paper-with-me

Entity Resolution 벤치마크

Entity Resolution on Amazon-Google

34개 결과 · ⬇ CSV · JSON

F1 (%)

49.16 58.17 67.19 76.2 85.21 2018-05 2026-09 DeepMatcher - Hybrid — 69.3 (2018-05-01) DeepMatcher - Hybrid — 69.3 (2018-05-01) Ditto — 75.58 (2020-04-01) Ditto — 75.58 (2020-04-01) CorDEL-Sum — 70.2 (2020-09-15) CorDEL-Sum — 70.2 (2020-09-15) Random Forest — 79.0 (2020-10-19) Random Forest — 79.0 (2020-10-19) RoBERTa-SupCon — 79.28 (2022-02-04) RoBERTa-SupCon — 79.28 (2022-02-04) text-davinci-002_fewshot-10 — 63.5 (2022-05-20) text-davinci-002_zeroshot — 54.3 (2022-05-20) text-davinci-002_fewshot-10 — 63.5 (2022-05-20) text-davinci-002_zeroshot — 54.3 (2022-05-20) HG — 76.4 (2022-06-01) HG — 76.4 (2022-06-01) RobEM — 79.06 (2022-10-01) RobEM — 79.06 (2022-10-01) D-HAT — 67.5 (2022-11-24) D-HAT — 67.5 (2022-11-24) gpt4-0613_fewshot-10 — 85.21 (2023-10-17) gpt4-0613_fewshot-10 — 85.21 (2023-10-17) gpt-4o-mini-2024-07-18_fine_tuned — 80.25 (2024-09-12) gpt-4o-2024-08-06 — 63.45 (2024-09-12) gpt-4o-mini-2024-07-18 — 59.2 (2024-09-12) Meta-Llama-3.1-70B-Instruct — 51.44 (2024-09-12) Meta-Llama-3.1-8B-Instruct_fine_tuned — 50.0 (2024-09-12) Meta-Llama-3.1-8B-Instruct — 49.16 (2024-09-12) gpt-4o-mini-2024-07-18_fine_tuned — 80.25 (2024-09-12) gpt-4o-2024-08-06 — 63.45 (2024-09-12) gpt-4o-mini-2024-07-18 — 59.2 (2024-09-12) Meta-Llama-3.1-70B-Instruct — 51.44 (2024-09-12) Meta-Llama-3.1-8B-Instruct_fine_tuned — 50.0 (2024-09-12) Meta-Llama-3.1-8B-Instruct — 49.16 (2024-09-12) DeepMatcher - Hybrid — 69.3 (2018-05-01) Ditto — 75.58 (2020-04-01) Random Forest — 79.0 (2020-10-19) RoBERTa-SupCon — 79.28 (2022-02-04) gpt4-0613_fewshot-10 — 85.21 (2023-10-17)
RankModel F1 (%) PaperCodeYear
1 gpt4-0613_fewshot-10 85.21 Entity Matching using Large Language Models wbsg-uni-mannheim/matchgpt 2023
2 gpt-4o-mini-2024-07-18_fine_tuned 80.25 Fine-tuning Large Language Models for Entity Matching wbsg-uni-mannheim/tailormatch 2024
3 RoBERTa-SupCon 79.28 Supervised Contrastive Learning for Product Matching wbsg-uni-mannheim/contrastive-product-matching 2022
4 RobEM 79.06 Probing the Robustness of Pre-trained Language Models for Entity Matching makbn/robem 2022
5 Random Forest 79.0 Profiling Entity Matching Benchmark Tasks wbsg-uni-mannheim/EntityMatchingTaskProfiler 2020
6 HG 76.4 Entity Resolution with Hierarchical Graph Attention Networks CGCL-codes/HierGAT 2022
7 Ditto 75.58 Deep Entity Matching with Pre-Trained Language Models megagonlabs/ditto 2020
8 CorDEL-Sum 70.2 CorDEL: A Contrastive Deep Learning Approach for Entity Linkage 2020
9 DeepMatcher - Hybrid 69.3 Deep Learning for Entity Matching: A Design Space Exploration anhaidgroup/deepmatcher 2018
10 D-HAT 67.5 Deduplication Over Heterogeneous Attribute Types (D-HAT) Loujainl/D-HAT 2022
11 text-davinci-002_fewshot-10 63.50 Can Foundation Models Wrangle Your Data? fminference/flexgen · hazyresearch/fm_data_tasks 2022
12 gpt-4o-2024-08-06 63.45 Fine-tuning Large Language Models for Entity Matching wbsg-uni-mannheim/tailormatch 2024
13 gpt-4o-mini-2024-07-18 59.20 Fine-tuning Large Language Models for Entity Matching wbsg-uni-mannheim/tailormatch 2024
14 text-davinci-002_zeroshot 54.30 Can Foundation Models Wrangle Your Data? fminference/flexgen · hazyresearch/fm_data_tasks 2022
15 Meta-Llama-3.1-70B-Instruct 51.44 Fine-tuning Large Language Models for Entity Matching wbsg-uni-mannheim/tailormatch 2024
16 Meta-Llama-3.1-8B-Instruct_fine_tuned 50.00 Fine-tuning Large Language Models for Entity Matching wbsg-uni-mannheim/tailormatch 2024
17 Meta-Llama-3.1-8B-Instruct 49.16 Fine-tuning Large Language Models for Entity Matching wbsg-uni-mannheim/tailormatch 2024
18 gpt4-0613_fewshot-10 85.21 Entity Matching using Large Language Models wbsg-uni-mannheim/matchgpt 2023
19 gpt-4o-mini-2024-07-18_fine_tuned 80.25 Fine-tuning Large Language Models for Entity Matching wbsg-uni-mannheim/tailormatch 2024
20 RoBERTa-SupCon 79.28 Supervised Contrastive Learning for Product Matching wbsg-uni-mannheim/contrastive-product-matching 2022
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