change with finetuned model

HF_LayoutLM_with_Passage.py

@@ -1,3 +1,369 @@
+#
+# import json
+# import argparse
+# import os
+# import random
+# import torch
+# import torch.nn as nn
+# from torch.utils.data import Dataset, DataLoader, random_split
+# from transformers import LayoutLMv3TokenizerFast, LayoutLMv3Model
+# from TorchCRF import CRF
+# from torch.optim import AdamW
+# from tqdm import tqdm
+# from sklearn.metrics import precision_recall_fscore_support
+#
+#
+# # --- Configuration for Augmentation ---
+# MAX_BBOX_DIMENSION = 999
+# MAX_SHIFT = 30
+# AUGMENTATION_FACTOR = 1
+#
+#
+# # -------------------------------------
+#
+#
+# # -------------------------
+# # Step 1: Preprocessing (Label Studio → BIO + bboxes)
+# # -------------------------
+# def preprocess_labelstudio(input_path, output_path):
+#     with open(input_path, "r", encoding="utf-8") as f:
+#         data = json.load(f)
+#
+#     processed = []
+#     total_items = len(data)  # Added for potential verbose logging
+#     print(f"🔄 Starting preprocessing of {total_items} documents...")
+#
+#     for item in data:
+#         words = item["data"]["original_words"]
+#         bboxes = item["data"]["original_bboxes"]
+#         labels = ["O"] * len(words)
+#
+#         if "annotations" in item:
+#             for ann in item["annotations"]:
+#                 for res in ann["result"]:
+#                     # Check if the result item is a span annotation
+#                     if "value" in res and "labels" in res["value"]:
+#                         text = res["value"]["text"]
+#                         tag = res["value"]["labels"][0]
+#                         # Some tokenizers may split words, so we must find a consecutive word match.
+#                         text_tokens = text.split()
+#
+#                         for i in range(len(words) - len(text_tokens) + 1):
+#                             if words[i:i + len(text_tokens)] == text_tokens:
+#                                 labels[i] = f"B-{tag}"
+#                                 for j in range(1, len(text_tokens)):
+#                                     labels[i + j] = f"I-{tag}"
+#                                 break  # Move to next annotation if a match is found
+#
+#         processed.append({"tokens": words, "labels": labels, "bboxes": bboxes})
+#
+#     with open(output_path, "w", encoding="utf-8") as f:
+#         json.dump(processed, f, indent=2, ensure_ascii=False)
+#
+#     print(f"✅ Preprocessed data saved to {output_path}")
+#     return output_path
+#
+#
+# # -------------------------
+# # Step 1.5: Bounding Box Augmentation
+# # -------------------------
+#
+# def translate_bbox(bbox, shift_x, shift_y):
+#     """
+#     Translates a single bounding box [x_min, y_min, x_max, y_max] by (shift_x, shift_y)
+#     and clamps the coordinates to the valid range [0, MAX_BBOX_DIMENSION].
+#     """
+#     x_min, y_min, x_max, y_max = bbox
+#
+#     new_x_min = x_min + shift_x
+#     new_y_min = y_min + shift_y
+#     new_x_max = x_max + shift_x
+#     new_y_max = y_max + shift_y
+#
+#     # Clamp the new coordinates
+#     new_x_min = max(0, min(new_x_min, MAX_BBOX_DIMENSION))
+#     new_y_min = max(0, min(new_y_min, MAX_BBOX_DIMENSION))
+#     new_x_max = max(0, min(new_x_max, MAX_BBOX_DIMENSION))
+#     new_y_max = max(0, min(new_y_max, MAX_BBOX_DIMENSION))
+#
+#     # Safety check
+#     if new_x_min > new_x_max: new_x_min = new_x_max
+#     if new_y_min > new_y_max: new_y_min = new_y_max
+#
+#     return [new_x_min, new_y_min, new_x_max, new_y_max]
+#
+#
+# def augment_sample(sample):
+#     """
+#     Generates a new sample by translating all bounding boxes.
+#     """
+#     shift_x = random.randint(-MAX_SHIFT, MAX_SHIFT)
+#     shift_y = random.randint(-MAX_SHIFT, MAX_SHIFT)
