roberta-base-formality-ranker
s-nlp/roberta-base-formality-ranker
Tracked by OpenModelStats since August 24, 2026View on Hugging Face ↗
- Downloads 30DRolling recent-window download count as reported by the source platform.
- 70.7K
- Total downloadsCumulative all-time download counter reported by the source platform.
- 615.6K
- Gained 7DIncrease in cumulative total downloads between OpenModelStats' own observations seven days apart.
- +14.2K(increase)
- 2.5%(up) vs baseline
- Gained 30DIncrease in cumulative total downloads between OpenModelStats' own observations roughly thirty days apart.
- —
- LikesCommunity likes reported by the source platform.
- 23
- +0 in 7D
- Rank · tracked modelsPosition among all eligible models tracked by OpenModelStats, ordered by recent downloads. #94 among tracked Text Classification models.
- #3,218
- 46(up 46 places)this week
- Parameters
- 125M
- Last source update
- Sep 8, 2023
- MomentumOpenModelStats' composite rising-model signal (0–100): a percentile blend of absolute 7-day download gains, capped percentage growth, and likes gained. See Methodology.
- 65.7
- roberta-base-formality-ranker gained 14.2K downloads during the last seven tracked days.
- It moved from #95 to #94 among tracked Text Classification models this week.
Downloads over time
Cumulative total downloads observed by OpenModelStatsLikes over time
Rank among tracked models
Lower is betterOverview
roberta-base-formality-ranker is a text classification model published by s-nlp. OpenModelStats has tracked the model since Aug 24, 2026, most recently observing it Oct 6, 2026.
- Published
- Mar 2, 2022
- Last updated
- Sep 8, 2023
- Library
- transformers
- License
- cc-by-nc-sa-4.0
- Task
- Text Classification
- Parameters
- 124,647,684
- Spaces
- —
- Derivative models
- —
- Velocity 7D/day
- 2,028
transformerspytorchsafetensorsrobertatext-classificationformalityendataset:GYAFCdataset:Pavlick-Tetreault-2016license:cc-by-nc-sa-4.0text-embeddings-inferenceendpoints_compatible
Publisher
s-nlpView publisher statistics →Similar models
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