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@@ -3,9 +3,14 @@ license: mit
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  base_model: microsoft/deberta-v3-base
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  tags:
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  - generated_from_trainer
 
 
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  model-index:
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  - name: apricot_clustering_trivia_qa_deberta-v3-base_for_gpt-3.5-turbo-0125
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  results: []
 
 
 
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -13,11 +18,11 @@ should probably proofread and complete it, then remove this comment. -->
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  # apricot_clustering_trivia_qa_deberta-v3-base_for_gpt-3.5-turbo-0125
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- This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the trivia_qa dataset.
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  ## Model description
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- More information needed
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  ## Intended uses & limitations
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  base_model: microsoft/deberta-v3-base
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  tags:
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  - generated_from_trainer
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+ - calibration
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+ - uncertainty
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  model-index:
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  - name: apricot_clustering_trivia_qa_deberta-v3-base_for_gpt-3.5-turbo-0125
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  results: []
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+ datasets:
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+ - mandarjoshi/trivia_qa
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+ library_name: transformers
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  # apricot_clustering_trivia_qa_deberta-v3-base_for_gpt-3.5-turbo-0125
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+ This model is fine-tuned for black-box LLM calibration as part of the 🍑 Apricot paper ["Calibrating Large Language Models Using Their Generations Only"](https://arxiv.org/abs/2403.05973) (ACL 2024).
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  ## Model description
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+ This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) to predict the calibration score for the gpt-3.5-turbo-0125 model on the questions from the trivia_qa dataset. It uses the clustering type of calibration target score.
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  ## Intended uses & limitations
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