Update README.md

README.md

@@ -32,7 +32,7 @@ This project showcases the power of transfer learning in machine learning and hi
 
 The personality prediction model can be used directly by individuals who are interested in gaining insights into their own personality traits based on their input text. Users can input text and receive predictions for the Big Five personality traits.
 
-### Downstream Use [optional]
+### Downstream Use
 
 This model is not intended for downstream use or fine-tuning for specific tasks. It is designed as a standalone personality prediction model.
 
@@ -44,39 +44,39 @@ This model is not suitable for uses beyond personality prediction. It should not
 
 The personality prediction model, like any machine learning model, has certain limitations and potential biases that should be taken into account:
 
-  Limited Context: 
-  The model makes predictions based on input text alone and may not capture the full context of an individual's personality. It is important to consider that personality traits are influenced by various factors beyond textual expression.
-
-  Generalization: 
-  The model predicts personality traits based on patterns learned from a specific dataset. Its performance may vary when applied to individuals from different demographic or cultural backgrounds not well represented in the training data.
-
-  Ethical Considerations: 
-  Personality prediction models should be used responsibly, with an understanding that personality traits do not determine a person's worth or abilities. It is important to avoid making unfair judgments or discriminating against individuals based on predicted personality traits.
-
-  Privacy Concerns: 
-  The model relies on user-provided input text, which may contain sensitive or personal information. Users should exercise caution when sharing personal details and ensure the security of their data.
-
-  False Positives/Negatives: 
-  The model's predictions may not always align perfectly with an individual's actual personality traits. It is possible for the model to generate false positives (predicting a trait that is not present) or false negatives (missing a trait that is present).
+    Limited Context: 
+    The model makes predictions based on input text alone and may not capture the full context of an individual's personality. It is important to consider that personality traits are influenced by various factors beyond textual expression.
+  
+    Generalization: 
+    The model predicts personality traits based on patterns learned from a specific dataset. Its performance may vary when applied to individuals from different demographic or cultural backgrounds not well represented in the training data.
+  
+    Ethical Considerations: 
+    Personality prediction models should be used responsibly, with an understanding that personality traits do not determine a person's worth or abilities. It is important to avoid making unfair judgments or discriminating against individuals based on predicted personality traits.
+  
+    Privacy Concerns: 
+    The model relies on user-provided input text, which may contain sensitive or personal information. Users should exercise caution when sharing personal details and ensure the security of their data.
+  
+    False Positives/Negatives: 
+    The model's predictions may not always align perfectly with an individual's actual personality traits. It is possible for the model to generate false positives (predicting a trait that is not present) or false negatives (missing a trait that is present).
 
 ### Recommendations
 
 To mitigate risks and limitations associated with personality prediction models, the following recommendations are suggested:
 
-  Awareness and Education: 
-  Users should be informed about the limitations and potential biases of the model. Promote understanding that personality traits are complex and cannot be fully captured by a single model or text analysis.
-
-  Avoid Stereotyping and Discrimination: 
-  Users should be cautious about making judgments or decisions solely based on predicted personality traits. Personality predictions should not be used to discriminate against individuals or perpetuate stereotypes.
-
-  Interpret with Context: 
-  Interpret the model's predictions in the appropriate context and consider additional information about an individual beyond their input text.
-
-  Data Privacy and Security: 
-  Ensure that user data is handled securely and with respect to privacy regulations. Users should be aware of the information they provide and exercise caution when sharing personal details.
-
-  Promote Ethical Use: 
-  Encourage responsible use of personality prediction models and discourage misuse or harmful applications.
+    Awareness and Education: 
+    Users should be informed about the limitations and potential biases of the model. Promote understanding that personality traits are complex and cannot be fully captured by a single model or text analysis.
+  
+    Avoid Stereotyping and Discrimination: 
+    Users should be cautious about making judgments or decisions solely based on predicted personality traits. Personality predictions should not be used to discriminate against individuals or perpetuate stereotypes.
+  
+    Interpret with Context: 
+    Interpret the model's predictions in the appropriate context and consider additional information about an individual beyond their input text.
+  
+    Data Privacy and Security: 
+    Ensure that user data is handled securely and with respect to privacy regulations. Users should be aware of the information they provide and exercise caution when sharing personal details.
+  
+    Promote Ethical Use: 
+    Encourage responsible use of personality prediction models and discourage misuse or harmful applications.
 
 It is important to note that the above recommendations are general guidelines, and further context-specific recommendations should be developed based on the particular use case and ethical considerations.
 
@@ -174,7 +174,7 @@ You can call the personality_detection function with your input text to obtain t
 Please note that this code assumes you have already downloaded the necessary model files (config.json, pytorch_model.bin, special_tokens_map.json, tokenizer_config.json, vocab.txt
 ) and placed them in the current directory (indicated by "."). Adjust the paths and filenames accordingly if needed.
 
-#### Result Format [optional]
+#### Result Format
 
 The personality_detection function returns a dictionary containing the predicted personality traits based on the given input text.
 
@@ -192,7 +192,7 @@ personality_prediction = personality_detection(text_input)
 
 print(personality_prediction)
 ```
-#####Output:
+###### Output:
 ```python
 {
     "Extroversion": 0.535,
@@ -207,7 +207,7 @@ Note: The values in the example output are just placeholders and may not reflect
 
 You can modify the example code and the result format to match your specific use case and desired output format.
 
-## Citation [optional]
+## Citation
 
 @article{DBLP:journals/corr/abs-1810-04805,
   author    = {Jacob Devlin and
@@ -228,7 +228,7 @@ You can modify the example code and the result format to match your specific use
 }
 
 
-## More Information [optional]
+## More Information
 
 TBA