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durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00098.json

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+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "24/445",
+      "invoice_date": "18/07/2025",
+      "seller": "SINGH ELECTRONICS H.No. 94, Dhawan Road, Uluberia 261107",
+      "client": "NAKUL ROUT H.No. 889, Bhat Circle, Visakhapatnam 758457",
+      "shipping_address": "55/655, Dar Chowk, Mau 622197",
+      "seller_tax_id": "01NVDPT6891V1ZK",
+      "client_tax_id": "10ZYZAB3179B1Z6",
+      "client_phone": "+916346574312"
+    },
+    "items": [
+      {
+        "item_desc": "Sony Speaker SON-928E",
+        "item_qty": "1",
+        "item_net_price": "89,159.95",
+        "item_net_worth": "89,159.95",
+        "item_vat": "18%",
+        "item_hsn": "8518"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹89,159.95",
+      "total_vat": "₹16,048.79",
+      "total_gross_worth": "₹105,208.74",
+      "total_cgst": "₹8,024.40",
+      "total_sgst": "₹8,024.40",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00099.json

@@ -0,0 +1,40 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "QTM/141",
+      "invoice_date": "04/09/2025",
+      "seller": "STAR ELECTRONICS H.No. 419, Khosla Street, Noida 730951",
+      "client": "VASATIKA KUMAR 767, Om Zila, Chennai 252852",
+      "shipping_address": "767, Om Zila, Chennai 252852",
+      "seller_tax_id": "23UGWSV9551G1ZL",
+      "client_tax_id": "18KJGHJ6973L1ZS",
+      "client_phone": "2691355356"
+    },
+    "items": [
+      {
+        "item_desc": "Hero Bike HER-977C",
+        "item_qty": "1",
+        "item_net_price": "28,458.66",
+        "item_net_worth": "28,458.66",
+        "item_vat": "28%",
+        "item_hsn": "8711"
+      },
+      {
+        "item_desc": "Fire-Boltt Smartwatch FIR-663O",
+        "item_qty": "1",
+        "item_net_price": "49,694.62",
+        "item_net_worth": "49,694.62",
+        "item_vat": "28%",
+        "item_hsn": "8517"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹78,153.28",
+      "total_vat": "₹21,882.92",
+      "total_gross_worth": "₹100,036.20",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹21,882.92"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00100.json

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+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "PYF/465",
+      "invoice_date": "14/07/2025",
+      "seller": "MOBILE POINT 42/53, Srivastava Nagar, Howrah-350238",
+      "client": "EKANI VENKATESH 10/16, Buch, Tiruppur 632411",
+      "shipping_address": "10/16, Buch, Tiruppur 632411",
+      "seller_tax_id": "01YAMYQ9558X1ZQ",
+      "client_tax_id": "07NLFBX9801U1Z1",
+      "client_phone": "3071027108"
+    },
+    "items": [
+      {
+        "item_desc": "Hero Bike HER-732U",
+        "item_qty": "1",
+        "item_net_price": "23,593.07",
+        "item_net_worth": "23,593.07",
+        "item_vat": "18%",
+        "item_hsn": "8711"
+      },
+      {
+        "item_desc": "Xiaomi Tablets XIA-608X",
+        "item_qty": "2",
+        "item_net_price": "79,417.67",
+        "item_net_worth": "158,835.34",
+        "item_vat": "18%",
+        "item_hsn": "8471"
+      },
+      {
+        "item_desc": "Hero Bike HER-246I",
+        "item_qty": "2",
+        "item_net_price": "73,169.55",
+        "item_net_worth": "146,339.10",
+        "item_vat": "18%",
+        "item_hsn": "8711"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹328,767.51",
+      "total_vat": "₹59,178.15",
+      "total_gross_worth": "₹387,945.66",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹59,178.15"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00101.json

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+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "24/992",
+      "invoice_date": "19/06/2025",
+      "seller": "SUPREME ELECTRONICS H.No. 91, Baria Chowk, Gorakhpur-824779",
+      "client": "UPMA SARAN 216, Wali Path, Chinsurah-118751",
+      "shipping_address": "216, Wali Path, Chinsurah-118751",
+      "seller_tax_id": "37OVINF3874R1ZB",
+      "client_tax_id": "17ICLYY1888F1Z8",
+      "client_phone": "06293207209"
+    },
+    "items": [
+      {
+        "item_desc": "Boat Speaker BOA-326J",
+        "item_qty": "2",
+        "item_net_price": "18,571.37",
+        "item_net_worth": "37,142.74",
+        "item_vat": "18%",
+        "item_hsn": "8518"
+      },
+      {
+        "item_desc": "IFB Microwave IFB-313U",
+        "item_qty": "2",
+        "item_net_price": "74,261.90",
+        "item_net_worth": "148,523.80",
+        "item_vat": "18%",
+        "item_hsn": "8516"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹185,666.54",
+      "total_vat": "₹33,419.98",
+      "total_gross_worth": "₹219,086.52",
+      "total_cgst": "₹16,709.99",
+      "total_sgst": "₹16,709.99",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00102.json