+#
+#     new_sample = sample.copy()
+#
+#     # Ensure tokens and labels are copied (they remain unchanged)
+#     new_sample["tokens"] = sample["tokens"]
+#     new_sample["labels"] = sample["labels"]
+#
+#     # Translate all bounding boxes
+#     new_bboxes = [translate_bbox(bbox, shift_x, shift_y) for bbox in sample["bboxes"]]
+#     new_sample["bboxes"] = new_bboxes
+#
+#     return new_sample
+#
+#
+# def augment_and_save_dataset(input_json_path, output_json_path):
+#     """
+#     Loads preprocessed data, performs augmentation, and saves the result.
+#     """
+#     print(f"🔄 Loading preprocessed data from {input_json_path} for augmentation...")
+#     with open(input_json_path, 'r', encoding="utf-8") as f:
+#         training_data = json.load(f)
+#
+#     augmented_data = []
+#     original_count = len(training_data)
+#
+#     print(f"🔄 Starting augmentation (Factor: {AUGMENTATION_FACTOR}, {original_count} documents)...")
+#
+#     for i, original_sample in enumerate(training_data):
+#         # 1. Add the original sample
+#         augmented_data.append(original_sample)
+#
+#         # 2. Generate augmented samples
+#         for _ in range(AUGMENTATION_FACTOR):
+#             if "tokens" in original_sample and "labels" in original_sample and "bboxes" in original_sample:
+#                 augmented_data.append(augment_sample(original_sample))
+#             else:
+#                 print(f"Warning: Skipping augmentation for sample {i} due to missing keys.")
+#
+#     augmented_count = len(augmented_data)
+#     print(f"Dataset Augmentation: Original samples: {original_count}, Total samples: {augmented_count}")
+#
+#     # Save the augmented dataset
+#     with open(output_json_path, 'w', encoding="utf-8") as f:
+#         json.dump(augmented_data, f, indent=2, ensure_ascii=False)
+#
+#     print(f"✅ Augmented data saved to {output_json_path}")
+#     return output_json_path
+#
+#
+# # -------------------------
+# # Step 2: Dataset Class (Unchanged)
+# # -------------------------
+# class LayoutDataset(Dataset):
+#     def __init__(self, json_path, tokenizer, label2id, max_len=512):
+#         with open(json_path, "r", encoding="utf-8") as f:
+#             self.data = json.load(f)
+#         self.tokenizer = tokenizer
+#         self.label2id = label2id
+#         self.max_len = max_len
+#
+#     def __len__(self):
+#         return len(self.data)
+#
+#     def __getitem__(self, idx):
+#         item = self.data[idx]
+#         words, bboxes, labels = item["tokens"], item["bboxes"], item["labels"]
+#
+#         # Tokenize
+#         encodings = self.tokenizer(
+#             words,
+#             boxes=bboxes,
+#             padding="max_length",
+#             truncation=True,
+#             max_length=self.max_len,
+#             return_offsets_mapping=True,
+#             return_tensors="pt"
+#         )
+#
+#         # Align labels to word pieces
+#         word_ids = encodings.word_ids(batch_index=0)
+#         label_ids = []
+#         for word_id in word_ids:
+#             if word_id is None:
+#                 label_ids.append(self.label2id["O"])  # [CLS], [SEP], padding
+#             else:
+#                 label_ids.append(self.label2id.get(labels[word_id], self.label2id["O"]))
+#
+#         encodings.pop("offset_mapping")
+#         encodings["labels"] = torch.tensor(label_ids)
+#
+#         return {key: val.squeeze(0) for key, val in encodings.items()}
+#
+#
+# # -------------------------
+# # Step 3: Model Architecture (Unchanged)
+# # -------------------------
+# class LayoutLMv3CRF(nn.Module):
+#     def __init__(self, model_name, num_labels):
+#         super().__init__()
+#         self.layoutlm = LayoutLMv3Model.from_pretrained(model_name)
