Update README.md

README.md

@@ -42,7 +42,7 @@ The python package currently provides six QA evaluation methods.
 - Question/Answer Type Evaluation and Transformer Neural evaluations are cost free and suitable for short-form and longer-form QA datasets. They have higher correlation with human judgments than exact match and F1 score when the length of the gold and candidate answers become long.
 - Black-box LLM evaluations are closest to human evaluations, and they are not cost-free.
 
-### Normalized Exact Match
+## Normalized Exact Match
 #### `em_match`
 
 Returns a boolean indicating whether there are any exact normalized matches between gold and candidate answers.
@@ -68,7 +68,7 @@ Exact Match:  False
 '''
 ```
 
-### F1 Score
+## F1 Score
 #### `f1_score_with_precision_recall`
 
 Calculates F1 score, precision, and recall between a reference and a candidate answer.
@@ -98,7 +98,7 @@ F1 Match:  False
 '''
 ```
 
-### Transformer Neural Evaluation
+## Transformer Neural Evaluation
 Our fine-tuned BERT model is on 🤗 [Huggingface](https://huggingface.co/Zongxia/answer_equivalence_bert?text=The+goal+of+life+is+%5BMASK%5D.). Our Package also supports downloading and matching directly. [distilroberta](https://huggingface.co/Zongxia/answer_equivalence_distilroberta), [distilbert](https://huggingface.co/Zongxia/answer_equivalence_distilbert), [roberta](https://huggingface.co/Zongxia/answer_equivalence_roberta), and [roberta-large](https://huggingface.co/Zongxia/answer_equivalence_roberta-large) are also supported now! 🔥🔥🔥
 
 #### `transformer_match`
@@ -128,7 +128,7 @@ Score: {'The Frog Prince': {'The movie "The Princess and the Frog" is loosely ba
 '''
 ```
 
-### Efficient and Robust Question/Answer Type Evaluation
+## Efficient and Robust Question/Answer Type Evaluation
 #### 1. `get_highest_score`
 
 Returns the gold answer and candidate answer pair that has the highest matching score. This function is useful for evaluating the closest match to a given candidate response based on a list of reference answers.
@@ -196,7 +196,7 @@ print(pedant.get_score(reference_answer[1], candidate_answer, question))
 ```
 
 
-#### Prompting LLM For Evaluation
+## Prompting LLM For Evaluation
 
 Note: The prompting function can be used for any prompting purposes.