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a7857c1 | 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 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | #!/usr/bin/env python3
"""Standalone inference for the internal three-label PII classifier."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Any
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
LABELS = (
"privacy_asking_for_pii",
"privacy_giving_pii",
"directing_users_off_platform",
)
INSTRUCTION_PREFIX = (
"Instruct: In the following chat messages from target speaker t and possibly "
"other speakers s1, s2, etc., detect abuse by speaker t.\nQuery:"
)
INSTRUCTION_SEPARATOR = "\n\n"
TURN_SEPARATOR = " </s> "
TARGET_SPEAKER = "t"
SPEAKER_TEXT_SEPARATOR = ": "
MAX_LENGTH = 512
def _validate_turn(turn: Any, index: int) -> tuple[str, str]:
if not isinstance(turn, dict):
raise TypeError(f"conversation turn {index} must be a JSON object")
if set(turn) != {"speaker", "text"}:
raise ValueError(
f"conversation turn {index} must contain exactly 'speaker' and 'text'"
)
speaker = turn["speaker"]
text = turn["text"]
if not isinstance(speaker, str) or not speaker:
raise ValueError(f"conversation turn {index} has an invalid speaker")
if not isinstance(text, str):
raise ValueError(f"conversation turn {index} has non-string text")
return speaker, text
def format_conversation(value: str | list[dict[str, str]]) -> str:
"""Format plain text or speaker/text turns exactly like the training formatter."""
if isinstance(value, str):
turns: list[dict[str, str]] = [{"speaker": TARGET_SPEAKER, "text": value}]
elif isinstance(value, list):
if not value:
raise ValueError("conversation must contain at least one turn")
turns = value
else:
raise TypeError("input must be plain text or a JSON-list conversation")
other_speakers: dict[str, str] = {}
formatted_turns: list[str] = []
for index, turn in enumerate(turns):
speaker, text = _validate_turn(turn, index)
if speaker == TARGET_SPEAKER:
anonymous_speaker = TARGET_SPEAKER
else:
if speaker not in other_speakers:
other_speakers[speaker] = f"s{len(other_speakers) + 1}"
anonymous_speaker = other_speakers[speaker]
formatted_turns.append(
f"{anonymous_speaker}{SPEAKER_TEXT_SEPARATOR}{text}"
)
return (
INSTRUCTION_PREFIX
+ INSTRUCTION_SEPARATOR
+ TURN_SEPARATOR.join(formatted_turns)
)
def parse_input(value: str) -> str | list[dict[str, str]]:
"""Interpret a JSON list as turns; otherwise retain the value as plain text."""
try:
decoded = json.loads(value)
except json.JSONDecodeError:
return value
if isinstance(decoded, list):
return decoded
return value
def predict(
value: str | list[dict[str, str]],
model_path: str | Path,
) -> dict[str, float]:
"""Return uncalibrated sigmoid probabilities in the fixed three-label order."""
formatted = format_conversation(value)
tokenizer = AutoTokenizer.from_pretrained(model_path)
tokenizer.truncation_side = "left"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
model.eval()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
encoded = tokenizer(
formatted,
padding="max_length",
max_length=MAX_LENGTH,
truncation=True,
return_tensors="pt",
)
encoded = {name: tensor.to(device) for name, tensor in encoded.items()}
with torch.inference_mode():
logits = model(**encoded).logits
if logits.ndim != 2 or logits.shape[0] != 1 or logits.shape[1] != len(LABELS):
raise ValueError(
f"expected logits shape (1, {len(LABELS)}), got {tuple(logits.shape)}"
)
probabilities = torch.sigmoid(logits[0]).float().cpu().tolist()
return {label: probability for label, probability in zip(LABELS, probabilities)}
def _arguments() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Run the internal three-label PII classifier."
)
inputs = parser.add_mutually_exclusive_group(required=True)
inputs.add_argument("--text", help="Plain text or a JSON-list conversation.")
inputs.add_argument(
"--input-file",
type=Path,
help="UTF-8 file containing plain text or a JSON-list conversation.",
)
parser.add_argument("--model-path", type=Path, default=Path(__file__).parent)
return parser.parse_args()
def main() -> None:
args = _arguments()
raw_value = (
args.text
if args.text is not None
else args.input_file.read_text(encoding="utf-8")
)
result = predict(parse_input(raw_value), args.model_path)
print(json.dumps(result, indent=2, ensure_ascii=False))
if __name__ == "__main__":
main()
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