Optical Character Recognition2020en

dart

Dataset from Papers With Code

Metrics:accuracy, cer, wer, f1

bert

#ModelScorePaper / CodeDate
1
T5B Baseline
0.951
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
2
FactT5B
0.951
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
3
FactJointGT
0.949
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
4
JointGT Baseline
0.949
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
5
GPT-2-Large (fine-tuning)
0.940Jul 2021
6
HTLM (fine-tuning)
0.940Jul 2021

bleu

#ModelScorePaper / CodeDate
1
T5B Baseline
48.47
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
2
FactT5B
48.37
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
3
JointGT Baseline
47.51
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
4
FactJointGT
47.39
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
5
HTLM (fine-tuning)
47.2Jul 2021
6
GPT-2-Large (fine-tuning)
47Jul 2021

bleurt

#ModelScorePaper / CodeDate
1
T5B Baseline
0.675
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
2
FactT5B
0.674
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
3
JointGT Baseline
0.673
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
4
FactJointGT
0.673
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
5
GPT-2-Large (fine-tuning)
0.400Jul 2021
6
HTLM (fine-tuning)
0.400Jul 2021

factspotter

#ModelScorePaper / CodeDate
1
FactT5B
97.6
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
2
FactJointGT
97.25
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
3
T5B Baseline
96.65
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
4
JointGT Baseline
95.86
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023

meteor

#ModelScorePaper / CodeDate
1
T5B Baseline
0.407
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
2
FactT5B
0.407
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
3
JointGT Baseline
0.404
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
4
FactJointGT
0.403
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode
Oct 2023
5
HTLM (fine-tuning)
0.390Jul 2021
6
GPT-2-Large (fine-tuning)
0.390Jul 2021

mover

#ModelScorePaper / CodeDate
1
HTLM (fine-tuning)
0.510Jul 2021
2
GPT-2-Large (fine-tuning)
0.510Jul 2021

ter

#ModelScorePaper / CodeDate
1
GPT-2-Large (fine-tuning)
0.460Jul 2021
2
HTLM (fine-tuning)
0.440Jul 2021

Related Papers1

HTLM: Hyper-Text Pre-Training and Prompting of Language Models
Jul 2021Models: GPT-2-Large (fine-tuning), HTLM (fine-tuning)

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