Upload train_ner_pii.py with huggingface_hub

train_ner_pii.py

@@ -12,8 +12,12 @@
 """
 ModernBERT PII NER — remapped to 11 company policy labels.
 
-Starts from the v6 fine-tuned backbone (jefftherover/modernbert-pii-ner)
-and trains a new 23-label classification head using the label mapping below.
+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
 """
@@ -32,79 +36,97 @@ from transformers import (
 )
 import evaluate
 
-# ── Label mapping: ai4privacy 56 types → 11 policy categories ────────────────
-# Labels absent from this dict → treated as O (not redacted)
-LABEL_MAP = {
-    # PER — person names and titles
-    "FIRSTNAME":        "PER",
-    "MIDDLENAME":       "PER",
-    "LASTNAME":         "PER",
-    "PREFIX":           "PER",      # Mr., Dr., Ms.
-    # ORG — organisation names
-    "COMPANYNAME":      "ORG",
+# ── 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",
     # EMAIL
-    "EMAIL":            "EMAIL",
+    "EMAIL":               "EMAIL",
     # PHONE
-    "PHONENUMBER":      "PHONE",
-    # ADDRESS — postal / physical address components
-    "BUILDINGNUMBER":   "ADDRESS",
-    "STREET":           "ADDRESS",
-    "SECONDARYADDRESS": "ADDRESS",  # apt / suite
-    "CITY":             "ADDRESS",
-    "COUNTY":           "ADDRESS",
-    "STATE":            "ADDRESS",
-    "ZIPCODE":          "ADDRESS",
-    # GOV_ID — government-issued identity numbers
-    "SSN":              "GOV_ID",
-    # FINANCIAL_ID — payment cards, bank & crypto account identifiers
-    "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 — bank / service account identifiers
-    "ACCOUNTNAME":      "ACCOUNT_ID",
-    "ACCOUNTNUMBER":    "ACCOUNT_ID",
-    "USERNAME":         "ACCOUNT_ID",
-    # DEVICE_ID — device, vehicle, and network identifiers
-    "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 — birth dates only (generic DATE → O)
-    "DOB":              "DATE_OF_BIRTH",
-    # CREDENTIALS — authentication secrets
-    "PASSWORD":         "CREDENTIALS",
-    "PIN":              "CREDENTIALS",
-    # Everything else (AGE, DATE, GENDER, JOBAREA, etc.) → O
+    "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",
 }
 
-TARGET_LABELS = [
-    "PER", "ORG", "EMAIL", "PHONE", "ADDRESS",
-    "GOV_ID", "FINANCIAL_ID", "ACCOUNT_ID", "DEVICE_ID",
-    "DATE_OF_BIRTH", "CREDENTIALS",
+TRAIN_LABELS = [
+    "ACCOUNT_ID", "ADDRESS", "AMOUNT", "DATE", "DATE_OF_BIRTH",
+    "DEVICE_ID", "EMAIL", "FINANCIAL_ID", "GOV_ID", "NEARBYGPSCOORDINATE",
+    "ORG", "PASSWORD", "PER", "PHONE", "PIN", "TIME", "URL",
 ]
 
 label_list = (
     ["O"]
-    + sorted(f"B-{l}" for l in TARGET_LABELS)
-    + sorted(f"I-{l}" for l in TARGET_LABELS)
+    + 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   = "jefftherover/modernbert-pii-ner"   # v6 backbone, new head
+MODEL_NAME   = "answerdotai/ModernBERT-base"        # train from base
 DATASET_NAME = "ai4privacy/pii-masking-200k"
-HUB_MODEL_ID = "jefftherover/modernbert-pii-mapped"
-OUTPUT_DIR   = "modernbert-pii-mapped"
+HUB_MODEL_ID = "jefftherover/modernbert-pii-mapped-v3"
+OUTPUT_DIR   = "modernbert-pii-mapped-v3"
 MAX_LENGTH   = 512
 
 print(f"Labels ({len(label_list)}): {label_list}")
@@ -131,7 +153,7 @@ def make_char_labels(text, 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.get(src_lbl)
+        tgt_lbl = LABEL_MAP_TRAIN.get(src_lbl)
         if tgt_lbl is None:
             continue
         for i in range(s, min(e, len(text))):
@@ -155,23 +177,26 @@ def tokenize_and_align(examples):
             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:
-                # 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
-                if prev_end is None or real_s > tok_s:
-                    # Word-start token
-                    lbl = cl[real_s] if real_s < len(cl) else "O"
-                    labels.append(label2id.get(lbl, label2id["O"]))
-                else:
-                    # Subword continuation: label entity spans as I-, ignore O
-                    lbl = cl[real_s] if real_s < len(cl) else "O"
-                    if lbl != "O":
-                        labels.append(label2id.get(f"I-{lbl[2:]}", label2id["O"]))
-                    else:
-                        labels.append(-100)
-                prev_end = tok_e
+                labels.append(label2id.get(lbl, label2id["O"]))
+            prev_end = tok_e
         all_labels.append(labels)
     enc.pop("offset_mapping")
     enc["labels"] = all_labels
@@ -200,29 +225,26 @@ def compute_metrics(p):
         "accuracy":  res["overall_accuracy"],
     }
 
-# ── Model: v6 backbone + new 23-label head ────────────────────────────────────
-print("Loading model (v6 backbone, new head)...")
+# ── Model ─────────────────────────────────────────────────────────────────────
+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,
-    ignore_mismatched_sizes=True,   # replaces the old 113-label head
 )
 
 # ── Trackio ───────────────────────────────────────────────────────────────────
-trackio.init(project="modernbert-pii-mapped", name="modernbert-pii-mapped-v1")
+trackio.init(project="modernbert-pii-mapped", name="modernbert-pii-mapped-v3")
 
 # ── Training args ─────────────────────────────────────────────────────────────
-# Lower LR than v6 (5e-5) to protect the pre-trained backbone while the new
-# classifier head converges.
 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=3e-5,
+    learning_rate=5e-5,
     weight_decay=0.01,
     warmup_ratio=0.1,
     lr_scheduler_type="cosine_with_restarts",
@@ -236,9 +258,10 @@ args = TrainingArguments(
     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-v1",
+    run_name="modernbert-pii-mapped-v3",
     fp16=True,
     logging_steps=100,
     dataloader_num_workers=2,
@@ -258,5 +281,4 @@ trainer = Trainer(
 print("Starting training...")
 trainer.train()
 trainer.push_to_hub()
-trackio.finish()
 print(f"Done! Model pushed to: https://huggingface.co/{HUB_MODEL_ID}")