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Cataloged from perplexity-ai/pplx-pii-masking
All models by perplexity-ai →PII masking model for conversational data. A ~600M-parameter bidirectional
Qwen3 encoder
(perplexity-ai/pplx-embed-v1-0.6b
backbone, use_bidirectional_attention=true) with two heads:
private_person, account_number, private_url, private_date,
private_address, private_email, private_phone, other_pii, secret),
decoded with a constrained Viterbi decoder.pip install torch transformers, then load the model with trust_remote_code:
predict and mask wrap model(input_ids, attention_mask), which returns the
37 BIOES tag logits per token and one sensitivity logit per document. The
implementation is
modeling_pii_masking.py [blocked] in this repo: it builds
the encoder from modeling_pplx_qwen3.py [blocked] (vendored
from the
backbone repo),
applies this repo's fine-tuned weights and the two heads, and decodes spans
with the constrained BIOES Viterbi included in the file. Input is truncated
to max_seq_len (4096) tokens; chunk longer documents before calling
predict. model.save_pretrained(dir) also saves the tokenizer, so the saved
directory loads and predicts on its own.
model.safetensors holds the fine-tuned backbone (bf16, backbone.*), both
heads (fp32, token_cls_head.* / sensitivity_head.*), and the Viterbi bias
scalars (viterbi.*). max_seq_len is 4096 tokens.
Inference outline: tokenize (no BOS/EOS added), run the bidirectional encoder,
then per token logits = h @ W_cls.T + b_cls decoded with a constrained BIOES
Viterbi, and sensitivity = sigmoid(mean(h) @ W_sen.T + b_sen). The
PPLXQwen3Model encoder implementation (configuration_pplx_qwen3.py /
modeling_pplx_qwen3.py) is vendored from the
backbone repo;
config.json's backbone holds its config.
from transformers import AutoModel
model = AutoModel.from_pretrained( "perplexity-ai/pplx-pii-masking", trust_remote_code=True)
text = ("Hi, I'm Daniel Whitfield, you can reach me at " "daniels@meridiancap.com or 415-555-0123.")spans, sensitivity = model.predict(text)for s in spans: print(s.label, (s.start, s.end), text[s.start:s.end])# private_person (8, 24) Daniel Whitfield# private_email (46, 69) daniels@meridiancap.com# private_phone (73, 85) 415-555-0123
print(model.mask(text))# Hi, I'm [PRIVATE_PERSON], you can reach me at [PRIVATE_EMAIL] or [PRIVATE_PHONE].