Model card
ML + RL (Paulus et al., 2017).
UnknownunknownUnknown paramsUnknown
Imported from Papers With Code
§ 02 · Benchmarks
Every benchmark ML + RL (Paulus et al., 2017) has a recorded score for.
| # | Benchmark | Area · Task | Metric | Value | Rank | Date | Source |
|---|---|---|---|---|---|---|---|
| 01 | cnn-/-daily-mail | Computer Vision · Optical Character Recognition | rouge-l | 36.9% | #22 | 2017-05-11 | source ↗ |
| 02 | cnn-/-daily-mail | Computer Vision · Optical Character Recognition | rouge-1 | 39.9% | #26 | 2017-05-11 | source ↗ |
| 03 | cnn-/-daily-mail | Computer Vision · Optical Character Recognition | rouge-2 | 15.8% | #30 | 2017-05-11 | 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.
§ 03 · Strengths by area
Where ML + RL (Paulus et al., 2017) actually performs.
§ 04 · Papers
1 paper with results for ML + RL (Paulus et al., 2017).
- 2017-05-11· Computer Vision· 3 results
A Deep Reinforced Model for Abstractive Summarization
§ 05 · Related models
Other Unknown models scored on Codesota.
CRAFT
Unknown params · 21 results
GPT-2-Large (fine-tuning)
Unknown params · 20 results
HTLM (fine-tuning)
Unknown params · 20 results
TextFuseNet (ResNeXt-101)
Unknown params · 16 results
MixNet
Unknown params · 13 results
PAN
Unknown params · 12 results
SPCNET
Unknown params · 12 results
TESTR
Unknown params · 12 results
§ 06 · Sources & freshness
Where these numbers come from.
papers-with-code
3
results
3 of 3 rows marked verified.