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Entity Resolution On Amazon Google

Metrics

F1 (%)

Results

Performance results of various models on this benchmark

Model Name	F1 (%)	Paper Title	Repository
RoBERTa-SupCon	79.28	Supervised Contrastive Learning for Product Matching
Random Forest	79.0	Profiling Entity Matching Benchmark Tasks
text-davinci-002_zeroshot	54.30	Can Foundation Models Wrangle Your Data?
CorDEL-Sum	70.2	CorDEL: A Contrastive Deep Learning Approach for Entity Linkage	-
gpt-4o-mini-2024-07-18	59.20	Fine-tuning Large Language Models for Entity Matching
DeepMatcher - Hybrid	69.3	Deep Learning for Entity Matching: A Design Space Exploration
D-HAT	67.5	Deduplication Over Heterogeneous Attribute Types (D-HAT)
gpt-4o-mini-2024-07-18_fine_tuned	80.25	Fine-tuning Large Language Models for Entity Matching
gpt4-0613_fewshot-10	85.21	Entity Matching using Large Language Models
Meta-Llama-3.1-70B-Instruct	51.44	Fine-tuning Large Language Models for Entity Matching
Meta-Llama-3.1-8B-Instruct_fine_tuned	50.00	Fine-tuning Large Language Models for Entity Matching
HG	76.4	Entity Resolution with Hierarchical Graph Attention Networks
Meta-Llama-3.1-8B-Instruct	49.16	Fine-tuning Large Language Models for Entity Matching
gpt-4o-2024-08-06	63.45	Fine-tuning Large Language Models for Entity Matching
RobEM	79.06	Probing the Robustness of Pre-trained Language Models for Entity Matching
text-davinci-002_fewshot-10	63.50	Can Foundation Models Wrangle Your Data?
Ditto	75.58	Deep Entity Matching with Pre-Trained Language Models

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