OpenMed-NER-DNADetect-TinyMed-65M
OpenMed/OpenMed-NER-DNADetect-TinyMed-65M
Tracked by OpenModelStats since September 3, 2026View on Hugging Face ↗
- Downloads 30DRolling recent-window download count as reported by the source platform.
- 94.3K
- Total downloadsCumulative all-time download counter reported by the source platform.
- 1.1M
- Gained 7DIncrease in cumulative total downloads between OpenModelStats' own observations seven days apart.
- +23.7K(increase)
- 2.3%(up) vs baseline
- Gained 30DIncrease in cumulative total downloads between OpenModelStats' own observations roughly thirty days apart.
- —
- LikesCommunity likes reported by the source platform.
- 0
- +0 in 7D
- Rank · tracked modelsPosition among all eligible models tracked by OpenModelStats, ordered by recent downloads. #286 among tracked Token Classification models.
- #2,769
- 42(down 42 places)this week
- Parameters
- 65.2M
- Last source update
- Aug 5, 2025
- MomentumOpenModelStats' composite rising-model signal (0–100): a percentile blend of absolute 7-day download gains, capped percentage growth, and likes gained. See Methodology.
- 66.0
- OpenMed-NER-DNADetect-TinyMed-65M gained 23.7K downloads during the last seven tracked days.
- It moved from #250 to #286 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
OpenMed-NER-DNADetect-TinyMed-65M is a token classification model published by OpenMed. OpenModelStats has tracked the model since Sep 3, 2026, most recently observing it Oct 3, 2026.
- Published
- Jul 18, 2025
- Last updated
- Aug 5, 2025
- Library
- transformers
- License
- apache-2.0
- Task
- Token Classification
- Parameters
- 65,199,371
- Spaces
- —
- Derivative models
- —
- Velocity 7D/day
- 3,391
transformerssafetensorsdistilberttoken-classificationnamed-entity-recognitionbiomedical-nlpprotein-recognitiongene-recognitionmolecular-biologygenomicsdnarna
Publisher
OpenMedView publisher statistics →Similar models
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