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+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "FIR/975",
+      "invoice_date": "16/07/2025",
+      "seller": "PATEL MOBILES 68/295, Tella Zila, Kurnool-594681",
+      "client": "RAKSHA BHATNAGAR 46, Ramachandran Circle, Udaipur-943032",
+      "shipping_address": "H.No. 07, Mall Street, Fatehpur-644619",
+      "seller_tax_id": "25EOVOT1628Q1ZA",
+      "client_tax_id": "27MFJZY7459H1ZD",
+      "client_phone": "+919069454273"
+    },
+    "items": [
+      {
+        "item_desc": "Canon Camera CAN-353M",
+        "item_qty": "1",
+        "item_net_price": "31,044.45",
+        "item_net_worth": "31,044.45",
+        "item_vat": "18%",
+        "item_hsn": "8525"
+      },
+      {
+        "item_desc": "Apple Tablets APP-140A",
+        "item_qty": "1",
+        "item_net_price": "18,391.45",
+        "item_net_worth": "18,391.45",
+        "item_vat": "18%",
+        "item_hsn": "8471"
+      },
+      {
+        "item_desc": "Sennheiser Headphones SEN-133X",
+        "item_qty": "1",
+        "item_net_price": "44,683.39",
+        "item_net_worth": "44,683.39",
+        "item_vat": "18%",
+        "item_hsn": "8518"
+      },
+      {
+        "item_desc": "Whirlpool Refrigerator WHI-351I",
+        "item_qty": "1",
+        "item_net_price": "26,343.27",
+        "item_net_worth": "26,343.27",
+        "item_vat": "18%",
+        "item_hsn": "8418"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹120,462.56",
+      "total_vat": "₹21,683.26",
+      "total_gross_worth": "₹142,145.82",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹21,683.26"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00103.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "KRN/824",
+      "invoice_date": "08/10/2025",
+      "seller": "SUPREME ELECTRONICS 89, Pau Path, Guntur-089782",
+      "client": "CHATRESH SHANKAR 89/727, Pathak Ganj, Arrah 705007",
+      "shipping_address": "89/727, Pathak Ganj, Arrah 705007",
+      "seller_tax_id": "08WPERQ6549L1ZX",
+      "client_tax_id": "21NTWDJ7661I1Z8",
+      "client_phone": "04457460836"
+    },
+    "items": [
+      {
+        "item_desc": "Bosch Washing Machine BOS-809Q",
+        "item_qty": "1",
+        "item_net_price": "34,940.07",
+        "item_net_worth": "34,940.07",
+        "item_vat": "18%",
+        "item_hsn": "8450"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹34,940.07",
+      "total_vat": "₹6,289.21",
+      "total_gross_worth": "₹41,229.28",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹6,289.21"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00104.json

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+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2023-5808",
+      "invoice_date": "27/02/2025",
+      "seller": "PRADHAN MOBILE SHOP 084, Mitra Road, Avadi-026388",
+      "client": "TAMANNA LAD 688, Karpe Ganj, North Dumdum-158379",
+      "shipping_address": "688, Karpe Ganj, North Dumdum-158379",
+      "seller_tax_id": "11GJWJF3744Q1ZM",
+      "client_tax_id": "21SQVTR1048R1ZJ",
+      "client_phone": "8557079921"
+    },
+    "items": [
+      {
+        "item_desc": "Nikon Camera NIK-276D",
+        "item_qty": "1",
+        "item_net_price": "73,286.60",
+        "item_net_worth": "73,286.60",
+        "item_vat": "12%",
+        "item_hsn": "8525"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹73,286.60",
+      "total_vat": "₹8,794.39",
+      "total_gross_worth": "₹82,080.99",
+      "total_cgst": "₹4,397.20",
+      "total_sgst": "₹4,397.20",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00105.json

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+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2024-7091",
+      "invoice_date": "03/01/2025",
+      "seller": "ROYAL MOBILES 51, Chaudhari Road, Singrauli-143462",
+      "client": "ISHAAN LUTHRA H.No. 625, Tella Path, Parbhani-891298",
+      "shipping_address": "93/899, Kulkarni Ganj, Jalna-943231",
+      "seller_tax_id": "17DTRXJ5088L1Z8",
+      "client_tax_id": "22NKSQO6522O1Z1",
+      "client_phone": "02785977049"
+    },
+    "items": [
+      {
+        "item_desc": "Nikon Camera NIK-322B",
+        "item_qty": "1",
+        "item_net_price": "41,241.36",
+        "item_net_worth": "41,241.36",
+        "item_vat": "12%",
+        "item_hsn": "8525"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹41,241.36",
+      "total_vat": "₹4,948.96",
+      "total_gross_worth": "₹46,190.32",
+      "total_cgst": "₹2,474.48",
+      "total_sgst": "₹2,474.48",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00106.json

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+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2024-9155",
+      "invoice_date": "01/06/2025",
+      "seller": "KUMAR TECH SOLUTIONS H.No. 968, Sheth Circle, Katihar 866943",
+      "client": "KRISHNA RAJ 01/618, Bhatia Chowk, Bathinda 328667",
+      "shipping_address": "01/618, Bhatia Chowk, Bathinda 328667",
+      "seller_tax_id": "04ADTEH0969N1ZN",
+      "client_tax_id": "21YDXEY9379F1ZQ",
+      "client_phone": "05841457512"
+    },
+    "items": [
+      {
+        "item_desc": "Bajaj Microwave BAJ-603K",
+        "item_qty": "1",
+        "item_net_price": "67,222.97",
+        "item_net_worth": "67,222.97",
+        "item_vat": "18%",
+        "item_hsn": "8516"
+      },
+      {
+        "item_desc": "Fire-Boltt Smartwatch FIR-486D",
+        "item_qty": "2",
+        "item_net_price": "89,810.93",
+        "item_net_worth": "179,621.86",
+        "item_vat": "18%",
+        "item_hsn": "8517"
+      },
+      {
+        "item_desc": "Asus Laptops ASU-428Y",
+        "item_qty": "2",
+        "item_net_price": "59,259.20",
+        "item_net_worth": "118,518.40",
+        "item_vat": "18%",
+        "item_hsn": "8471"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹365,363.23",
+      "total_vat": "₹65,765.38",
+      "total_gross_worth": "₹431,128.61",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹65,765.38"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00107.json

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+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "24/248",
+      "invoice_date": "12/01/2025",
+      "seller": "GADGET GALAXY 89, Karan Chowk, Rajkot-359370",
+      "client": "INDIRA AGATE H.No. 620, Dyal Chowk, Mira-Bhayandar 394017",
+      "shipping_address": "94/43, Ganesh Zila, Nellore-950589",
+      "seller_tax_id": "19EYZHV2036R1Z4",
+      "client_tax_id": "30CLTCA5070E1ZK",
+      "client_phone": "+914457779169"
+    },
+    "items": [
+      {
+        "item_desc": "Noise Smartwatch NOI-134T",
+        "item_qty": "2",
+        "item_net_price": "53,206.89",
+        "item_net_worth": "106,413.78",
+        "item_vat": "28%",
+        "item_hsn": "8517"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹106,413.78",
+      "total_vat": "₹29,795.86",
+      "total_gross_worth": "₹136,209.64",
+      "total_cgst": "₹14,897.93",
+      "total_sgst": "₹14,897.93",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00108.json

