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@@ -38,6 +38,7 @@ We explore **continued pre-training on domain-specific corpora** for large langu
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  ### πŸ€— We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! πŸ€—
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  **************************** **Updates** ****************************
 
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  * 2024/1/16: πŸŽ‰ Our [research paper](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024!!!πŸŽ‰
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  * 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B.
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  * 2023/12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B.
@@ -94,10 +95,36 @@ print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
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  ```
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  ## Domain-Specific Tasks
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- To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
 
 
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  **Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.
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  ## Citation
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  If you find our work helpful, please cite us:
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  ```bibtex
 
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  ### πŸ€— We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! πŸ€—
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  **************************** **Updates** ****************************
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+ * 2024/4/2: Released the raw data splits (train and test) of all the evaluation datasets
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  * 2024/1/16: πŸŽ‰ Our [research paper](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024!!!πŸŽ‰
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  * 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B.
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  * 2023/12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B.
 
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  ```
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  ## Domain-Specific Tasks
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+
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+ ### Pre-templatized/Formatted Testing Splits
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+ To easily reproduce our prompting results, we have uploaded the filled-in zero/few-shot input instructions and output completions of the test each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
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  **Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.
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+ ### Raw Datasets
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+ We have also uploaded the raw training and testing splits, for facilitating fine-tuning or other usages:
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+ - [ChemProt](https://huggingface.co/datasets/AdaptLLM/ChemProt)
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+ - [RCT](https://huggingface.co/datasets/AdaptLLM/RCT)
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+ - [ConvFinQA](https://huggingface.co/datasets/AdaptLLM/ConvFinQA)
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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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+
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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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+ # PubmedQA:
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+ dataset = load_dataset('bigbio/pubmed_qa')
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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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+
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  ## Citation
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  If you find our work helpful, please cite us:
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  ```bibtex