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  library_name: peft\
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  Base model: mistralai/Mistral-7B-v0.1\
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  pipeline_tag: text-generation\
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- --\
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- Description: Topic identification from a news article, with synthetic explanations.\
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- Original dataset: https://huggingface.co/datasets/ag_news\
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  ---\
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- Try querying this adapter for free in Lora Land at https://predibase.com/lora-land!\
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- The adapter_category is Explanation and the name is News Topic Identification Explained (ag_news)\
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  ---\
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- Sample input: Below is a news article. Please classify it under one of the following classes (World, Business, Sports, Sci/Tech) and provide a reasonable coherent explanation for why the article is classified as such. Please format your response as a JSON payload.\n\n### Article: US poverty rate climbs, along with number lacking health coverage (AFP) AFP - The number of Americans living in poverty or without health insurance grew last year, a government survey showed, adding potential dynamite in the battle for the White House.\n\n### JSON Response\
 
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  ---\
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- Sample output: {"text_label": "World", "explanation": "The article is classified as 'World' because it discusses the poverty rate and lack of health coverage in the United States. Although it is a domestic issue, it has potential implications for the upcoming presidential election, making it relevant to a global audience."}\
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  ---\
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- Try using this adapter yourself! \
 
 
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  ```
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  library_name: peft\
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  Base model: mistralai/Mistral-7B-v0.1\
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  pipeline_tag: text-generation\
 
 
 
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  ---\
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+ Description: Topic identification from a news article, with synthetic explanations.
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+ Original dataset: https://huggingface.co/datasets/ag_news
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  ---\
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+ Try querying this adapter for free in Lora Land at https://predibase.com/lora-land!
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+ The adapter_category is Explanation and the name is News Topic Identification Explained (ag_news)
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  ---\
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+ Sample input: Below is a news article. Please classify it under one of the following classes (World, Business, Sports, Sci/Tech) and provide a reasonable coherent explanation for why the article is classified as such. Please format your response as a JSON payload.\n\n### Article: US poverty rate climbs, along with number lacking health coverage (AFP) AFP - The number of Americans living in poverty or without health insurance grew last year, a government survey showed, adding potential dynamite in the battle for the White House.\n\n### JSON Response
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  ---\
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+ Sample output: {"text_label": "World", "explanation": "The article is classified as 'World' because it discusses the poverty rate and lack of health coverage in the United States. Although it is a domestic issue, it has potential implications for the upcoming presidential election, making it relevant to a global audience."}
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+ ---\
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+ Try using this adapter yourself!
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  ```
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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