@@ -0,0 +1,56 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "25/693",
+      "invoice_date": "09/04/2025",
+      "seller": "ELECTRONICS WORLD H.No. 95, Shere Circle, Sambhal 965379",
+      "client": "YOCHANA SAGAR 89, Menon Chowk, Akola-755035",
+      "shipping_address": "89, Menon Chowk, Akola-755035",
+      "seller_tax_id": "14WMMUI8520I1ZD",
+      "client_tax_id": "16ZSGFG7919K1ZZ",
+      "client_phone": "2460740785"
+    },
+    "items": [
+      {
+        "item_desc": "Whirlpool Refrigerator WHI-717Z",
+        "item_qty": "1",
+        "item_net_price": "63,621.78",
+        "item_net_worth": "63,621.78",
+        "item_vat": "28%",
+        "item_hsn": "8418"
+      },
+      {
+        "item_desc": "Amazfit Smartwatch AMA-787C",
+        "item_qty": "1",
+        "item_net_price": "62,686.42",
+        "item_net_worth": "62,686.42",
+        "item_vat": "28%",
+        "item_hsn": "8517"
+      },
+      {
+        "item_desc": "IFB Washing Machine IFB-567S",
+        "item_qty": "1",
+        "item_net_price": "37,860.68",
+        "item_net_worth": "37,860.68",
+        "item_vat": "28%",
+        "item_hsn": "8450"
+      },
+      {
+        "item_desc": "Samsung Tablets SAM-979B",
+        "item_qty": "1",
+        "item_net_price": "58,523.11",
+        "item_net_worth": "58,523.11",
+        "item_vat": "28%",
+        "item_hsn": "8471"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹222,691.99",
+      "total_vat": "₹62,353.76",
+      "total_gross_worth": "₹285,045.75",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹62,353.76"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00109.json

@@ -0,0 +1,48 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2024-8559",
+      "invoice_date": "22/08/2025",
+      "seller": "SINGH ELECTRONICS H.No. 16, Narang Marg, Bhilai-886053",
+      "client": "LOHIT SAINI H.No. 90, Sibal Chowk, Jalandhar-417638",
+      "shipping_address": "H.No. 90, Sibal Chowk, Jalandhar-417638",
+      "seller_tax_id": "30OETKR5037I1ZM",
+      "client_tax_id": "30UCBLI3183J1ZW",
+      "client_phone": "00021072997"
+    },
+    "items": [
+      {
+        "item_desc": "Royal Enfield Bike ROY-658Q",
+        "item_qty": "2",
+        "item_net_price": "75,993.93",
+        "item_net_worth": "151,987.86",
+        "item_vat": "28%",
+        "item_hsn": "8711"
+      },
+      {
+        "item_desc": "Samsung Smartphones SAM-465P IMEI: 288273677741554",
+        "item_qty": "2",
+        "item_net_price": "59,653.91",
+        "item_net_worth": "119,307.82",
+        "item_vat": "28%",
+        "item_hsn": "8517"
+      },
+      {
+        "item_desc": "Nikon Camera NIK-639J",
+        "item_qty": "2",
+        "item_net_price": "45,985.37",
+        "item_net_worth": "91,970.74",
+        "item_vat": "28%",
+        "item_hsn": "8525"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹363,266.42",
+      "total_vat": "₹101,714.60",
+      "total_gross_worth": "₹464,981.02",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹101,714.60"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00110.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "XOC/635",
+      "invoice_date": "21/05/2025",
+      "seller": "FUTURE TECH 82, Ratti Nagar, Chittoor 762981",
+      "client": "VRITTI KADE 00/55, Keer Zila, Mehsana 507142",
+      "shipping_address": "00/55, Keer Zila, Mehsana 507142",
+      "seller_tax_id": "08ADQUT6842Y1ZG",
+      "client_tax_id": "01MZMPB6749Z1ZF",
+      "client_phone": "03568036282"
+    },
+    "items": [
+      {
+        "item_desc": "LG Refrigerator LG-983C",
+        "item_qty": "2",
+        "item_net_price": "15,347.48",
+        "item_net_worth": "30,694.96",
+        "item_vat": "18%",
+        "item_hsn": "8418"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹30,694.96",
+      "total_vat": "₹5,525.09",
+      "total_gross_worth": "₹36,220.05",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹5,525.09"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00111.json

@@ -0,0 +1,40 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "25/564",
+      "invoice_date": "01/03/2025",
+      "seller": "PRADHAN MOBILE SHOP 117, Raghavan Path, Allahabad-329996",
+      "client": "BALVEER BISWAS 79/27, Mukhopadhyay Chowk, Sultan Pur Majra 315368",
+      "shipping_address": "79/27, Mukhopadhyay Chowk, Sultan Pur Majra 315368",
+      "seller_tax_id": "20BJIFC9618J1Z2",
+      "client_tax_id": "17MHQRE0693M1ZP",
+      "client_phone": "+917640165602"
+    },
+    "items": [
+      {
+        "item_desc": "Whirlpool Refrigerator WHI-517P",
+        "item_qty": "1",
+        "item_net_price": "17,382.88",
+        "item_net_worth": "17,382.88",
+        "item_vat": "18%",
+        "item_hsn": "8418"
+      },
+      {
+        "item_desc": "Whirlpool Washing Machine WHI-986E",
+        "item_qty": "2",
+        "item_net_price": "27,174.51",
+        "item_net_worth": "54,349.02",
+        "item_vat": "18%",
+        "item_hsn": "8450"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹71,731.90",
+      "total_vat": "₹12,911.74",
+      "total_gross_worth": "₹84,643.64",
+      "total_cgst": "₹6,455.87",
+      "total_sgst": "₹6,455.87",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00112.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "VQW/956",
+      "invoice_date": "28/08/2025",
+      "seller": "PATEL MOBILES 563, Raja Street, Kirari Suleman Nagar 769599",
+      "client": "ADWETA IYER 99/03, Ratta Path, Kakinada-787080",
+      "shipping_address": "99/03, Ratta Path, Kakinada-787080",
+      "seller_tax_id": "07WNGFK2222K1ZP",
+      "client_tax_id": "31RKVXN9067D1ZR",
+      "client_phone": "01568436776"
+    },
+    "items": [
+      {
+        "item_desc": "Lenovo Laptops LEN-653W",
+        "item_qty": "2",
+        "item_net_price": "31,489.59",
+        "item_net_worth": "62,979.18",
+        "item_vat": "18%",
+        "item_hsn": "8471"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹62,979.18",
+      "total_vat": "₹11,336.25",
+      "total_gross_worth": "₹74,315.43",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹11,336.25"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00113.json

