Update README.md

README.md

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 license: apache-2.0
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-In this project, we have refined the capabilities of a pre-existing model to assess the Big Five personality traits for a given text/sentence. By meticulously fine-tuning this model using a specially curated dataset tailored for personality traits, it has learned to correlate specific textual inputs with distinct personality characteristics. This targeted approach has significantly enhanced the model's precision in identifying the Big Five personality traits from text, outperforming other models that were developed or fine-tuned on more generalized datasets.
+In this project, we have refined the capabilities of a pre-existing model to assess **the Big Five personality traits** for a given text/sentence. By meticulously fine-tuning this model using a specially curated dataset tailored for personality traits, it has learned to correlate specific textual inputs with distinct personality characteristics. This targeted approach has significantly enhanced the model's precision in identifying the Big Five personality traits from text, outperforming other models that were developed or fine-tuned on more generalized datasets.
 
-The accuracy reaches 79%, and F1 score is 78%. Both are much higher than the similar personality-detection models hosted in huggingface. In other words, our model remarkably outperforms other models.
-Due to the fact that the output values are continuous, it is better to use mean squared errors or mean absolute error to evaluate the model's performance. 
-When both metrics are smaller, it indciates that the model performs better. Our models performance: Overall Mean Squared Error: 0.07532746344804764, Mean Absolute Error: 0.1635677814483642
+The **accuracy** reaches 79%, and **F1 score** is 78%. Both are much higher than the similar personality-detection models hosted in huggingface. In other words, our model remarkably outperforms other models.
+Due to the fact that the output values are continuous, it is better to use mean squared errors (MSE) or mean absolute error (MAE) to evaluate the model's performance. 
+When both metrics are smaller, it indciates that the model performs better. Our models performance: **MSE: 0.071**, **MAE: 0.14**.
 
 Please **cite**: "Wang, R., and Sun, K. 2024. Personality Detection Models with Continuous Ouput Values Trained by Mixed Strategies" if you use this model.