span-marker-roberta-large-ontonotes5
tomaarsen/span-marker-roberta-large-ontonotes5
Tracked by OpenModelStats since September 10, 2026View on Hugging Face ↗
- Downloads 30DRolling recent-window download count as reported by the source platform.
- 67
- Total downloadsCumulative all-time download counter reported by the source platform.
- 84.8K
- Gained 7DIncrease in cumulative total downloads between OpenModelStats' own observations seven days apart.
- +3(increase)
- 0% vs baseline
- Gained 30DIncrease in cumulative total downloads between OpenModelStats' own observations roughly thirty days apart.
- —
- LikesCommunity likes reported by the source platform.
- 15
- +0 in 7D
- Rank · tracked modelsPosition among all eligible models tracked by OpenModelStats, ordered by recent downloads. #1054 among tracked Token Classification models.
- #54,345
- 384(down 384 places)this week
- Parameters
- 355M
- Last source update
- Sep 22, 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.
- 11.4
- span-marker-roberta-large-ontonotes5 gained 3 downloads during the last seven tracked days.
- It moved from #1049 to #1054 among tracked Token Classification models this week.
Downloads over time
Cumulative total downloads observed by OpenModelStatsLikes over time
Rank among tracked models
Lower is betterOverview
span-marker-roberta-large-ontonotes5 is a token classification model published by tomaarsen. OpenModelStats has tracked the model since Sep 10, 2026, most recently observing it Sep 17, 2026.
- Published
- Jun 10, 2023
- Last updated
- Sep 22, 2023
- Library
- span-marker
- License
- apache-2.0
- Task
- Token Classification
- Parameters
- 355,401,237
- Spaces
- —
- Derivative models
- —
- Velocity 7D/day
- 0
span-markerpytorchsafetensorstoken-classificationnernamed-entity-recognitionendataset:tner/ontonotes5license:apache-2.0model-indexendpoints_compatibleregion:us
Publisher
tomaarsenView publisher statistics →Similar models
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