Model card
GPT-2-Large (fine-tuning).
UnknownunknownUnknown paramsUnknown
Imported from Papers With Code
§ 01 · Benchmarks
Every benchmark GPT-2-Large (fine-tuning) has a recorded score for.
| # | Benchmark | Area · Task | Metric | Value | Rank | Date | Source |
|---|---|---|---|---|---|---|---|
| 01 | dart | Computer Vision · Optical Character Recognition | mover | 0.5% | #1 | 2021-07-14 | source ↗ |
| 02 | dart | Computer Vision · Optical Character Recognition | ter | 0.5% | #1 | 2021-07-14 | source ↗ |
| 03 | webnlg-(all) | Computer Vision · Optical Character Recognition | meteor | 0.4% | #1 | 2021-07-14 | source ↗ |
| 04 | webnlg-(all) | Computer Vision · Optical Character Recognition | ter | 0.4% | #1 | 2021-07-14 | source ↗ |
| 05 | webnlg-(seen) | Computer Vision · Optical Character Recognition | meteor | 0.5% | #1 | 2021-07-14 | source ↗ |
| 06 | webnlg-(seen) | Computer Vision · Optical Character Recognition | ter | 0.3% | #1 | 2021-07-14 | source ↗ |
| 07 | webnlg-(unseen) | Computer Vision · Optical Character Recognition | ter | 0.5% | #1 | 2021-07-14 | source ↗ |
| 08 | webnlg-(all) | Computer Vision · Optical Character Recognition | bleu | 55.5% | #2 | 2021-07-14 | source ↗ |
| 09 | webnlg-(seen) | Computer Vision · Optical Character Recognition | bleu | 65.3% | #2 | 2021-07-14 | source ↗ |
| 10 | webnlg-(unseen) | Computer Vision · Optical Character Recognition | bleu | 43.1% | #2 | 2021-07-14 | source ↗ |
| 11 | webnlg-(unseen) | Computer Vision · Optical Character Recognition | meteor | 0.4% | #2 | 2021-07-14 | source ↗ |
| 12 | dart | Computer Vision · Optical Character Recognition | bert | 0.9% | #5 | 2021-07-14 | source ↗ |
| 13 | dart | Computer Vision · Optical Character Recognition | bleurt | 0.4% | #5 | 2021-07-14 | source ↗ |
| 14 | dart | Computer Vision · Optical Character Recognition | meteor | 0.4% | #5 | 2021-07-14 | source ↗ |
| 15 | e2e | Computer Vision · Optical Character Recognition | nist | 8.8% | #5 | 2021-07-14 | source ↗ |
| 16 | e2e | Computer Vision · Optical Character Recognition | cider | 2.5% | #5 | 2021-07-14 | source ↗ |
| 17 | e2e | Computer Vision · Optical Character Recognition | bleu | 68.5% | #5 | 2021-07-14 | source ↗ |
| 18 | dart | Computer Vision · Optical Character Recognition | bleu | 47.0% | #6 | 2021-07-14 | source ↗ |
| 19 | e2e | Computer Vision · Optical Character Recognition | rouge-l | 69.9% | #6 | 2021-07-14 | source ↗ |
| 20 | e2e | Computer Vision · Optical Character Recognition | meteor | 46.0% | #6 | 2021-07-14 | source ↗ |
Rank column shows this model’s position vs all other models scored on the same benchmark + metric (competitors after the slash). #1 in red means current SOTA. Sorted by rank, then newest result.
§ 02 · Strengths by area
Where GPT-2-Large (fine-tuning) actually performs.
§ 03 · Papers
1 paper with results for GPT-2-Large (fine-tuning).
- 2021-07-14· Computer Vision· 20 results
HTLM: Hyper-Text Pre-Training and Prompting of Language Models
§ 04 · Related models
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§ 05 · Sources & freshness
Where these numbers come from.
papers-with-code
20
results
20 of 20 rows marked verified.