@@ -0,0 +1,56 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "HDM/126",
+      "invoice_date": "06/09/2025",
+      "seller": "PRADHAN MOBILE SHOP H.No. 773, Narain Zila, Sultan Pur Majra 636938",
+      "client": "IRA PANT 62/82, Panchal Road, Bahraich-198917",
+      "shipping_address": "62/82, Panchal Road, Bahraich-198917",
+      "seller_tax_id": "16QGGPA6141N1Z3",
+      "client_tax_id": "32KRMLM5479M1Z8",
+      "client_phone": "9251026455"
+    },
+    "items": [
+      {
+        "item_desc": "JBL Headphones JBL-937V",
+        "item_qty": "1",
+        "item_net_price": "81,040.68",
+        "item_net_worth": "81,040.68",
+        "item_vat": "12%",
+        "item_hsn": "8518"
+      },
+      {
+        "item_desc": "JBL Speaker JBL-378H",
+        "item_qty": "2",
+        "item_net_price": "13,222.16",
+        "item_net_worth": "26,444.32",
+        "item_vat": "12%",
+        "item_hsn": "8518"
+      },
+      {
+        "item_desc": "Haier Refrigerator HAI-738Z",
+        "item_qty": "2",
+        "item_net_price": "28,586.84",
+        "item_net_worth": "57,173.68",
+        "item_vat": "12%",
+        "item_hsn": "8418"
+      },
+      {
+        "item_desc": "Whirlpool Microwave WHI-582X",
+        "item_qty": "1",
+        "item_net_price": "36,178.24",
+        "item_net_worth": "36,178.24",
+        "item_vat": "12%",
+        "item_hsn": "8516"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹200,836.92",
+      "total_vat": "₹24,100.43",
+      "total_gross_worth": "₹224,937.35",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹24,100.43"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00114.json

@@ -0,0 +1,56 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "XUU/911",
+      "invoice_date": "21/04/2025",
+      "seller": "RAM ELECTRONICS 73, Singh Circle, Unnao-169998",
+      "client": "BISHAKHA PALAN 38/27, Sharma, Chandrapur 803194",
+      "shipping_address": "38/27, Sharma, Chandrapur 803194",
+      "seller_tax_id": "32IPFHB0623H1ZC",
+      "client_tax_id": "35UFZAX8004U1ZZ",
+      "client_phone": "1610717465"
+    },
+    "items": [
+      {
+        "item_desc": "LG Air Conditioner LG-981P",
+        "item_qty": "2",
+        "item_net_price": "56,233.03",
+        "item_net_worth": "112,466.06",
+        "item_vat": "28%",
+        "item_hsn": "8415"
+      },
+      {
+        "item_desc": "Motorola Smartphones MOT-602F IMEI: 325554545927940",
+        "item_qty": "1",
+        "item_net_price": "22,637.62",
+        "item_net_worth": "22,637.62",
+        "item_vat": "28%",
+        "item_hsn": "8517"
+      },
+      {
+        "item_desc": "OnePlus Television ONE-860B",
+        "item_qty": "2",
+        "item_net_price": "58,336.16",
+        "item_net_worth": "116,672.32",
+        "item_vat": "28%",
+        "item_hsn": "8528"
+      },
+      {
+        "item_desc": "LG Television LG-251T",
+        "item_qty": "2",
+        "item_net_price": "48,575.92",
+        "item_net_worth": "97,151.84",
+        "item_vat": "28%",
+        "item_hsn": "8528"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹348,927.84",
+      "total_vat": "₹97,699.80",
+      "total_gross_worth": "₹446,627.64",
+      "total_cgst": "₹48,849.90",
+      "total_sgst": "₹48,849.90",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00115.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2024-4490",
+      "invoice_date": "27/03/2025",
+      "seller": "ELECTRONICS WORLD H.No. 35, Krish Nagar, Loni-905294",
+      "client": "JHALAK BEN 95/517, Kibe Path, Patna 289678",
+      "shipping_address": "95/517, Kibe Path, Patna 289678",
+      "seller_tax_id": "18TLOMD0277V1Z3",
+      "client_tax_id": "15IJCWR4774Q1ZU",
+      "client_phone": "00645169870"
+    },
+    "items": [
+      {
+        "item_desc": "Sony Camera SON-859Z",
+        "item_qty": "1",
+        "item_net_price": "64,078.80",
+        "item_net_worth": "64,078.80",
+        "item_vat": "12%",
+        "item_hsn": "8525"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹64,078.80",
+      "total_vat": "₹7,689.46",
+      "total_gross_worth": "₹71,768.26",
+      "total_cgst": "₹3,844.73",
+      "total_sgst": "₹3,844.73",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00116.json

@@ -0,0 +1,56 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "24/627",
+      "invoice_date": "14/02/2025",
+      "seller": "RAM ELECTRONICS 789, Prashad Marg, Kharagpur 012697",
+      "client": "LABAN RATTA 86/891, Chakraborty Street, Rajahmundry 193424",
+      "shipping_address": "86/891, Chakraborty Street, Rajahmundry 193424",
+      "seller_tax_id": "18CTVLV9556Z1Z5",
+      "client_tax_id": "22PFNYL6627U1Z3",
+      "client_phone": "05739345152"
+    },
+    "items": [
+      {
+        "item_desc": "Sennheiser Headphones SEN-124K",
+        "item_qty": "2",
+        "item_net_price": "63,112.98",
+        "item_net_worth": "126,225.96",
+        "item_vat": "28%",
+        "item_hsn": "8518"
+      },
+      {
+        "item_desc": "Realme Smartphones REA-101W IMEI: 033992540322637",
+        "item_qty": "1",
+        "item_net_price": "51,995.49",
+        "item_net_worth": "51,995.49",
+        "item_vat": "28%",
+        "item_hsn": "8517"
+      },
+      {
+        "item_desc": "Dell Laptops DEL-962N",
+        "item_qty": "2",
+        "item_net_price": "60,927.42",
+        "item_net_worth": "121,854.84",
+        "item_vat": "28%",
+        "item_hsn": "8471"
+      },
+      {
+        "item_desc": "Samsung Tablets SAM-108I",
+        "item_qty": "2",
+        "item_net_price": "87,279.97",
+        "item_net_worth": "174,559.94",
+        "item_vat": "28%",
+        "item_hsn": "8471"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹474,636.23",
+      "total_vat": "₹132,898.14",
+      "total_gross_worth": "₹607,534.37",
+      "total_cgst": "₹66,449.07",
+      "total_sgst": "₹66,449.07",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00117.json

