Optical Character Recognition2020en
dart
Dataset from Papers With Code
Metrics:accuracy, cer, wer, f1
bert
| # | Model | Score | Paper / Code | Date |
|---|---|---|---|---|
| 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) | 0.940 | Jul 2021 | |
| 6 | HTLM (fine-tuning) | 0.940 | Jul 2021 |
bleu
| # | Model | Score | Paper / Code | Date |
|---|---|---|---|---|
| 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) | 47.2 | Jul 2021 | |
| 6 | GPT-2-Large (fine-tuning) | 47 | Jul 2021 |
bleurt
| # | Model | Score | Paper / Code | Date |
|---|---|---|---|---|
| 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) | 0.400 | Jul 2021 | |
| 6 | HTLM (fine-tuning) | 0.400 | Jul 2021 |
factspotter
| # | Model | Score | Paper / Code | Date |
|---|---|---|---|---|
| 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
| # | Model | Score | Paper / Code | Date |
|---|---|---|---|---|
| 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) | 0.390 | Jul 2021 | |
| 6 | GPT-2-Large (fine-tuning) | 0.390 | Jul 2021 |
mover
| # | Model | Score | Paper / Code | Date |
|---|---|---|---|---|
| 1 | HTLM (fine-tuning) | 0.510 | Jul 2021 | |
| 2 | GPT-2-Large (fine-tuning) | 0.510 | Jul 2021 |
ter
| # | Model | Score | Paper / Code | Date |
|---|---|---|---|---|
| 1 | GPT-2-Large (fine-tuning) | 0.460 | Jul 2021 | |
| 2 | HTLM (fine-tuning) | 0.440 | Jul 2021 |
Related Papers1
HTLM: Hyper-Text Pre-Training and Prompting of Language Models
Jul 2021Models: GPT-2-Large (fine-tuning), HTLM (fine-tuning)