+#         # self.layoutlm = LayoutLMv3Model.from_pretrained("heerjtdev/edugenius")
+#         self.dropout = nn.Dropout(0.1)
+#         self.classifier = nn.Linear(self.layoutlm.config.hidden_size, num_labels)
+#         self.crf = CRF(num_labels)
+#
+#     def forward(self, input_ids, bbox, attention_mask, labels=None):
+#         outputs = self.layoutlm(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask)
+#         sequence_output = self.dropout(outputs.last_hidden_state)
+#         emissions = self.classifier(sequence_output)
+#
+#         if labels is not None:
+#             # Training mode: calculate loss
+#             log_likelihood = self.crf(emissions, labels, mask=attention_mask.bool())
+#             return -log_likelihood.mean()
+#         else:
+#             # Inference mode: decode best path
+#             best_paths = self.crf.viterbi_decode(emissions, mask=attention_mask.bool())
+#             return best_paths
+#
+#
+# # -------------------------
+# # Step 4: Training + Evaluation (Unchanged)
+# # -------------------------
+# def train_one_epoch(model, dataloader, optimizer, device):
+#     model.train()
+#     total_loss = 0
+#     for batch in tqdm(dataloader, desc="Training"):
+#         batch = {k: v.to(device) for k, v in batch.items()}
+#         labels = batch.pop("labels")
+#         optimizer.zero_grad()
+#         loss = model(**batch, labels=labels)
+#         loss.backward()
+#         optimizer.step()
+#         total_loss += loss.item()
+#     return total_loss / len(dataloader)
+#
+#
+# def evaluate(model, dataloader, device, id2label):
+#     model.eval()
+#     all_preds, all_labels = [], []
+#     with torch.no_grad():
+#         for batch in tqdm(dataloader, desc="Evaluating"):
+#             batch = {k: v.to(device) for k, v in batch.items()}
+#             labels = batch.pop("labels").cpu().numpy()
+#             preds = model(**batch)
+#             for p, l, mask in zip(preds, labels, batch["attention_mask"].cpu().numpy()):
+#                 valid = mask == 1
+#                 l = l[valid].tolist()
+#                 all_labels.extend(l)
+#                 all_preds.extend(p[:len(l)])
+#
+#     # Exclude the "O" label and other special tokens if necessary, but using 'micro' average
+#     # on all valid tokens is typically fine for the initial evaluation.
+#     precision, recall, f1, _ = precision_recall_fscore_support(all_labels, all_preds, average="micro", zero_division=0)
+#     return precision, recall, f1
+#
+#
+# # -------------------------
+# # Step 5: Main Pipeline (Training) - MODIFIED LABELS + FILE PATH FIX
+# # -------------------------
+# def main(args):
+#     # LABELS UPDATED: Added SECTION_HEADING and PASSAGE
+#     labels = [
+#         "O",
+#         "B-QUESTION", "I-QUESTION",
+#         "B-OPTION", "I-OPTION",
+#         "B-ANSWER", "I-ANSWER",
+#         "B-SECTION_HEADING", "I-SECTION_HEADING",
+#         "B-PASSAGE", "I-PASSAGE"
+#     ]
+#     label2id = {l: i for i, l in enumerate(labels)}
+#     id2label = {i: l for l, i in label2id.items()}
+#
+#     # --- FIX for FileNotFoundError: Use a temporary directory for intermediate files ---
+#     TEMP_DIR = "temp_intermediate_files"
+#     os.makedirs(TEMP_DIR, exist_ok=True)
+#     print(f"\n--- SETUP PHASE: Created temp directory: {TEMP_DIR} ---")
+#
+#     # 1. Preprocess and save the initial training data
+#     print("\n--- START PHASE: PREPROCESSING ---")
+#
+#     # FIX: Prepend the directory path to the file name
+#     initial_bio_json = os.path.join(TEMP_DIR, "training_data_bio_bboxes.json")
+#     preprocess_labelstudio(args.input, initial_bio_json)
+#
+#     # 2. Augment the dataset with translated bboxes
+#     print("\n--- START PHASE: AUGMENTATION ---")
+#
+#     # FIX: Prepend the directory path to the file name
