bert-base-swedish-lowermix-reallysimple-ner
KBLab/bert-base-swedish-lowermix-reallysimple-ner
Tracked by OpenModelStats since September 7, 2026View on Hugging Face ↗
- Downloads 30DRolling recent-window download count as reported by the source platform.
- 3,617
- Total downloadsCumulative all-time download counter reported by the source platform.
- 134.9K
- Gained 7DIncrease in cumulative total downloads between OpenModelStats' own observations seven days apart.
- +1,327(increase)
- 1.0%(up) vs baseline
- Gained 30DIncrease in cumulative total downloads between OpenModelStats' own observations roughly thirty days apart.
- —
- LikesCommunity likes reported by the source platform.
- 5
- +0 in 7D
- Rank · tracked modelsPosition among all eligible models tracked by OpenModelStats, ordered by recent downloads. #731 among tracked Token Classification models.
- #14,231
- 65(down 65 places)this week
- Parameters
- 124M
- Last source update
- Apr 19, 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.
- 54.9
- bert-base-swedish-lowermix-reallysimple-ner gained 1,327 downloads during the last seven tracked days.
- It moved from #735 to #731 among tracked Token Classification models this week.
Downloads over time
Daily gain between consecutive observationsLikes over time
Rank among tracked models
Lower is betterOverview
bert-base-swedish-lowermix-reallysimple-ner is a token classification model published by KBLab. OpenModelStats has tracked the model since Sep 7, 2026, most recently observing it Oct 6, 2026.
- Published
- Mar 2, 2022
- Last updated
- Apr 19, 2023
- Library
- transformers
- License
- —
- Task
- Token Classification
- Parameters
- 124,107,785
- Spaces
- —
- Derivative models
- —
- Velocity 7D/day
- 190
transformerspytorchsafetensorsberttoken-classificationsequence-tagger-modelsvdataset:KBLab/sucx3_nerendpoints_compatibleregion:usdeploy:azure
Publisher
KBLabView publisher statistics →Similar models
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