v5: full fine-tune with CUSTOMER_NAME + PROJECT_NAME labels, 49k dataset

train_ner_pii_newlabel.py

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+# /// script
+# dependencies = [
+#   "transformers>=4.48.0",
+#   "datasets>=2.20.0",
+#   "evaluate>=0.4.0",
+#   "seqeval>=1.2.2",
+#   "trackio",
+#   "numpy<2.0",
+#   "accelerate>=0.34.0",
+# ]
+# ///
+"""
+ModernBERT PII NER — remapped to 11 company policy labels.
+
+Trains from answerdotai/ModernBERT-base with a new 23-label classification
+head.  Fixes the entity-scan alignment bug: instead of reading char_labels
+only at real_s (the first non-space position), we now scan the entire token
+span [real_s, tok_e) for the first entity character.  This ensures entities
+that start after punctuation (e.g. "(Home" or ":John") are correctly labeled
+rather than silently dropped.
+
+Run with:  uv run train_ner_pii.py
+"""
+
+import json
+import numpy as np
+import trackio
+from datasets import load_dataset
+from transformers import (
+    AutoTokenizer,
+    AutoModelForTokenClassification,
+    TrainingArguments,
+    Trainer,
+    DataCollatorForTokenClassification,
+    EarlyStoppingCallback,
+)
+import evaluate
+
+# ── Training label map: 56 source types → 17 training categories ─────────────
+# At inference, LABEL_MAP_INFER collapses these to 11 policy categories.
+LABEL_MAP_TRAIN = {
+    # PER — names only; PREFIX removed (standalone Mr./Dr. caused boundary FPs)
+    "FIRSTNAME":           "PER",
+    "MIDDLENAME":          "PER",
+    "LASTNAME":            "PER",
+    "PREFIX":              "O",
+    # ORG
+    "COMPANYNAME":         "ORG",
+    # New labels
+    "CUSTOMER_NAME":       "CUSTOMER_NAME",
+    "PROJECT_NAME":        "PROJECT_NAME",
+    # EMAIL
+    "EMAIL":               "EMAIL",
+    # PHONE
+    "PHONENUMBER":         "PHONE",
+    # ADDRESS
+    "BUILDINGNUMBER":      "ADDRESS",
+    "STREET":              "ADDRESS",
+    "SECONDARYADDRESS":    "ADDRESS",
+    "CITY":                "ADDRESS",
+    "COUNTY":              "ADDRESS",
+    "STATE":               "ADDRESS",
+    "ZIPCODE":             "ADDRESS",
+    # GOV_ID
+    "SSN":                 "GOV_ID",
+    # FINANCIAL_ID
+    "CREDITCARDNUMBER":    "FINANCIAL_ID",
+    "CREDITCARDCVV":       "FINANCIAL_ID",
+    "IBAN":                "FINANCIAL_ID",
+    "BIC":                 "FINANCIAL_ID",
+    "BITCOINADDRESS":      "FINANCIAL_ID",
+    "ETHEREUMADDRESS":     "FINANCIAL_ID",
+    "LITECOINADDRESS":     "FINANCIAL_ID",
+    "MASKEDNUMBER":        "FINANCIAL_ID",
+    # ACCOUNT_ID — ACCOUNTNAME removed (too ambiguous)
+    "ACCOUNTNAME":         "O",
+    "ACCOUNTNUMBER":       "ACCOUNT_ID",
+    "USERNAME":            "ACCOUNT_ID",
+    # DEVICE_ID
+    "IP":                  "DEVICE_ID",
+    "IPV4":                "DEVICE_ID",
+    "IPV6":                "DEVICE_ID",
+    "MAC":                 "DEVICE_ID",
+    "PHONEIMEI":           "DEVICE_ID",
+    "USERAGENT":           "DEVICE_ID",
+    "VEHICLEVIN":          "DEVICE_ID",
+    "VEHICLEVRM":          "DEVICE_ID",
+    # DATE_OF_BIRTH
+    "DOB":                 "DATE_OF_BIRTH",
+    # Training-only categories (model learns them; suppressed at inference)
+    "AMOUNT":              "AMOUNT",
+    "DATE":                "DATE",
+    "NEARBYGPSCOORDINATE": "NEARBYGPSCOORDINATE",
+    "PASSWORD":            "PASSWORD",
+    "PIN":                 "PIN",
+    "TIME":                "TIME",
+    "URL":                 "URL",
+    # Explicitly O
+    "AGE":                 "O",