+#     augmented_bio_json = os.path.join(TEMP_DIR, "augmented_training_data_bio_bboxes.json")
+#     final_data_path = augment_and_save_dataset(initial_bio_json, augmented_bio_json)
+#
+#     # Clean up the intermediary file (optional)
+#     # import shutil
+#     # shutil.rmtree(TEMP_DIR)
+#
+#     # 3. Load and split augmented dataset
+#     print("\n--- START PHASE: MODEL/DATASET SETUP ---")
+#     #MODEL_ID = "heerjtdev/edugenius"
+#     tokenizer = LayoutLMv3TokenizerFast.from_pretrained("microsoft/layoutlmv3-base")
+#     #tokenizer = LayoutLMv3TokenizerFast.from_pretrained(MODEL_ID)
+#
+#     dataset = LayoutDataset(final_data_path, tokenizer, label2id, max_len=args.max_len)
+#     val_size = int(0.2 * len(dataset))
+#     train_size = len(dataset) - val_size
+#
+#     # Use a fixed seed for reproducibility in split
+#     torch.manual_seed(42)
+#     train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
+#     train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
+#     val_loader = DataLoader(val_dataset, batch_size=args.batch_size)
+#
+#     # 4. Initialize and load model
+#     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+#     print(f"Using device: {device}")
+#     # Num_labels is based on the updated 'labels' list
+#     model = LayoutLMv3CRF("microsoft/layoutlmv3-base", num_labels=len(labels)).to(device)
+#     # model = LayoutLMv3CRF(MODEL_ID, num_labels=len(labels)).to(device)
+#     ckpt_path = "checkpoints/layoutlmv3_crf_passage.pth"
+#     os.makedirs("checkpoints", exist_ok=True)
+#     if os.path.exists(ckpt_path):
+#         # NOTE: Loading an old checkpoint will likely fail now because num_labels has changed,
+#         # unless the old checkpoint had the *exact* same number of labels.
+#         # It is recommended to start training from scratch.
+#         # print(f"🔄 Loading checkpoint from {ckpt_path}")
+#         # model.load_state_dict(torch.load(ckpt_path, map_location=device))
+#         print(f"⚠️ Starting fresh training. Old checkpoint {ckpt_path} may be incompatible with new label count.")
+#
+#     optimizer = AdamW(model.parameters(), lr=args.lr)
+#
+#     # 5. Training loop
+#     for epoch in range(args.epochs):
+#         print(f"\n--- START PHASE: EPOCH {epoch + 1}/{args.epochs} TRAINING ---")
+#         avg_loss = train_one_epoch(model, train_loader, optimizer, device)
+#
+#         print(f"\n--- START PHASE: EPOCH {epoch + 1}/{args.epochs} EVALUATION ---")
+#         precision, recall, f1 = evaluate(model, val_loader, device, id2label)
+#
+#         print(
+#             f"Epoch {epoch + 1}/{args.epochs} | Loss: {avg_loss:.4f} | P: {precision:.3f} R: {recall:.3f} F1: {f1:.3f}")
+#         torch.save(model.state_dict(), ckpt_path)
+#         print(f"💾 Model saved at {ckpt_path}")
+#
+#
+#
+#
+# # -------------------------
+# # Step 7: Main Execution (Unchanged)
+# # -------------------------
+# if __name__ == "__main__":
+#     parser = argparse.ArgumentParser(description="LayoutLMv3 Fine-tuning and Inference Script.")
+#     parser.add_argument("--mode", type=str, required=True, choices=["train", "infer"],
+#                         help="Select mode: 'train' or 'infer'")
+#     parser.add_argument("--input", type=str, help="Path to input file (Label Studio JSON for train, PDF for infer).")
+#     parser.add_argument("--batch_size", type=int, default=4)
+#     parser.add_argument("--epochs", type=int, default=5)
+#     parser.add_argument("--lr", type=float, default=5e-5)
+#     parser.add_argument("--max_len", type=int, default=512)
+#     args = parser.parse_args()
+#
+#     if args.mode == "train":
+#         if not args.input:
+#             parser.error("--input is required for 'train' mode.")
+#         main(args)
+
 
