## Model description:
+[Distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters
+```
+ learning rate 2e-5, 
+ batch size 64,
+ num_train_epochs=8,
+```
+
+## Model Performance Comparision on Emotion Dataset from Twitter:
+
+| Model | Accuracy | F1 Score |  Test Sample per Second |
+| --- | --- | --- | --- |
+| [Distilbert-base-turkish-cased-emotion](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) | 83.25 | 83.17 | 232.197 |
+
+## How to Use the model:
+```python
+from transformers import pipeline
+classifier = pipeline("text-classification",model='zafercavdar/distilbert-base-turkish-cased-emotion', return_all_scores=True)
+prediction = classifier("Bu kütüphaneyi seviyorum, en iyi yanı kolay kullanımı.", )
+print(prediction)
+
+"""
+Output:
+[
+  [
+    {'label': 'sadness', 'score': 0.0026786490343511105},
+    {'label': 'joy', 'score': 0.6600754261016846},
+    {'label': 'love', 'score': 0.3203163146972656},
+    {'label': 'anger', 'score': 0.004358913749456406},
+    {'label': 'fear', 'score': 0.002354539930820465},
+    {'label': 'surprise', 'score': 0.010216088965535164}
+  ]
+]
+
+"""
+```
+
+## Dataset:
+[Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion).
+
+## Eval results
+```json
+{
+ 'eval_accuracy': 0.8325,
+ 'eval_f1': 0.8317301441160213,
+ 'eval_loss': 0.5021793842315674,
+ 'eval_runtime': 8.6167,
+ 'eval_samples_per_second': 232.108,
+ 'eval_steps_per_second': 3.714
+}
+```