Update README.md

README.md

@@ -98,36 +98,6 @@ F1 Match:  False
 '''
 ```
 
-## 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`
-
-Returns True if the candidate answer is a match of any of the gold answers.
-
-**Parameters**
-
-- `reference_answer` (list of str): A list of gold (correct) answers to the question.
-- `candidate_answer` (str): The answer provided by a candidate that needs to be evaluated.
-- `question` (str): The question for which the answers are being evaluated.
-
-**Returns**
-
-- `boolean`: A boolean True/False signifying matches between reference or candidate answers.
-
-```python
-from qa_metrics.transformerMatcher import TransformerMatcher
-
-question = "Which movie is loosley based off the Brother Grimm's Iron Henry?"
-tm = TransformerMatcher("distilroberta")
-scores = tm.get_scores(reference_answer, candidate_answer, question)
-match_result = tm.transformer_match(reference_answer, candidate_answer, question)
-print("Score: %s; bert Match: %s" % (scores, match_result))
-'''
-Score: {'The Frog Prince': {'The movie "The Princess and the Frog" is loosely based off the Brother Grimm\'s "Iron Henry"': 0.6934309}, 'The Princess and the Frog': {'The movie "The Princess and the Frog" is loosely based off the Brother Grimm\'s "Iron Henry"': 0.7400551}}; TM Match: True
-'''
-```
-
 ## Efficient and Robust Question/Answer Type Evaluation
 #### 1. `get_highest_score`
 
@@ -195,6 +165,36 @@ print(pedant.get_score(reference_answer[1], candidate_answer, question))
 '''
 ```
 
+## 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`
+
+Returns True if the candidate answer is a match of any of the gold answers.
+
+**Parameters**
+
+- `reference_answer` (list of str): A list of gold (correct) answers to the question.
+- `candidate_answer` (str): The answer provided by a candidate that needs to be evaluated.
+- `question` (str): The question for which the answers are being evaluated.
+
+**Returns**
+
+- `boolean`: A boolean True/False signifying matches between reference or candidate answers.
+
+```python
+from qa_metrics.transformerMatcher import TransformerMatcher
+
+question = "Which movie is loosley based off the Brother Grimm's Iron Henry?"
+# Supported models: roberta-large, roberta, bert, distilbert, distilroberta
+tm = TransformerMatcher("roberta-large")
+scores = tm.get_scores(reference_answer, candidate_answer, question)
+match_result = tm.transformer_match(reference_answer, candidate_answer, question)
+print("Score: %s; bert Match: %s" % (scores, match_result))
+'''
+Score: {'The Frog Prince': {'The movie "The Princess and the Frog" is loosely based off the Brother Grimm\'s "Iron Henry"': 0.6934309}, 'The Princess and the Frog': {'The movie "The Princess and the Frog" is loosely based off the Brother Grimm\'s "Iron Henry"': 0.7400551}}; TM Match: True
+'''
+```
 
 ## Prompting LLM For Evaluation