@@ -0,0 +1,40 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2023-1180",
+      "invoice_date": "28/10/2025",
+      "seller": "FUTURE TECH 01, Basak Circle, Khammam 993575",
+      "client": "VASATIKA MITTER 33, Manne Zila, Dhanbad-365039",
+      "shipping_address": "33, Manne Zila, Dhanbad-365039",
+      "seller_tax_id": "03JJBIK3171Z1ZV",
+      "client_tax_id": "14NPFGB1758F1ZU",
+      "client_phone": "6235362527"
+    },
+    "items": [
+      {
+        "item_desc": "Bajaj Bike BAJ-532W",
+        "item_qty": "1",
+        "item_net_price": "51,235.34",
+        "item_net_worth": "51,235.34",
+        "item_vat": "12%",
+        "item_hsn": "8711"
+      },
+      {
+        "item_desc": "Xiaomi Tablets XIA-267T",
+        "item_qty": "1",
+        "item_net_price": "92,741.77",
+        "item_net_worth": "92,741.77",
+        "item_vat": "12%",
+        "item_hsn": "8471"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹143,977.11",
+      "total_vat": "₹17,277.25",
+      "total_gross_worth": "₹161,254.36",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹17,277.25"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00118.json

@@ -0,0 +1,56 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "WQN/195",
+      "invoice_date": "13/02/2025",
+      "seller": "FUTURE TECH 365, Boase Circle, Faridabad 657785",
+      "client": "ISHANVI TRIPATHI 76/074, Bandi Circle, Pali-313847",
+      "shipping_address": "69/35, Sridhar Ganj, Amaravati-094038",
+      "seller_tax_id": "09AYQAR6200B1Z2",
+      "client_tax_id": "19LGWPF8034P1ZS",
+      "client_phone": "02385293027"
+    },
+    "items": [
+      {
+        "item_desc": "Samsung Smartphones SAM-412H IMEI: 577912016128191",
+        "item_qty": "1",
+        "item_net_price": "74,077.58",
+        "item_net_worth": "74,077.58",
+        "item_vat": "18%",
+        "item_hsn": "8517"
+      },
+      {
+        "item_desc": "JBL Speaker JBL-562H",
+        "item_qty": "2",
+        "item_net_price": "14,511.26",
+        "item_net_worth": "29,022.52",
+        "item_vat": "18%",
+        "item_hsn": "8518"
+      },
+      {
+        "item_desc": "Samsung Television SAM-974A",
+        "item_qty": "2",
+        "item_net_price": "64,368.46",
+        "item_net_worth": "128,736.92",
+        "item_vat": "18%",
+        "item_hsn": "8528"
+      },
+      {
+        "item_desc": "Hero Bike HER-941F",
+        "item_qty": "2",
+        "item_net_price": "55,479.15",
+        "item_net_worth": "110,958.30",
+        "item_vat": "18%",
+        "item_hsn": "8711"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹342,795.32",
+      "total_vat": "₹61,703.16",
+      "total_gross_worth": "₹404,498.48",
+      "total_cgst": "₹30,851.58",
+      "total_sgst": "₹30,851.58",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00119.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2023-2269",
+      "invoice_date": "23/09/2025",
+      "seller": "STAR ELECTRONICS H.No. 25, Reddy, New Delhi-334644",
+      "client": "WILLIAM PAREKH H.No. 69, Nigam, Panchkula 205668",
+      "shipping_address": "H.No. 69, Nigam, Panchkula 205668",
+      "seller_tax_id": "26MXTSM8255U1Z3",
+      "client_tax_id": "23XKUDS6304Y1ZV",
+      "client_phone": "+912805575957"
+    },
+    "items": [
+      {
+        "item_desc": "Apple Tablets APP-513G",
+        "item_qty": "2",
+        "item_net_price": "49,447.84",
+        "item_net_worth": "98,895.68",
+        "item_vat": "28%",
+        "item_hsn": "8471"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹98,895.68",
+      "total_vat": "₹27,690.79",
+      "total_gross_worth": "₹126,586.47",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹27,690.79"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00120.json

@@ -0,0 +1,56 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "24/958",
+      "invoice_date": "02/10/2025",
+      "seller": "SMART ELECTRONICS H.No. 26, Sampath Circle, Bikaner-806898",
+      "client": "MAANAV ACHARYA H.No. 699, Agrawal Street, Phagwara-375459",
+      "shipping_address": "64, Sangha Zila, Nashik 949406",
+      "seller_tax_id": "32YBWJK7438A1ZX",
+      "client_tax_id": "20UNHXE8411O1Z8",
+      "client_phone": "1969776346"
+    },
+    "items": [
+      {
+        "item_desc": "OnePlus Smartphones ONE-659U IMEI: 586887525288261",
+        "item_qty": "2",
+        "item_net_price": "15,914.81",
+        "item_net_worth": "31,829.62",
+        "item_vat": "12%",
+        "item_hsn": "8517"
+      },
+      {
+        "item_desc": "Oppo Smartphones OPP-925Z IMEI: 729627307129886",
+        "item_qty": "2",
+        "item_net_price": "56,522.32",
+        "item_net_worth": "113,044.64",
+        "item_vat": "12%",
+        "item_hsn": "8517"
+      },
+      {
+        "item_desc": "Bose Speaker BOS-932L",
+        "item_qty": "2",
+        "item_net_price": "49,382.89",
+        "item_net_worth": "98,765.78",
+        "item_vat": "12%",
+        "item_hsn": "8518"
+      },
+      {
+        "item_desc": "Bosch Washing Machine BOS-761M",
+        "item_qty": "1",
+        "item_net_price": "12,269.69",
+        "item_net_worth": "12,269.69",
+        "item_vat": "12%",
+        "item_hsn": "8450"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹255,909.73",
+      "total_vat": "₹30,709.17",
+      "total_gross_worth": "₹286,618.90",
+      "total_cgst": "₹15,354.58",
+      "total_sgst": "₹15,354.58",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00121.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "25/666",
+      "invoice_date": "11/04/2025",
+      "seller": "KUMAR TECH SOLUTIONS 17, Sharma Chowk, Surat-374455",
+      "client": "AYUSH KEER H.No. 13, Tailor Street, Kanpur 942697",
+      "shipping_address": "H.No. 55, Loyal Nagar, Bhilai 189360",
+      "seller_tax_id": "26YOGUB9569I1Z3",
+      "client_tax_id": "31AXEAL6236O1ZA",
+      "client_phone": "05474453691"
+    },
+    "items": [
+      {
+        "item_desc": "Samsung Tablets SAM-451W",
+        "item_qty": "2",
+        "item_net_price": "21,213.75",
+        "item_net_worth": "42,427.50",
+        "item_vat": "18%",
+        "item_hsn": "8471"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹42,427.50",
+      "total_vat": "₹7,636.95",
+      "total_gross_worth": "₹50,064.45",
+      "total_cgst": "₹3,818.47",
+      "total_sgst": "₹3,818.47",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00122.json