 import json
 import argparse
@@ -6,21 +372,28 @@ import random
 import torch
 import torch.nn as nn
 from torch.utils.data import Dataset, DataLoader, random_split
+# Using LayoutLMv3TokenizerFast, LayoutLMv3Model
 from transformers import LayoutLMv3TokenizerFast, LayoutLMv3Model
+from transformers.utils import cached_file
+from safetensors.torch import load_file
 from TorchCRF import CRF
 from torch.optim import AdamW
 from tqdm import tqdm
 from sklearn.metrics import precision_recall_fscore_support
 
-
 # --- Configuration for Augmentation ---
 MAX_BBOX_DIMENSION = 999
 MAX_SHIFT = 30
 AUGMENTATION_FACTOR = 1
 
-
 # -------------------------------------
 
+# --- Hugging Face Model ID ---
+HF_MODEL_ID = "heerjtdev/edugenius"
+
+
+# -----------------------------
+
 
 # -------------------------
 # Step 1: Preprocessing (Label Studio → BIO + bboxes)
@@ -149,7 +522,7 @@ def augment_and_save_dataset(input_json_path, output_json_path):
 
 
 # -------------------------
-# Step 2: Dataset Class (Unchanged)
+# Step 2: Dataset Class
 # -------------------------
 class LayoutDataset(Dataset):
     def __init__(self, json_path, tokenizer, label2id, max_len=512):
@@ -193,13 +566,50 @@ class LayoutDataset(Dataset):
 
 
 # -------------------------
-# Step 3: Model Architecture (Unchanged)
+# Step 3: Model Architecture (PATCHED TO LOAD WEIGHTS CORRECTLY)
 # -------------------------
 class LayoutLMv3CRF(nn.Module):
-    def __init__(self, model_name, num_labels):
+    def __init__(self, model_name, num_labels, device):
         super().__init__()
-        self.layoutlm = LayoutLMv3Model.from_pretrained(model_name)
-        # self.layoutlm = LayoutLMv3Model.from_pretrained("heerjtdev/edugenius")
+
+        # 1. Initialize the LayoutLMv3 model using the base class
+        # We start by initializing from the base configuration to ensure all weights are present
+        self.layoutlm = LayoutLMv3Model.from_pretrained("microsoft/layoutlmv3-base")
+
+        # 2. Try to load the fine-tuned weights from the Hugging Face Hub/Cache
+        try:
+            # This resolves the path to the downloaded model.safetensors in the cache
+            # Assumes you have renamed your file on the Hugging Face Hub to 'model.safetensors'
+            weights_path = cached_file(model_name, "model.safetensors")
+            fine_tuned_weights = load_file(weights_path)
+
+            # 3. Strip the Mismatching Prefix (Assuming 'layoutlm.' prefix from a previous wrapper)
+            new_state_dict = {}
+            prefix_to_strip = "layoutlm."
+
+            for key, value in fine_tuned_weights.items():
+                if key.startswith(prefix_to_strip):
+                    new_key = key[len(prefix_to_strip):]
+                    new_state_dict[new_key] = value
+                else:
+                    new_state_dict[key] = value
+
+            # 4. Load the fixed state dictionary into the LayoutLMv3Model
+            # strict=False allows us to ignore classifier/CRF weights not in LayoutLMv3Model
+            print("🔄 Successfully loaded and stripped keys. Loading base LayoutLMv3 weights...")
+
+            # Load only the weights for the transformer body
+            missing_keys, unexpected_keys = self.layoutlm.load_state_dict(new_state_dict, strict=False)
+
+            print(f"Weights loading done: {len(missing_keys)} missing, {len(unexpected_keys)} unexpected keys.")
+
+        except Exception as e:
+            print(f"❌ Fine-tuned weights could not be loaded directly and mapped. Starting with random weights.")
+            print(f"Error: {e}")
+            # Fallback: Load the LayoutLMv3 component directly from the Hub ID (will result in random weights for layers)
+            self.layoutlm = LayoutLMv3Model.from_pretrained(model_name)
+
+        # 5. Initialize the new heads (CRF layer and Classifier)
         self.dropout = nn.Dropout(0.1)
         self.classifier = nn.Linear(self.layoutlm.config.hidden_size, num_labels)
         self.crf = CRF(num_labels)
@@ -220,7 +630,7 @@ class LayoutLMv3CRF(nn.Module):
 
 
 # -------------------------
-# Step 4: Training + Evaluation (Unchanged)
+# Step 4: Training + Evaluation
 # -------------------------
 def train_one_epoch(model, dataloader, optimizer, device):
     model.train()
@@ -243,21 +653,22 @@ def evaluate(model, dataloader, device, id2label):
         for batch in tqdm(dataloader, desc="Evaluating"):
             batch = {k: v.to(device) for k, v in batch.items()}
             labels = batch.pop("labels").cpu().numpy()
+            # The model returns a list of lists of predicted labels in inference mode
             preds = model(**batch)
             for p, l, mask in zip(preds, labels, batch["attention_mask"].cpu().numpy()):
                 valid = mask == 1
                 l = l[valid].tolist()
                 all_labels.extend(l)
+                # Ensure pred length matches label length for the unmasked tokens
                 all_preds.extend(p[:len(l)])
 
-    # Exclude the "O" label and other special tokens if necessary, but using 'micro' average
-    # on all valid tokens is typically fine for the initial evaluation.
+                # Exclude the "O" label and other special tokens if necessary, but using 'micro' average
     precision, recall, f1, _ = precision_recall_fscore_support(all_labels, all_preds, average="micro", zero_division=0)
     return precision, recall, f1
 
