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README.md
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@@ -60,23 +60,22 @@ 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,
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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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```
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