Update README.md

README.md

@@ -27,6 +27,19 @@ match_result = em_match(reference_answer, candidate_answer)
 print("Exact Match: ", match_result)
 ```
 
+#### Transformer Match
+Our fine-tuned BERT model is repository. Our Package also supports downloading and matching directly. More Matching transformer models will be available 🔥🔥🔥
+
+```python
+from qa_metrics.transformerMatcher import TransformerMatcher
+
+question = "who will take the throne after the queen dies"
+tm = TransformerMatcher("bert")
+scores = tm.get_scores(reference_answer, candidate_answer, question)
+match_result = tm.transformer_match(reference_answer, candidate_answer, question)
+print("Score: %s; CF Match: %s" % (scores, match_result))
+```
+
 #### F1 Score
 ```python
 from qa_metrics.f1 import f1_match,f1_score_with_precision_recall
@@ -49,18 +62,6 @@ match_result = cfm.cf_match(reference_answer, candidate_answer, question)
 print("Score: %s; CF Match: %s" % (scores, match_result))
 ```
 
-#### Transformer Match
-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. More Matching transformer models will be available 🔥🔥🔥
-
-```python
-from qa_metrics.transformerMatcher import TransformerMatcher
-
-question = "who will take the throne after the queen dies"
-tm = TransformerMatcher("bert")
-scores = tm.get_scores(reference_answer, candidate_answer, question)
-match_result = tm.transformer_match(reference_answer, candidate_answer, question)
-print("Score: %s; CF Match: %s" % (scores, match_result))
-```
 
 ## Datasets
 Our Training Dataset is adapted and augmented from [Bulian et al](https://github.com/google-research-datasets/answer-equivalence-dataset). Our [dataset repo](https://github.com/zli12321/Answer_Equivalence_Dataset.git) includes the augmented training set and QA evaluation testing sets discussed in our paper.