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gauthierrobert2/guard-ft-v19-trimmed-fp16
guard-ft-v19-trimmed-fp16 is a token classification model from gauthierrobert2. Use it when you need labels on individual words, such as names.
GUARD-FT round 19 (AgentDocs' EN+FR legal-document PII token classifier), vocabulary-trimmed to the pieces its EN+FR training corpus actually uses (250,002 - 53,751 pieces), fused (Attention/SkipLayerNormalization/Bia…
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From the Hugging Face model README
GUARD-FT round 19 (AgentDocs' EN+FR legal-document PII token classifier), vocabulary-trimmed to
the pieces its EN+FR training corpus actually uses (250,002 -> 53,751 pieces), fused
(Attention/SkipLayerNormalization/BiasGelu via onnxruntime.transformers.optimizer) then
weight-cast to fp16 (encoder + classifier head; graph I/O stays float32/int64 at the boundary).
Same checkpoint and same predictions as gauthierrobert2/guard-ft-v19 on GPU, and now also on
CPU — measured near-identical across a 259-document evaluation corpus at every downstream masking
stage (stage 3 leaked identical, F2 within 0.01pt). Loads correctly under ONNX Runtime 1.24.1's
CPU execution provider at ORT_ENABLE_ALL (an earlier revision of this repo did not: fusing
BEFORE the fp16 cast, instead of after, is what fixes it — see the model card history).
CUDA remains the fast path (~1.7x the CPU token-classifier throughput measured here); on CPU this
export is correct but not faster than fp32 — ONNX Runtime has very limited native fp16 CPU
kernels, so gauthierrobert2/guard-ft-v19-trimmed-int8 is the CPU-side sibling instead.