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@@ -64,23 +64,22 @@ We have also uploaded the raw training and testing splits, for facilitating fine
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  - [FiQA_SA](https://huggingface.co/datasets/AdaptLLM/FiQA_SA)
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  - [Headline](https://huggingface.co/datasets/AdaptLLM/Headline)
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  - [NER](https://huggingface.co/datasets/AdaptLLM/NER)
 
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- The other datasets used in our paper have already been available in huggingface, so you can directly load them with the following code
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  ```python
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  from datasets import load_dataset
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  # MQP:
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  dataset = load_dataset('medical_questions_pairs')
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-
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  # PubmedQA:
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  dataset = load_dataset('bigbio/pubmed_qa')
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-
 
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  # SCOTUS
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  dataset = load_dataset("lex_glue", 'scotus')
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-
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  # CaseHOLD
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  dataset = load_dataset("lex_glue", 'case_hold')
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-
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  # UNFAIR-ToS
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  dataset = load_dataset("lex_glue", 'unfair_tos')
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  ```
 
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  - [FiQA_SA](https://huggingface.co/datasets/AdaptLLM/FiQA_SA)
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  - [Headline](https://huggingface.co/datasets/AdaptLLM/Headline)
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  - [NER](https://huggingface.co/datasets/AdaptLLM/NER)
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+ - [FPB](https://huggingface.co/datasets/AdaptLLM/FPB)
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+ The other datasets used in our paper have already been available in huggingface, and you can directly load them with the following code:
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  ```python
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  from datasets import load_dataset
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  # MQP:
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  dataset = load_dataset('medical_questions_pairs')
 
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  # PubmedQA:
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  dataset = load_dataset('bigbio/pubmed_qa')
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+ # USMLE:
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+ dataset=load_dataset('GBaker/MedQA-USMLE-4-options')
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  # SCOTUS
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  dataset = load_dataset("lex_glue", 'scotus')
 
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  # CaseHOLD
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  dataset = load_dataset("lex_glue", 'case_hold')
 
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  # UNFAIR-ToS
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  dataset = load_dataset("lex_glue", 'unfair_tos')
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  ```