File size: 2,154 Bytes
2aa1b7d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import json
import os
os.environ.setdefault("TRANSFORMERS_NO_TF", "1")
os.environ.setdefault("TRANSFORMERS_NO_FLAX", "1")
os.environ.setdefault("TRANSFORMERS_NO_TORCHVISION", "1")
os.environ["USE_TF"] = "0"
os.environ["USE_FLAX"] = "0"
os.environ["USE_TORCH"] = "1"
from common import decode_span_matrix, load_onnx_session, run_onnx_span, sigmoid_np
def replacement(label: str) -> str:
return f"[PII:{label}]"
def mask_text(text: str, spans: list[dict]) -> str:
out = text
for span in sorted(spans, key=lambda item: (item["start"], item["end"]), reverse=True):
out = out[: span["start"]] + replacement(span["label"]) + out[span["end"] :]
return out
def predict(text: str, session, tokenizer, config, min_score: float):
encoded = tokenizer(text, return_offsets_mapping=True, return_tensors="np", truncation=True)
offsets = [tuple(item) for item in encoded["offset_mapping"][0].tolist()]
span_logits = run_onnx_span(session, encoded)
span_scores = sigmoid_np(span_logits[0])
spans = decode_span_matrix(text, offsets, span_scores, config, min_score)
for span in spans:
span["replacement"] = replacement(span["label"])
return spans
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--model", required=True)
parser.add_argument("--text", required=True)
parser.add_argument("--min-score", type=float, default=0.5)
parser.add_argument("--json", action="store_true")
args = parser.parse_args()
session, tokenizer, config = load_onnx_session(args.model, onnx_file="model_quantized.onnx", onnx_subfolder="onnx")
spans = predict(args.text, session, tokenizer, config, args.min_score)
result = {
"model": args.model,
"backend": "onnx_global_pointer_q8",
"min_score": args.min_score,
"spans": spans,
"masked_text": mask_text(args.text, spans),
}
if args.json:
print(json.dumps(result, indent=2, ensure_ascii=False))
else:
print(result["masked_text"])
if __name__ == "__main__":
main()
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