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Vela PII

Vela PII finds sensitive entity spans for privacy-aware routing and redaction.

307M parameters · Input capacity: 32,768 tokens, including special tokens.

Outputs use 35 BIO labels across 17 entity types. The example returns spans with Unicode character offsets.

Evaluation

Exact-span micro F1 (×100) on the same synthetic development sets, compared with the original mmBERT32K PII model. Higher is better.

Evaluation	Original mmBERT	Vela
Short inputs · 888	23.66	90.76
Controlled 4K context · 30	0.44	89.03
Controlled 8K context · 30	0.28	89.88
Controlled 16K context · 30	0.36	89.73
Controlled 32K context · 30	0.23	89.24

Synthetic examples cover six languages. Long inputs include sparse entities, densely packed repeated entities and negative examples; micro F1 weights each entity equally. Both models process complete inputs in FP32 with the same exact-span scorer. These development sets informed Vela selection; they are not an independent natural-document benchmark.

Quick start

With PyTorch and Transformers 4.57.6 or 5.17.0:

from transformers import pipeline

model_id = "llm-semantic-router/Vela-1.0-Encoder-307M-PII"
model = pipeline("token-classification", model=model_id, aggregation_strategy="simple", device=-1)
text = "Contact Mara Wells at mara.wells@example.com."
assert len(model.tokenizer.encode(text)) <= model.model.config.max_position_embeddings
print(model(text))

Explore the Vela model collection