Update README.md

README.md

@@ -1,12 +1,12 @@
 ---
 title: TweetGPT
-emoji: 🐦
-colorFrom: blue
+emoji: 🚀
+colorFrom: purple
 colorTo: blue
 sdk: streamlit
 sdk_version: 1.34.0
 app_file: app.py
-pinned: false
+pinned: true
 license: apache-2.0
 ---
 
@@ -144,33 +144,33 @@ We used Weights and Biases (WandB) for tracking and visualizing our machine lear
 ##### 4.5.1 Evaluation Metrics
 
 - **Steps per Second**
-![Steps per Second](.\WandB_Training_Evaluation\eval_stepspersecond.png)
+![Steps per Second](./WandB_Training_Evaluation/eval_stepspersecond.png)
 
 - **Runtime**
-![Runtime](.\WandB_Training_Evaluation\eval_runtime.png)
+![Runtime](./WandB_Training_Evaluation/eval_runtime.png)
 
 - **Samples per Second**
-![Samples per Second](.\WandB_Training_Evaluation\eval_samplespersecond.png)
+![Samples per Second](./WandB_Training_Evaluation/eval_samplespersecond.png)
 
 - **Loss**
-![Loss](.\WandB_Training_Evaluation\eval_loss.png)
+![Loss](./WandB_Training_Evaluation/eval_loss.png)
 
 ##### 4.5.2 Training Metrics
 
 - **Gradient Norm**
-![Gradient Norm](.\WandB_Training_Evaluation\train_gradnorm.png)
+![Gradient Norm](./WandB_Training_Evaluation/train_gradnorm.png)
 
 - **Global Step**
-![Global Step](.\WandB_Training_Evaluation\train_globalstep.png)
+![Global Step](./WandB_Training_Evaluation/train_globalstep.png)
 
 - **Loss**
-![Training Loss](.\WandB_Training_Evaluation\train_loss.png)
+![Training Loss](./WandB_Training_Evaluation/train_loss.png)
 
 - **Learning Rate**
-![Learning Rate](.\WandB_Training_Evaluation\train_learningrate.png)
+![Learning Rate](./WandB_Training_Evaluation/train_learningrate.png)
 
 - **Epoch**
-![Epoch](.\WandB_Training_Evaluation\train_epoch.png)
+![Epoch](./WandB_Training_Evaluation/train_epoch.png)
 
 ## 5 Discussion
 
@@ -212,4 +212,4 @@ Future research could explore:
 
 ## 6 Conclusion
 
-In this project, we analyzed the communication strategies of German political parties on Twitter using a fine-tuned GPT-2 model. Our results demonstrate the potential of NLP techniques in political communication analysis. Future research could build on these findings to explore more advanced applications and address the ethical implications of AI in social media.
+In this project, we analyzed the communication strategies of German political parties on Twitter using a fine-tuned GPT-2 model. Our results demonstrate the potential of NLP techniques in political communication analysis. Future research could build on these findings to explore more advanced applications and address the ethical implications of AI in social media.