 
 # -------------------------
-# Step 5: Main Pipeline (Training) - MODIFIED LABELS + FILE PATH FIX
+# Step 5: Main Pipeline (Training) - MODIFIED MODEL/TOKENIZER LOADING
 # -------------------------
 def main(args):
     # LABELS UPDATED: Added SECTION_HEADING and PASSAGE
@@ -272,34 +683,26 @@ def main(args):
     label2id = {l: i for i, l in enumerate(labels)}
     id2label = {i: l for l, i in label2id.items()}
 
-    # --- FIX for FileNotFoundError: Use a temporary directory for intermediate files ---
+    # --- SETUP: Use a temporary directory for intermediate files ---
     TEMP_DIR = "temp_intermediate_files"
     os.makedirs(TEMP_DIR, exist_ok=True)
     print(f"\n--- SETUP PHASE: Created temp directory: {TEMP_DIR} ---")
 
-    # 1. Preprocess and save the initial training data
+    # 1. Preprocess
     print("\n--- START PHASE: PREPROCESSING ---")
-
-    # FIX: Prepend the directory path to the file name
     initial_bio_json = os.path.join(TEMP_DIR, "training_data_bio_bboxes.json")
     preprocess_labelstudio(args.input, initial_bio_json)
 
-    # 2. Augment the dataset with translated bboxes
+    # 2. Augment
     print("\n--- START PHASE: AUGMENTATION ---")
-
-    # FIX: Prepend the directory path to the file name
     augmented_bio_json = os.path.join(TEMP_DIR, "augmented_training_data_bio_bboxes.json")
     final_data_path = augment_and_save_dataset(initial_bio_json, augmented_bio_json)
 
-    # Clean up the intermediary file (optional)
-    # import shutil
-    # shutil.rmtree(TEMP_DIR)
-
     # 3. Load and split augmented dataset
     print("\n--- START PHASE: MODEL/DATASET SETUP ---")
-    #MODEL_ID = "heerjtdev/edugenius"
-    tokenizer = LayoutLMv3TokenizerFast.from_pretrained("microsoft/layoutlmv3-base")
-    #tokenizer = LayoutLMv3TokenizerFast.from_pretrained(MODEL_ID)
+
+    # Load tokenizer from the specified Hugging Face ID
+    tokenizer = LayoutLMv3TokenizerFast.from_pretrained(HF_MODEL_ID)
 
     dataset = LayoutDataset(final_data_path, tokenizer, label2id, max_len=args.max_len)
     val_size = int(0.2 * len(dataset))
@@ -314,17 +717,13 @@ def main(args):
     # 4. Initialize and load model
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     print(f"Using device: {device}")
-    # Num_labels is based on the updated 'labels' list
-    model = LayoutLMv3CRF("microsoft/layoutlmv3-base", num_labels=len(labels)).to(device)
-    # model = LayoutLMv3CRF(MODEL_ID, num_labels=len(labels)).to(device)
+
+    # Pass the Hugging Face ID and device to the custom model wrapper
+    model = LayoutLMv3CRF(HF_MODEL_ID, num_labels=len(labels), device=device).to(device)
+
     ckpt_path = "checkpoints/layoutlmv3_crf_passage.pth"
     os.makedirs("checkpoints", exist_ok=True)
     if os.path.exists(ckpt_path):
-        # NOTE: Loading an old checkpoint will likely fail now because num_labels has changed,
-        # unless the old checkpoint had the *exact* same number of labels.
-        # It is recommended to start training from scratch.
-        # print(f"🔄 Loading checkpoint from {ckpt_path}")
-        # model.load_state_dict(torch.load(ckpt_path, map_location=device))
         print(f"⚠️ Starting fresh training. Old checkpoint {ckpt_path} may be incompatible with new label count.")
 
     optimizer = AdamW(model.parameters(), lr=args.lr)
@@ -343,10 +742,8 @@ def main(args):
         print(f"💾 Model saved at {ckpt_path}")
 
 
-
-
 # -------------------------
-# Step 7: Main Execution (Unchanged)
+# Step 7: Main Execution
 # -------------------------
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="LayoutLMv3 Fine-tuning and Inference Script.")
@@ -362,4 +759,4 @@ if __name__ == "__main__":
     if args.mode == "train":
         if not args.input:
             parser.error("--input is required for 'train' mode.")
-        main(args)
+        main(args)