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cd07add | 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 | """Fine-tune the email-risk classifier (HF Pro).
Trains distilbert-base-uncased (4 classes) on the blocklist-derived dataset.
The dataset is class-imbalanced (DISPOSABLE >> SPAM_TRAP), so cross-entropy is
class-weighted and per-class precision is reported - the promotion gate.
Usage:
python -m app.ml.train --data app/ml/data/email_risk.csv \
--hub-repo <hf-user>/email-risk-distilbert [--eval-only --model <repo>]
Requires requirements-ml.txt (torch CPU, transformers, datasets, evaluate).
"""
from __future__ import annotations
import argparse
import json
import sys
from collections import Counter
from pathlib import Path
LABEL_NAMES = ["NORMAL", "DISPOSABLE", "ROLE", "SPAM_TRAP"]
PROMOTION_PRECISION = 0.98 # DISPOSABLE precision required to leave advisory
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--data", default="app/ml/data/email_risk.csv")
parser.add_argument("--hub-repo", default="")
parser.add_argument("--model", default="distilbert-base-uncased")
parser.add_argument("--eval-only", action="store_true")
parser.add_argument("--epochs", type=int, default=3)
parser.add_argument("--out", default="app/ml/metrics.json")
args = parser.parse_args()
import datasets
import evaluate
import numpy as np
import torch
from sklearn.metrics import precision_recall_fscore_support
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
Trainer,
TrainingArguments,
)
raw = datasets.load_dataset("csv", data_files=args.data)["train"]
raw = raw.train_test_split(test_size=0.15, seed=42)
tokenizer = AutoTokenizer.from_pretrained(args.model)
def tok(batch):
return tokenizer(batch["text"], padding="max_length", truncation=True,
max_length=64)
ds = raw.map(tok, batched=True)
labels = ds["train"]["label"]
counts = Counter(labels)
total = sum(counts.values())
weights = [total / counts.get(i, 1) for i in range(len(LABEL_NAMES))]
print(f"class counts: {dict(sorted(counts.items()))} -> weights {weights}")
model = AutoModelForSequenceClassification.from_pretrained(
args.model, num_labels=len(LABEL_NAMES),
id2label={i: n for i, n in enumerate(LABEL_NAMES)},
label2id={n: i for i, n in enumerate(LABEL_NAMES)},
)
def compute_metrics(eval_pred):
logits, y = eval_pred
pred = np.argmax(logits, axis=-1)
p, r, f1, _ = precision_recall_fscore_support(
y, pred, labels=range(len(LABEL_NAMES)), zero_division=0)
return {
"accuracy": float((pred == y).mean()),
**{f"precision_{LABEL_NAMES[i]}": float(p[i]) for i in range(len(LABEL_NAMES))},
**{f"recall_{LABEL_NAMES[i]}": float(r[i]) for i in range(len(LABEL_NAMES))},
}
class WeightedTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False, **kw):
y = inputs.pop("labels")
outputs = model(**inputs)
loss = torch.nn.functional.cross_entropy(
outputs.logits, y, weight=torch.tensor(weights, dtype=outputs.logits.dtype,
device=outputs.logits.device))
return (loss, outputs) if return_outputs else loss
targs = TrainingArguments(
output_dir="app/ml/runs",
learning_rate=2e-5,
per_device_train_batch_size=64,
per_device_eval_batch_size=128,
num_train_epochs=args.epochs,
weight_decay=0.01,
eval_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
use_cpu=not torch.cuda.is_available(),
hub_model_id=args.hub_repo or None,
hub_strategy="end",
push_to_hub=bool(args.hub_repo),
)
trainer = WeightedTrainer(model=model, args=targs, train_dataset=ds["train"],
eval_dataset=ds["test"], compute_metrics=compute_metrics)
if args.eval_only:
metrics = trainer.evaluate()
else:
trainer.train()
metrics = trainer.evaluate()
if args.hub_repo:
trainer.push_to_hub()
disposable_precision = metrics.get("eval_precision_DISPOSABLE", 0.0)
result = {
"model": args.hub_repo or args.model,
"metrics": {k: round(v, 4) for k, v in metrics.items() if k.startswith("eval_")},
"class_weights": weights,
"promotion": {
"gate": f"precision_DISPOSABLE >= {PROMOTION_PRECISION}",
"precision_disposable": round(disposable_precision, 4),
"promotion_ready": disposable_precision >= PROMOTION_PRECISION,
},
}
Path(args.out).write_text(json.dumps(result, indent=2), encoding="utf-8")
print(json.dumps(result["promotion"], indent=2))
print(f"metrics -> {args.out}")
print("PROMOTION READY" if result["promotion"]["promotion_ready"] else
"STAY ADVISORY - precision gate not met")
return 0
if __name__ == "__main__":
raise SystemExit(main()) |