Minej
/

bert-base-personality

Text Classification

Inference Endpoints

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Minej commited on Jul 13, 2023

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@@ -18,7 +18,6 @@ By leveraging transfer learning and fine-tuning BERT BASE UNCASED, we accurately
 This project showcases the power of transfer learning in machine learning and highlights the effectiveness of BERT BASE UNCASED for predicting Big Five personality traits.
-- **Developed by:** Minej
 - **Model type:** BERT BASE UNCASED
 - **Language(s) (NLP):** English
 - **License:** MIT
@@ -45,7 +44,7 @@ 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:
@@ -181,11 +180,11 @@ The personality_detection function returns a dictionary containing the predicted
 The dictionary contains the following personality traits with their corresponding predicted values:
-    Extroversion: A value between 0 and 1 representing the predicted extroversion trait.
-    Neuroticism: A value between 0 and 1 representing the predicted neuroticism trait.
-    Agreeableness: A value between 0 and 1 representing the predicted agreeableness trait.
-    Conscientiousness: A value between 0 and 1 representing the predicted conscientiousness trait.
-    Openness: A value between 0 and 1 representing the predicted openness trait.
 ```python
 text_input = "I am feeling excited about the upcoming event."

 This project showcases the power of transfer learning in machine learning and highlights the effectiveness of BERT BASE UNCASED for predicting Big Five personality traits.
 - **Model type:** BERT BASE UNCASED
 - **Language(s) (NLP):** English
 - **License:** MIT
 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 dictionary contains the following personality traits with their corresponding predicted values:
+  Extroversion: A value between 0 and 1 representing the predicted extroversion trait.
+  Neuroticism: A value between 0 and 1 representing the predicted neuroticism trait.
+  Agreeableness: A value between 0 and 1 representing the predicted agreeableness trait.
+  Conscientiousness: A value between 0 and 1 representing the predicted conscientiousness trait.
+  Openness: A value between 0 and 1 representing the predicted openness trait.
 ```python
 text_input = "I am feeling excited about the upcoming event."