@@ -0,0 +1,48 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2023-6622",
+      "invoice_date": "07/08/2025",
+      "seller": "TECH BAZAAR H.No. 35, Kibe Path, Kolkata-301427",
+      "client": "KEYA NORI 160, Bahri, Nagercoil 990900",
+      "shipping_address": "160, Bahri, Nagercoil 990900",
+      "seller_tax_id": "07XTNTV0197X1ZA",
+      "client_tax_id": "15DZHSY8588G1ZU",
+      "client_phone": "+913443796346"
+    },
+    "items": [
+      {
+        "item_desc": "Apple Smartwatch APP-594S",
+        "item_qty": "2",
+        "item_net_price": "83,855.37",
+        "item_net_worth": "167,710.74",
+        "item_vat": "12%",
+        "item_hsn": "8517"
+      },
+      {
+        "item_desc": "Apple Smartwatch APP-372W",
+        "item_qty": "1",
+        "item_net_price": "15,425.17",
+        "item_net_worth": "15,425.17",
+        "item_vat": "12%",
+        "item_hsn": "8517"
+      },
+      {
+        "item_desc": "Hero Bike HER-494T",
+        "item_qty": "1",
+        "item_net_price": "88,595.21",
+        "item_net_worth": "88,595.21",
+        "item_vat": "12%",
+        "item_hsn": "8711"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹271,731.12",
+      "total_vat": "₹32,607.73",
+      "total_gross_worth": "₹304,338.85",
+      "total_cgst": "₹16,303.87",
+      "total_sgst": "₹16,303.87",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00123.json

@@ -0,0 +1,48 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2023-1537",
+      "invoice_date": "18/06/2025",
+      "seller": "SMART ELECTRONICS H.No. 66, Keer Circle, Shahjahanpur 434288",
+      "client": "SUDIKSHA KUMAR 10/973, Balan Street, Tinsukia-096943",
+      "shipping_address": "10/973, Balan Street, Tinsukia-096943",
+      "seller_tax_id": "34WSCCK4212N1Z7",
+      "client_tax_id": "19DMKAW4997U1Z3",
+      "client_phone": "08537261970"
+    },
+    "items": [
+      {
+        "item_desc": "Samsung Microwave SAM-222K",
+        "item_qty": "1",
+        "item_net_price": "71,732.57",
+        "item_net_worth": "71,732.57",
+        "item_vat": "18%",
+        "item_hsn": "8516"
+      },
+      {
+        "item_desc": "Apple Tablets APP-867K",
+        "item_qty": "1",
+        "item_net_price": "93,096.30",
+        "item_net_worth": "93,096.30",
+        "item_vat": "18%",
+        "item_hsn": "8471"
+      },
+      {
+        "item_desc": "Mi Television MI-382X",
+        "item_qty": "1",
+        "item_net_price": "73,275.35",
+        "item_net_worth": "73,275.35",
+        "item_vat": "18%",
+        "item_hsn": "8528"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹238,104.22",
+      "total_vat": "₹42,858.76",
+      "total_gross_worth": "₹280,962.98",
+      "total_cgst": "₹21,429.38",
+      "total_sgst": "₹21,429.38",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00124.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2024-1367",
+      "invoice_date": "13/09/2025",
+      "seller": "DIGITAL STORE 80, Amble Chowk, Firozabad-813833",
+      "client": "WAIDA RAJ 20, Sem Marg, Panchkula 273475",
+      "shipping_address": "20, Sem Marg, Panchkula 273475",
+      "seller_tax_id": "33ATZSH8305Z1ZY",
+      "client_tax_id": "31AYERX8339T1ZS",
+      "client_phone": "6721751676"
+    },
+    "items": [
+      {
+        "item_desc": "Sony Television SON-145X",
+        "item_qty": "1",
+        "item_net_price": "37,071.39",
+        "item_net_worth": "37,071.39",
+        "item_vat": "12%",
+        "item_hsn": "8528"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹37,071.39",
+      "total_vat": "₹4,448.57",
+      "total_gross_worth": "₹41,519.96",
+      "total_cgst": "₹2,224.28",
+      "total_sgst": "₹2,224.28",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00125.json