+    "CURRENCY":            "O",
+    "CURRENCYCODE":        "O",
+    "CURRENCYNAME":        "O",
+    "CURRENCYSYMBOL":      "O",
+    "EYECOLOR":            "O",
+    "GENDER":              "O",
+    "SEX":                 "O",
+    "HEIGHT":              "O",
+    "JOBAREA":             "O",
+    "JOBTITLE":            "O",
+    "JOBTYPE":             "O",
+    "ORDINALDIRECTION":    "O",
+}
+
+TRAIN_LABELS = [
+    "ACCOUNT_ID", "ADDRESS", "AMOUNT", "CUSTOMER_NAME", "DATE", "DATE_OF_BIRTH",
+    "DEVICE_ID", "EMAIL", "FINANCIAL_ID", "GOV_ID", "NEARBYGPSCOORDINATE",
+    "ORG", "PASSWORD", "PER", "PHONE", "PIN", "PROJECT_NAME", "TIME", "URL",
+]
+
+label_list = (
+    ["O"]
+    + sorted(f"B-{l}" for l in TRAIN_LABELS)
+    + sorted(f"I-{l}" for l in TRAIN_LABELS)
+)
+id2label = {i: l for i, l in enumerate(label_list)}
+label2id = {l: i for i, l in id2label.items()}
+
+# ── Config ────────────────────────────────────────────────────────────────────
+MODEL_NAME   = "answerdotai/ModernBERT-base"        # train from base
+DATASET_NAME = "jefftherover/pii-masking-200k-newlabel"
+HUB_MODEL_ID = "jefftherover/modernbert-pii-mapped-v5"
+OUTPUT_DIR   = "modernbert-pii-mapped-v5"
+MAX_LENGTH   = 512
+
+print(f"Labels ({len(label_list)}): {label_list}")
+
+# ── Dataset ───────────────────────────────────────────────────────────────────
+print("Loading dataset...")
+en = load_dataset(DATASET_NAME, split="train")
+print(f"Rows: {len(en)}")
+
+splits   = en.train_test_split(test_size=0.1, seed=42)
+train_ds = splits["train"]
+eval_ds  = splits["test"]
+print(f"Train: {len(train_ds)}  Eval: {len(eval_ds)}")
+
+# ── Tokenizer ─────────────────────────────────────────────────────────────────
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+# ── Tokenisation + label alignment ────────────────────────────────────────────
+# Identical to v6 logic, with LABEL_MAP applied inside make_char_labels.
+
+def make_char_labels(text, raw):
+    spans = json.loads(raw) if isinstance(raw, str) else raw
+    cl = ["O"] * len(text)
+    for span in spans:
+        s, e, src_lbl = int(span[0]), int(span[1]), span[2]
+        tgt_lbl = LABEL_MAP_TRAIN.get(src_lbl)
+        if tgt_lbl is None:
+            continue
+        for i in range(s, min(e, len(text))):
+            cl[i] = f"B-{tgt_lbl}" if i == s else f"I-{tgt_lbl}"
+    return cl
+
+def tokenize_and_align(examples):
+    enc = tokenizer(
+        examples["source_text"],
+        truncation=True,
+        max_length=MAX_LENGTH,
+        return_offsets_mapping=True,
+    )
+    all_labels = []
+    for idx in range(len(examples["source_text"])):
+        text    = examples["source_text"][idx]
+        cl      = make_char_labels(text, examples["span_labels"][idx])
+        offsets = enc["offset_mapping"][idx]
+        labels, prev_end = [], None
+        for tok_s, tok_e in offsets:
+            if tok_s == tok_e:
+                labels.append(-100)
+                prev_end = None
+                continue
+            # Space-offset fix: ModernBERT absorbs leading space into token offset
+            real_s = tok_s
+            while real_s < tok_e and text[real_s] == " ":
+                real_s += 1
+            is_word_start = prev_end is None or real_s > tok_s
+            # Scan the full token span for the first entity character.
+            # Fixes the case where an entity begins after punctuation with no
+            # preceding space (e.g. "(Home Loan Account") — previously real_s
+            # landed on "(" (O) and the entity was silently dropped.
