PII-Tracer

模型介绍

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.

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

常见问题

资料来源与可追溯记录

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。