Models and datasets of our EMNLP 2024 paper "PairDistill: Pairwise Relevance Distillation for Dense Retrieval"
Chao-Wei Huang
chaoweihuang
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Collections
2
models
6
chaoweihuang/PairDistill-colbertv2
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11
chaoweihuang/FactAlign-Phi-3-Mini
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chaoweihuang/FactAlign-gemma-2b-sft
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chaoweihuang/FactAlign-LLaMA-3-8B
Text Generation
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23
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1
chaoweihuang/mistral-7B-pairwise-feedback
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chaoweihuang/mt5-xl-lm-adapt
Text2Text Generation
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18
datasets
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chaoweihuang/PairDistill-dataset
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chaoweihuang/factalign-llama3-f1_0.8-fg0.5
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2.33k
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chaoweihuang/factalign-phi3-f1_0.7-fg0.5
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chaoweihuang/factalign-gemma2-f1_0.75
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2.56k
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127
chaoweihuang/lf-response-llama3-f1_100_0.8-fg1.0
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chaoweihuang/synthetic-instruct-gptj_pairwise
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chaoweihuang/summarize_from_feedback_pairwise
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92.9k
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chaoweihuang/hh-rlhf_pairwise
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chaoweihuang/SHP_pairwise
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75