paraphrase-filipino-mpnet-base-v2
meedan/paraphrase-filipino-mpnet-base-v2
Sentence Similaritysentence-transformers
Tracked by OpenModelStats since August 24, 2026View on Hugging Face ↗
- Downloads 30DRolling recent-window download count as reported by the source platform.
- 236
- Total downloadsCumulative all-time download counter reported by the source platform.
- 69.5K
- Gained 7DIncrease in cumulative total downloads between OpenModelStats' own observations seven days apart.
- +56(increase)
- 0.1%(up) vs baseline
- Gained 30DIncrease in cumulative total downloads between OpenModelStats' own observations roughly thirty days apart.
- —
- LikesCommunity likes reported by the source platform.
- 2
- +0 in 7D
- Rank · tracked modelsPosition among all eligible models tracked by OpenModelStats, ordered by recent downloads. #917 among tracked Sentence Similarity models.
- #46,548
- 295(down 295 places)this week
- Parameters
- —
- Last source update
- May 11, 2022
- MomentumOpenModelStats' composite rising-model signal (0–100): a percentile blend of absolute 7-day download gains, capped percentage growth, and likes gained. See Methodology.
- 25.4
- paraphrase-filipino-mpnet-base-v2 gained 56 downloads during the last seven tracked days.
- It moved from #918 to #917 among tracked Sentence Similarity models this week.
Downloads over time
Cumulative total downloads observed by OpenModelStatsLikes over time
Rank among tracked models
Lower is betterOverview
paraphrase-filipino-mpnet-base-v2 is a sentence similarity model published by meedan. OpenModelStats has tracked the model since Aug 24, 2026, most recently observing it Sep 21, 2026.
- Published
- Apr 4, 2022
- Last updated
- May 11, 2022
- Library
- sentence-transformers
- License
- —
- Task
- Sentence Similarity
- Parameters
- —
- Spaces
- 2
- Derivative models
- —
- Velocity 7D/day
- 8
sentence-transformerspytorchxlm-robertafeature-extractionsentence-similaritytransformerstext-embeddings-inferenceendpoints_compatibledeploy:azureregion:us
Publisher
meedanView publisher statistics →Similar models
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