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@@ -40,7 +40,7 @@ print("Exact Match: ", match_result)
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  ```
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  #### Transformer Match
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- Our fine-tuned BERT model is this repository. Our Package also supports downloading and matching directly. More Matching transformer models will be available πŸ”₯πŸ”₯πŸ”₯
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  ```python
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  from qa_metrics.transformerMatcher import TransformerMatcher
@@ -49,7 +49,7 @@ question = "who will take the throne after the queen dies"
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  tm = TransformerMatcher("distilroberta")
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  scores = tm.get_scores(reference_answer, candidate_answer, question)
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  match_result = tm.transformer_match(reference_answer, candidate_answer, question)
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- print("Score: %s; distilroberta Match: %s" % (scores, match_result))
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  ```
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  #### F1 Score
@@ -71,10 +71,10 @@ question = "who will take the throne after the queen dies"
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  cfm = CFMatcher()
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  scores = cfm.get_scores(reference_answer, candidate_answer, question)
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  match_result = cfm.cf_match(reference_answer, candidate_answer, question)
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- print("Score: %s; CF Match: %s" % (scores, match_result))
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  ```
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- If you find this repo avialable, please cite:
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  ```bibtex
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  @misc{li2024cfmatch,
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  title={CFMatch: Aligning Automated Answer Equivalence Evaluation with Expert Judgments For Open-Domain Question Answering},
@@ -86,10 +86,11 @@ If you find this repo avialable, please cite:
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  }
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  ```
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  ## Updates
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  - [01/24/24] πŸ”₯ The full paper is uploaded and can be accessed [here]([https://arxiv.org/abs/2310.14566](https://arxiv.org/abs/2401.13170)). The dataset is expanded and leaderboard is updated.
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  - 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.
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- - Now our model supports Distilroberta, a smaller and more robust matching model than Bert!
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  ## License
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  ```
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  #### Transformer Match
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+ Our fine-tuned BERT model is this repository. Our Package also supports downloading and matching directly. distilroberta, distilbert, and roberta are also supported now! πŸ”₯πŸ”₯πŸ”₯
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  ```python
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  from qa_metrics.transformerMatcher import TransformerMatcher
 
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  tm = TransformerMatcher("distilroberta")
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  scores = tm.get_scores(reference_answer, candidate_answer, question)
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  match_result = tm.transformer_match(reference_answer, candidate_answer, question)
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+ print("Score: %s; CF Match: %s" % (scores, match_result))
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  ```
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  #### F1 Score
 
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  cfm = CFMatcher()
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  scores = cfm.get_scores(reference_answer, candidate_answer, question)
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  match_result = cfm.cf_match(reference_answer, candidate_answer, question)
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+ print("Score: %s; bert Match: %s" % (scores, match_result))
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  ```
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+ If you find this repo avialable, please cite our paper:
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  ```bibtex
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  @misc{li2024cfmatch,
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  title={CFMatch: Aligning Automated Answer Equivalence Evaluation with Expert Judgments For Open-Domain Question Answering},
 
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  }
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  ```
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+
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  ## Updates
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  - [01/24/24] πŸ”₯ The full paper is uploaded and can be accessed [here]([https://arxiv.org/abs/2310.14566](https://arxiv.org/abs/2401.13170)). The dataset is expanded and leaderboard is updated.
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  - 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.
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+ - Now our model supports [distilroberta](https://huggingface.co/Zongxia/answer_equivalence_distilroberta), [distilbert](https://huggingface.co/Zongxia/answer_equivalence_distilbert), a smaller and more robust matching model than Bert!
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  ## License
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