ettin-reranker-32m-v1
cross-encoder/ettin-reranker-32m-v1
Tracked by OpenModelStats since August 24, 2026View on Hugging Face ↗
- Downloads 30DRolling recent-window download count as reported by the source platform.
- 12.8K
- Total downloadsCumulative all-time download counter reported by the source platform.
- 73.2K
- Gained 7DIncrease in cumulative total downloads between OpenModelStats' own observations seven days apart.
- +3,082(increase)
- 4.9%(up) vs baseline
- Gained 30DIncrease in cumulative total downloads between OpenModelStats' own observations roughly thirty days apart.
- —
- LikesCommunity likes reported by the source platform.
- 4
- +0 in 7D
- Rank · tracked modelsPosition among all eligible models tracked by OpenModelStats, ordered by recent downloads. #53 among tracked Text Ranking models.
- #7,264
- 70(up 70 places)this week
- Parameters
- 31.9M
- Last source update
- May 19, 2026
- MomentumOpenModelStats' composite rising-model signal (0–100): a percentile blend of absolute 7-day download gains, capped percentage growth, and likes gained. See Methodology.
- 65.4
- ettin-reranker-32m-v1 gained 3,082 downloads during the last seven tracked days.
Downloads over time
Cumulative total downloads observed by OpenModelStatsLikes over time
Rank among tracked models
Lower is betterOverview
ettin-reranker-32m-v1 is a text ranking model published by cross-encoder. OpenModelStats has tracked the model since Aug 24, 2026, most recently observing it 19h ago.
- Published
- May 15, 2026
- Last updated
- May 19, 2026
- Library
- sentence-transformers
- License
- apache-2.0
- Task
- Text Ranking
- Parameters
- 31,883,136
- Spaces
- —
- Derivative models
- 1
- Velocity 7D/day
- 440
sentence-transformersonnxsafetensorsopenvinomodernbertcross-encoderrerankergenerated_from_trainerdataset_size:143393475loss:MSELosstext-rankingen
Publisher
cross-encoderView publisher statistics →Derived from jhu-clsp/ettin-encoder-32m (reported by the source; parent not yet tracked).
Model family
Based on base-model relationships reported by the source's metadata.
Derivative models
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