+            lbl = "O"
+            for c in range(real_s, min(tok_e, len(cl))):
+                if cl[c] != "O":
+                    lbl = cl[c]
+                    break
+            if lbl == "O":
+                labels.append(label2id["O"] if is_word_start else -100)
+            else:
+                labels.append(label2id.get(lbl, label2id["O"]))
+            prev_end = tok_e
+        all_labels.append(labels)
+    enc.pop("offset_mapping")
+    enc["labels"] = all_labels
+    return enc
+
+print("Tokenising datasets...")
+cols      = train_ds.column_names
+train_tok = train_ds.map(tokenize_and_align, batched=True, remove_columns=cols)
+eval_tok  = eval_ds.map(tokenize_and_align,  batched=True, remove_columns=cols)
+
+# ── Metrics ───────────────────────────────────────────────────────────────────
+seqeval = evaluate.load("seqeval")
+
+def compute_metrics(p):
+    logits, labels = p
+    preds = np.argmax(logits, axis=2)
+    true_preds  = [[id2label[pp] for pp, ll in zip(pr, la) if ll != -100]
+                   for pr, la in zip(preds,  labels)]
+    true_labels = [[id2label[ll] for pp, ll in zip(pr, la) if ll != -100]
+                   for pr, la in zip(preds,  labels)]
+    res = seqeval.compute(predictions=true_preds, references=true_labels)
+    return {
+        "precision": res["overall_precision"],
+        "recall":    res["overall_recall"],
+        "f1":        res["overall_f1"],
+        "accuracy":  res["overall_accuracy"],
+    }
+
+# ── Model ─────────────────────────────────────────────────────────────────────
+# v5: full fine-tune — body + head trained end-to-end on the 49k PII dataset.
+# This produces a PII-adapted body that becomes the frozen base for v6+.
+#
+# v6+ LoRA plan (re-enable this block, change MODEL_NAME to the v5 hub id):
+#   from peft import LoraConfig, get_peft_model, TaskType  # add peft to deps too
+#   lora_config = LoraConfig(
+#       task_type=TaskType.TOKEN_CLS,
+#       r=16, lora_alpha=32, lora_dropout=0.1,
+#       target_modules=["Wqkv", "out_proj", "Wi", "Wo"],
+#       modules_to_save=["classifier"],
+#       bias="none",
+#   )
+#   model = get_peft_model(model, lora_config)
+#   model.print_trainable_parameters()
+print(f"Loading model (ModernBERT-base + new {len(label_list)}-label head)...")
+model = AutoModelForTokenClassification.from_pretrained(
+    MODEL_NAME,
+    num_labels=len(label_list),
+    id2label=id2label,
+    label2id=label2id,
+)
+
+# ── Trackio ───────────────────────────────────────────────────────────────────
+trackio.init(project="modernbert-pii-mapped", name="modernbert-pii-mapped-v5")
+
+# ── Training args ─────────────────────────────────────────────────────────────
+args = TrainingArguments(
+    output_dir=OUTPUT_DIR,
+    num_train_epochs=5,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=32,
+    gradient_accumulation_steps=2,          # effective batch = 32
+    learning_rate=5e-5,
+    weight_decay=0.01,
+    warmup_ratio=0.1,
+    lr_scheduler_type="cosine_with_restarts",
+    eval_strategy="steps",
+    eval_steps=500,
+    save_strategy="steps",
+    save_steps=500,
+    save_total_limit=3,
+    load_best_model_at_end=True,
+    metric_for_best_model="f1",
+    greater_is_better=True,
+    push_to_hub=True,
+    hub_model_id=HUB_MODEL_ID,
+    hub_private_repo=False,
+    hub_strategy="every_save",
+    report_to="trackio",
+    run_name="modernbert-pii-mapped-v5",
+    fp16=True,
+    logging_steps=100,
+    dataloader_num_workers=2,
+)
+
+# ── Train ─────────────────────────────────────────────────────────────────────
+trainer = Trainer(
+    model=model,
+    args=args,
+    train_dataset=train_tok,
+    eval_dataset=eval_tok,
+    data_collator=DataCollatorForTokenClassification(tokenizer),
+    compute_metrics=compute_metrics,
+    callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+)
+
+print("Starting training...")
+trainer.train()
+trainer.push_to_hub()
+print(f"Done! Model pushed to: https://huggingface.co/{HUB_MODEL_ID}")