Model overview
PII masking model for conversational data. A ~600M-parameter bidirectional Qwen3 encoder (`perplexity-ai/pplx-embed-v1-0.6b` backbone, run encoder-only via `is_causal=false`) with two heads: `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` in this repo: it builds the encoder with the standard Transformers `Qwen3Model` run bidirectionally via `config.is_causal = False` (hence `transformers>=5.2`, which passes `is_causal` through to the attention backend), 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.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
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Source prices & cost analysis
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| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
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Estimate monthly base token costs
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Public benchmarks & run configuration
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Sources & traceability
许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。