fixed the typos

README.md

@@ -36,21 +36,27 @@ This model is a fine-tuned RoBERTa-based classifier designed to predict the pres
 The model can be directly used for classifying text into the following moral foundations:
 
 **Care**: Care/harm for others, protecting them from harm.
+
 **Fairness**: Justice, treating others equally.
+
 **Loyalty**: Group loyalty, patriotism, self-sacrifice for the group.
+
 **Authority**: Respect for tradition and legitimate authority.
+
 **Sanctity**: Disgust, avoiding dangerous diseases and contaminants.
 
 Each foundation is represented as a virtue (positive expression) and a vice (negative expression).
 
- It's particularly useful for researchers, policymakers, and analysts interested in understanding moral reasoning and rhetoric in different contexts.
+It's particularly useful for researchers, policymakers, and analysts interested in understanding moral reasoning and rhetoric in different contexts.
 
 ### Downstream Use
 
 Potential downstream uses include:
 
 **Content analysis**: Analyzing the moral framing of news articles, social media posts, or other types of text.
+
 **Opinion mining**: Understanding the moral values underlying people's opinions and arguments.
+
 **Ethical assessment**: Evaluating the ethical implications of decisions, policies, or products.
 
 ### Out-of-Scope Use
@@ -116,12 +122,12 @@ The trainng data is a subset of >60M sentences.
 
 The model was fine-tuned using the HuggingFace Transformers library with the following hyperparameters:
 
-Num_train_epochs: 10
-Per_device_train_batch_size: 8
-Per_device_eval_batch_size: 8
-Learning rate: 3e-5
-Optimizer: AdamW
-Loss function: Binary Cross Entropy with Logits Loss
+* Num_train_epochs: 10
+* Per_device_train_batch_size: 8
+* Per_device_eval_batch_size: 8
+* Learning rate: 3e-5
+* Optimizer: AdamW
+* Loss function: Binary Cross Entropy with Logits Loss
 
 ## Evaluation
 
@@ -147,61 +153,61 @@ The model achieves high overall performance, with variations across different mo
 
 ***Per-class metrics:***
 
-care_virtue:
+* care_virtue:
   accuracy: 0.9954
   precision: 0.9779
   recall: 0.9758
   f1: 0.9769
 
-care_vice:
+* care_vice:
   accuracy: 0.9960
   precision: 0.9734
   recall: 0.9506
   f1: 0.9619
 
-fairness_virtue:
+* fairness_virtue:
   accuracy: 0.9974
   precision: 0.9786
   recall: 0.9645
   f1: 0.9715
 
-fairness_vice:
+* fairness_vice:
   accuracy: 0.9970
   precision: 0.9319
   recall: 0.8574
   f1: 0.8931
 
-loyalty_virtue:
+* loyalty_virtue:
   accuracy: 0.9945
   precision: 0.9811
   recall: 0.9780
   f1: 0.9795
 
-loyalty_vice:
+* loyalty_vice:
   accuracy: 0.9972
   precision: 1.0000
   recall: 0.0531
   f1: 0.1008
 
-authority_virtue:
+* authority_virtue:
   accuracy: 0.9914
   precision: 0.9621
   recall: 0.9683
   f1: 0.9652
 
-authority_vice:
+* authority_vice:
   accuracy: 0.9963
   precision: 0.9848
   recall: 0.5838
   f1: 0.7331
 
-sanctity_virtue:
+* sanctity_virtue:
   accuracy: 0.9963
   precision: 0.9640
   recall: 0.9458
   f1: 0.9548
 
-sanctity_vice:
+* sanctity_vice:
   accuracy: 0.9958
   precision: 0.9538
   recall: 0.8530
@@ -245,7 +251,7 @@ via my personal website. thx
 
 ## Citation
 
-*** If you use this model in your research or applications, please cite it as follows:***
+***If you use this model in your research or applications, please cite it as follows:***
 
 Ardag, M.M.  (2024) Moral Foundations Classifier. HuggingFace. https://doi.org/10.57967/hf/2774