Upload train_ner.py with huggingface_hub

train_ner.py

@@ -96,17 +96,23 @@ def tokenize_and_align(examples):
                 real_s = tok_s
                 while real_s < tok_e and text[real_s] == ' ':
                     real_s += 1
-                # Word-boundary rule: new word if this is the very first token
-                # (prev_end is None) OR the token had a leading space stripped
-                # (real_s > tok_s).  The old check (tok_s > prev_end) was wrong
-                # for RoBERTa tokenizers: spaces are inside the token so there
-                # is never a gap between consecutive tokens, meaning only the
-                # very first token per sentence was ever labeled.
+                # Word-boundary rule: new word if first token (prev_end None)
+                # OR token had a leading space stripped (real_s > tok_s).
                 if prev_end is None or real_s > tok_s:
+                    # Word-start: assign label from char array (B-, I-, or O).
                     lbl = cl[real_s] if real_s < len(cl) else "O"
                     labels.append(label2id.get(lbl, label2id["O"]))
                 else:
-                    labels.append(-100)
+                    # Subword continuation (no leading space, within the same
+                    # word as the previous token).  v6: if this subword falls
+                    # inside an entity span give it I-<type> so the model
+                    # learns to sustain entity spans across subword boundaries.
+                    # Non-entity subword continuations keep -100 (ignored).
+                    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
         all_labels.append(labels)
     enc.pop("offset_mapping")
@@ -147,15 +153,15 @@ model = AutoModelForTokenClassification.from_pretrained(
 )
 
 # 7. Trackio
-trackio.init(project="modernbert-pii-ner", name="modernbert-pii-ner-43k-v5")
+trackio.init(project="modernbert-pii-ner", name="modernbert-pii-ner-43k-v6")
 
 # ── 8. Training args ─────────────────────────────────────────────────────────
-# v5: word-boundary detection fixed (real_s > tok_s instead of tok_s > prev_end).
-#     The old check never fired for RoBERTa-style tokenizers where spaces are
-#     absorbed into the next token's offset — only the first token per sentence
-#     was ever labeled (effectively 1 label per example).  With the fix every
-#     word gets labeled, giving the model a proper training signal for in-sentence
-#     and multi-word entities.
+# v6: subword continuation labeling fixed.
+#     Previously only the first subword of each word was labeled; within-word
+#     continuations got -100.  Now entity subword continuations receive I-<type>,
+#     so the model learns to sustain entity spans across subword boundaries.
+#     This directly targets the BOUNDARY fragmentation errors seen in v5 results
+#     (e.g. "Hadley_Larson" → Had/ley/_/Larson each emitting B- instead of one span).
 args = TrainingArguments(
     output_dir=OUTPUT_DIR,
     num_train_epochs=5,
@@ -178,7 +184,7 @@ args = TrainingArguments(
     hub_model_id=HUB_MODEL_ID,
     hub_strategy="every_save",
     report_to="trackio",
-    run_name="modernbert-pii-ner-43k-v5",
+    run_name="modernbert-pii-ner-43k-v6",
     fp16=True,
     logging_steps=100,
     dataloader_num_workers=2,