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dslimOpen weights model0.11B parameters

BERT base NER (dslim) benchmarks

All models
  • General rank
    Too few tasks4 non-coding tasks ranked5 needed for a rank
  • Cost
    Self-hostedSmall GPU, ~0.9 GBNo hosted price
  • LangWatch policy#30 / 42 tied
    90.1%Best task · PII left invs PII detectors

Comparison summary

Strongest
Safety and guardrails
Leads 0 of 4 (0%)

An entity tagger: it marks spans of text such as names, addresses or account numbers, used to find personal data and mask it. Strongest at safety.

Specifications

Lab
dslim
Weights
Open weights
Parameters
0.11B
Licence
MIT
Type
encoder-NER

Safety and guardrails

Share of tasks led · #36 of 46

  1. Gemma 4 31BLeads 3 of 4
  2. Perplexity PII TracerLeads 2 of 4
  3. Amazon ComprehendLeads 1 of 4
  4. Qwen3.8 27BLeads 1 of 5
  5. Gemma 4 12BLeads 0 of 4
  6. Ministral 3 14BLeads 0 of 5
  7. Gemma 4 26B A4BLeads 0 of 4
  8. BERT base NER (dslim)
    Leads 0 of 4
See the 5 tasks

Traces

Tier 14 of 19#34 / 46

PII left in, % · Lower is better

  1. Amazon Comprehend10.6, interval 9.4 to 11.9, ahead alone
  2. GLiNER Multi PII v115.3, interval 13.2 to 17.3, tier 2
  3. NVIDIA GLiNER PII19.7, interval 17.4 to 22.0, tier 3
  4. Gemma 4 26B A4B19.8, interval 15.8 to 24.2, tier 3
  5. Qwen3.8 27B20.3, interval 16.5 to 24.3, tier 3
  6. GLiNER2 Large20.4, interval 18.0 to 22.8, tier 3
  7. Gemma 4 12B23.3, interval 19.0 to 27.6, tier 3
  8. BERT base NER (dslim)
    85.0, interval 82.8 to 87.2, tier 14
Tested against 52 PII detectors

Business

Tier 14 of 18#34 / 46

PII left in, % · Lower is better

  1. Perplexity PII Tracer2.1, interval 1.2 to 3.0, tied for the lead
  2. Qwen3.8 27B2.4, interval 1.7 to 3.3, tied for the lead
  3. Gemma 4 31B2.9, interval 1.9 to 4.0, tied for the lead
  4. GLiNER Multi PII v14.1, interval 3.1 to 5.3, tier 2
  5. Ministral 3 14B5.4, interval 4.0 to 6.9, tier 2
  6. Gemma 4 12B5.8, interval 4.4 to 7.3, tier 3
  7. Amazon Comprehend5.9, interval 4.6 to 7.2, tier 3
  8. BERT base NER (dslim)
    69.5, interval 66.8 to 72.4, tier 14
Tested against 53 PII detectors

Multilingual

Tier 23 of 25#33 / 43

PII left in, % · Lower is better

  1. Perplexity PII Tracer2.9, interval 2.5 to 3.4, ahead alone
  2. Qwen3.8 27B4.2, interval 3.7 to 4.8, tier 2
  3. Gemma 4 31B4.5, interval 3.9 to 5.1, tier 2
  4. GLiNER Multi PII v15.7, interval 5.2 to 6.3, tier 3
  5. bardsai EU PII Multilang6.9, interval 6.3 to 7.5, tier 4
  6. Qwen3.6 35B A3B7.7, interval 7.0 to 8.4, tier 4
  7. Gemma 4 12B8.9, interval 8.1 to 9.7, tier 5
  8. BERT base NER (dslim)
    91.0, interval 90.0 to 92.0, tier 23
Tested against 53 PII detectors

Look-alikes

Not ranked

Look-alikes redacted, % · Lower is better

  1. BERT base NER (dslim)
    0.7, interval 0.4 to 1.1, not ranked
  2. Gemma 4 31B3.5, interval 2.1 to 5.1, ahead alone
  3. Qwen3.8 27B10.3, interval 7.9 to 12.9, tier 2
  4. NuExtract 319.8, interval 15.9 to 23.6, tier 3
  5. Qwen3.5 9B20.5, interval 16.8 to 24.3, tier 3
  6. Ministral 3 14B30.5, interval 26.5 to 34.8, tier 4
  7. NVIDIA GLiNER PII42.0, interval 38.2 to 46.1, tier 5
  8. Gemma 3 4B IT45.0, interval 41.0 to 49.0, tier 5
Tested against 53 PII detectors

LangWatch policy

Tier 12 of 13#30 / 42

PII left in, % · Lower is better

  1. Gemma 4 31B6.9, interval 2.8 to 11.3, ahead alone
  2. Ministral 3 14B10.2, interval 5.8 to 15.4, tier 2
  3. Gemma 4 12B16.5, interval 10.3 to 23.2, tier 3
  4. Qwen3.8 27B16.7, interval 9.8 to 25.0, tier 3
  5. Gemma 4 26B A4B20.5, interval 13.3 to 28.8, tier 4
  6. Qwen3.5 9B21.5, interval 13.4 to 30.6, tier 4
  7. Gemma 3 4B IT22.4, interval 14.3 to 32.1, tier 4
  8. BERT base NER (dslim)
    90.1, interval 82.4 to 96.1, tier 12
Tested against 57 PII detectors

Cost

Self-hosted · GPU memory needed, among encoder-NERs · lower is better

  1. Gravitee BERT Small PII~0.6 GB
  2. Secret Masker v3.3a~0.8 GB
  3. Anonym-IA CamemBERT PII FR~0.9 GB
  4. BERT base NER (dslim)
    ~0.9 GB
  5. Veil PII KO Lite~0.9 GB
  6. bardsai EU PII Multilang~1.6 GB
  7. XLM-RoBERTa NER HRL~1.6 GB
  8. OpenMed QwenMed XL~2.9 GB
How we count

A model leads a task when it is in the task's leading tie tier. Each task counts inside its own benchmark, against that benchmark's models; no score is averaged. An area chart shows the models ranked on at least 2 of the area's tasks in a benchmark this model is in too. The overall standing counts every non-coding tasks and needs 5 for a rank. Latency is compared only on one hardware tier.

BERT base NER (dslim)Other modelsWhisker: 95% interval