@@ -0,0 +1,40 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "IKI/259",
+      "invoice_date": "19/10/2025",
+      "seller": "SINGH ELECTRONICS 98/106, Balasubramanian Chowk, Barasat 497231",
+      "client": "CHAKRADEV SODHI H.No. 296, Bansal Nagar, Mango-721110",
+      "shipping_address": "747, Palla Ganj, Moradabad-553778",
+      "seller_tax_id": "21IXMAP6372E1Z0",
+      "client_tax_id": "23FCYBV9294M1ZT",
+      "client_phone": "5437178473"
+    },
+    "items": [
+      {
+        "item_desc": "Samsung Smartphones SAM-425U IMEI: 658417988934991",
+        "item_qty": "2",
+        "item_net_price": "60,880.52",
+        "item_net_worth": "121,761.04",
+        "item_vat": "28%",
+        "item_hsn": "8517"
+      },
+      {
+        "item_desc": "Sony Camera SON-574V",
+        "item_qty": "1",
+        "item_net_price": "74,278.02",
+        "item_net_worth": "74,278.02",
+        "item_vat": "28%",
+        "item_hsn": "8525"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹196,039.06",
+      "total_vat": "₹54,890.94",
+      "total_gross_worth": "₹250,930.00",
+      "total_cgst": "₹27,445.47",
+      "total_sgst": "₹27,445.47",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00126.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "MDB/293",
+      "invoice_date": "21/09/2025",
+      "seller": "RAM ELECTRONICS 06/670, Balasubramanian Road, Dehradun 853336",
+      "client": "HEMA ARYA H.No. 37, Deshpande Marg, Bihar Sharif-401566",
+      "shipping_address": "H.No. 37, Deshpande Marg, Bihar Sharif-401566",
+      "seller_tax_id": "05NCEOA1268I1ZW",
+      "client_tax_id": "17GUAOD6603E1ZP",
+      "client_phone": "+917446726921"
+    },
+    "items": [
+      {
+        "item_desc": "Fujifilm Camera FUJ-536V",
+        "item_qty": "2",
+        "item_net_price": "42,435.97",
+        "item_net_worth": "84,871.94",
+        "item_vat": "28%",
+        "item_hsn": "8525"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹84,871.94",
+      "total_vat": "₹23,764.14",
+      "total_gross_worth": "₹108,636.08",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹23,764.14"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00127.json

@@ -0,0 +1,56 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "OIZ/155",
+      "invoice_date": "17/10/2025",
+      "seller": "GADGET GALAXY 41, Thakkar, Medininagar-620526",
+      "client": "HARSH DOSHI H.No. 191, Rattan Street, Arrah 569248",
+      "shipping_address": "H.No. 191, Rattan Street, Arrah 569248",
+      "seller_tax_id": "32CMLBZ3364D1Z6",
+      "client_tax_id": "18NGKDL4634H1ZR",
+      "client_phone": "6353402139"
+    },
+    "items": [
+      {
+        "item_desc": "Canon Camera CAN-971B",
+        "item_qty": "1",
+        "item_net_price": "37,594.36",
+        "item_net_worth": "37,594.36",
+        "item_vat": "28%",
+        "item_hsn": "8525"
+      },
+      {
+        "item_desc": "Godrej Refrigerator GOD-712D",
+        "item_qty": "1",
+        "item_net_price": "90,702.12",
+        "item_net_worth": "90,702.12",
+        "item_vat": "28%",
+        "item_hsn": "8418"
+      },
+      {
+        "item_desc": "Whirlpool Washing Machine WHI-244C",
+        "item_qty": "1",
+        "item_net_price": "30,632.61",
+        "item_net_worth": "30,632.61",
+        "item_vat": "28%",
+        "item_hsn": "8450"
+      },
+      {
+        "item_desc": "Samsung Microwave SAM-389G",
+        "item_qty": "1",
+        "item_net_price": "91,822.72",
+        "item_net_worth": "91,822.72",
+        "item_vat": "28%",
+        "item_hsn": "8516"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹250,751.81",
+      "total_vat": "₹70,210.51",
+      "total_gross_worth": "₹320,962.32",
+      "total_cgst": "₹35,105.25",
+      "total_sgst": "₹35,105.25",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00128.json

@@ -0,0 +1,48 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2023-8556",
+      "invoice_date": "05/03/2025",
+      "seller": "RAM ELECTRONICS H.No. 658, Wagle Circle, Bhopal 524765",
+      "client": "LIAM RANGANATHAN 15/245, Zacharia Road, Guntakal-025070",
+      "shipping_address": "15/245, Zacharia Road, Guntakal-025070",
+      "seller_tax_id": "03IGZLJ0943N1ZU",
+      "client_tax_id": "07DKOVJ5404G1ZM",
+      "client_phone": "04454940885"
+    },
+    "items": [
+      {
+        "item_desc": "LG Microwave LG-572P",
+        "item_qty": "2",
+        "item_net_price": "30,171.09",
+        "item_net_worth": "60,342.18",
+        "item_vat": "18%",
+        "item_hsn": "8516"
+      },
+      {
+        "item_desc": "Samsung Microwave SAM-259K",
+        "item_qty": "2",
+        "item_net_price": "44,479.35",
+        "item_net_worth": "88,958.70",
+        "item_vat": "18%",
+        "item_hsn": "8516"
+      },
+      {
+        "item_desc": "Apple Smartphones APP-373M IMEI: 250391310307359",
+        "item_qty": "2",
+        "item_net_price": "47,879.82",
+        "item_net_worth": "95,759.64",
+        "item_vat": "18%",
+        "item_hsn": "8517"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹245,060.52",
+      "total_vat": "₹44,110.89",
+      "total_gross_worth": "₹289,171.41",
+      "total_cgst": "₹22,055.45",
+      "total_sgst": "₹22,055.45",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00129.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2023-4715",
+      "invoice_date": "22/06/2025",
+      "seller": "ROYAL MOBILES 032, Dasgupta Path, Bellary 233434",
+      "client": "MEGHANA DEEP H.No. 596, Tiwari Path, Bardhaman-244662",
+      "shipping_address": "34/37, Dugar Marg, Morbi-319941",
+      "seller_tax_id": "13MXJNG7656O1ZA",
+      "client_tax_id": "05ESBHX7991B1ZQ",
+      "client_phone": "05599789523"
+    },
+    "items": [
+      {
+        "item_desc": "Voltas Air Conditioner VOL-799K",
+        "item_qty": "1",
+        "item_net_price": "80,307.62",
+        "item_net_worth": "80,307.62",
+        "item_vat": "18%",
+        "item_hsn": "8415"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹80,307.62",
+      "total_vat": "₹14,455.37",
+      "total_gross_worth": "₹94,762.99",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹14,455.37"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00130.json

