Token Classification
Transformers
Safetensors
lfm2
liquid
lfm2.5
bidirectional
masked-lm
encoder
pii
ner
privacy
multilingual
custom_code
Instructions to use LiquidAI/LFM2.5-Encoder-350M-PII-Detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/LFM2.5-Encoder-350M-PII-Detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="LiquidAI/LFM2.5-Encoder-350M-PII-Detector", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-Encoder-350M-PII-Detector", trust_remote_code=True) model = AutoModelForTokenClassification.from_pretrained("LiquidAI/LFM2.5-Encoder-350M-PII-Detector", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
corrected proxy-gate numbers (crosswalk fix) + reproduced q4 baseline
Browse files
README.md
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@@ -137,14 +137,16 @@ _⚠ in OUR backbone's pretraining — contaminated for us; reference only_
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## ShieldFlow proxy-traffic product gate (native ShieldFlow taxonomy, partial)
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Held-out 10% of the v7 proxy corpus
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| dataset | ours (v4) P / R / F1 | q4 incumbent |
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| proxy_traffic_holdout | 0.
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| proxy_traffic_holdout_xlong | 0.
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v4
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## ShieldFlow proxy-traffic product gate (native ShieldFlow taxonomy, partial)
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Held-out 10% of the v7 proxy corpus; we train on the same 90% the incumbent did and
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evaluate on the identical untouched holdout (seed-42 split). q4 baseline reproduced
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exactly in this harness (validates the scoring path).
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| dataset | **ours (v4)** P / R / F1 | q4 incumbent (reproduced) |
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| proxy_traffic_holdout | 0.872 / 0.715 / **0.786** | 0.794 / 0.982 / 0.878 |
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| proxy_traffic_holdout_xlong | 0.793 / 0.767 / **0.780** | 0.834 / 0.940 / 0.884 |
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v4 has **higher precision** than the incumbent; the F1 gap is **recall** (q4 is tuned for
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near-total recall on this workload). Scored in native ShieldFlow space with these new
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types: `developer.login_credentials` (combo span), `developer.device_id`,
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`healthcare.health_plan_id`.
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