@@ -0,0 +1,56 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "PPB/111",
+      "invoice_date": "23/04/2025",
+      "seller": "ROYAL MOBILES 311, Mutti Zila, Ambattur 729025",
+      "client": "WAIDA SARAN 269, Karnik Circle, Panvel 614977",
+      "shipping_address": "269, Karnik Circle, Panvel 614977",
+      "seller_tax_id": "31MKQCO4338E1Z9",
+      "client_tax_id": "15BRWFJ4091Q1Z5",
+      "client_phone": "06909194581"
+    },
+    "items": [
+      {
+        "item_desc": "Godrej Refrigerator GOD-339K",
+        "item_qty": "2",
+        "item_net_price": "62,262.99",
+        "item_net_worth": "124,525.98",
+        "item_vat": "28%",
+        "item_hsn": "8418"
+      },
+      {
+        "item_desc": "Samsung Refrigerator SAM-147E",
+        "item_qty": "2",
+        "item_net_price": "20,059.16",
+        "item_net_worth": "40,118.32",
+        "item_vat": "28%",
+        "item_hsn": "8418"
+      },
+      {
+        "item_desc": "IFB Washing Machine IFB-260E",
+        "item_qty": "2",
+        "item_net_price": "42,813.35",
+        "item_net_worth": "85,626.70",
+        "item_vat": "28%",
+        "item_hsn": "8450"
+      },
+      {
+        "item_desc": "Noise Smartwatch NOI-471S",
+        "item_qty": "2",
+        "item_net_price": "72,532.30",
+        "item_net_worth": "145,064.60",
+        "item_vat": "28%",
+        "item_hsn": "8517"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹395,335.60",
+      "total_vat": "₹110,693.97",
+      "total_gross_worth": "₹506,029.57",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹110,693.97"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00131.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "24/919",
+      "invoice_date": "22/04/2025",
+      "seller": "GADGET GALAXY H.No. 356, Prabhu Circle, Ozhukarai 098178",
+      "client": "NAKUL SETHI H.No. 200, Singh Zila, Gudivada-047564",
+      "shipping_address": "H.No. 200, Singh Zila, Gudivada-047564",
+      "seller_tax_id": "06TSOLN2393C1ZW",
+      "client_tax_id": "12NLKKT9190A1Z4",
+      "client_phone": "+912959524136"
+    },
+    "items": [
+      {
+        "item_desc": "Daikin Air Conditioner DAI-213I",
+        "item_qty": "1",
+        "item_net_price": "19,046.25",
+        "item_net_worth": "19,046.25",
+        "item_vat": "28%",
+        "item_hsn": "8415"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹19,046.25",
+      "total_vat": "₹5,332.95",
+      "total_gross_worth": "₹24,379.20",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹5,332.95"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00132.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "OXD/164",
+      "invoice_date": "24/06/2025",
+      "seller": "GADGET GALAXY H.No. 29, Sibal Zila, Hosur-448678",
+      "client": "OESHI VERMA 21, Nanda Ganj, Katihar 296989",
+      "shipping_address": "21, Nanda Ganj, Katihar 296989",
+      "seller_tax_id": "25JABCN9059I1ZC",
+      "client_tax_id": "30ZROGB6822C1ZT",
+      "client_phone": "+918212337899"
+    },
+    "items": [
+      {
+        "item_desc": "LG Washing Machine LG-753G",
+        "item_qty": "2",
+        "item_net_price": "53,605.68",
+        "item_net_worth": "107,211.36",
+        "item_vat": "12%",
+        "item_hsn": "8450"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹107,211.36",
+      "total_vat": "₹12,865.36",
+      "total_gross_worth": "₹120,076.72",
+      "total_cgst": "₹6,432.68",
+      "total_sgst": "₹6,432.68",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00133.json

@@ -0,0 +1,40 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "DDR/340",
+      "invoice_date": "22/08/2025",
+      "seller": "GADGET GALAXY 42/703, Kunda Circle, Gopalpur 245669",
+      "client": "KIAAN ARYA H.No. 77, Chakrabarti Street, Darbhanga 643477",
+      "shipping_address": "H.No. 211, Ranganathan Circle, Ongole-190918",
+      "seller_tax_id": "18WPTKC3823G1ZW",
+      "client_tax_id": "34MMVPH8936D1ZS",
+      "client_phone": "4001888124"
+    },
+    "items": [
+      {
+        "item_desc": "Canon Camera CAN-386P",
+        "item_qty": "2",
+        "item_net_price": "59,892.44",
+        "item_net_worth": "119,784.88",
+        "item_vat": "28%",
+        "item_hsn": "8525"
+      },
+      {
+        "item_desc": "Bose Speaker BOS-339K",
+        "item_qty": "2",
+        "item_net_price": "86,320.79",
+        "item_net_worth": "172,641.58",
+        "item_vat": "28%",
+        "item_hsn": "8518"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹292,426.46",
+      "total_vat": "₹81,879.41",
+      "total_gross_worth": "₹374,305.87",
+      "total_cgst": "₹40,939.70",
+      "total_sgst": "₹40,939.70",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00134.json

@@ -0,0 +1,40 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2023-1025",
+      "invoice_date": "03/02/2025",
+      "seller": "VISHWA ELECTRONICS 29, Sehgal Chowk, Vellore 562843",
+      "client": "QUINCY BASAK H.No. 122, Prabhu Ganj, Bhalswa Jahangir Pur 504556",
+      "shipping_address": "46/11, Kale Nagar, Shahjahanpur-252689",
+      "seller_tax_id": "14BVRMR9136K1ZD",
+      "client_tax_id": "15IEUQZ8706G1ZU",
+      "client_phone": "+913656180731"
+    },
+    "items": [
+      {
+        "item_desc": "Whirlpool Refrigerator WHI-692L",
+        "item_qty": "1",
+        "item_net_price": "71,044.35",
+        "item_net_worth": "71,044.35",
+        "item_vat": "12%",
+        "item_hsn": "8418"
+      },
+      {
+        "item_desc": "Whirlpool Microwave WHI-546H",
+        "item_qty": "1",
+        "item_net_price": "22,426.34",
+        "item_net_worth": "22,426.34",
+        "item_vat": "12%",
+        "item_hsn": "8516"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹93,470.69",
+      "total_vat": "₹11,216.48",
+      "total_gross_worth": "₹104,687.17",
+      "total_cgst": "₹5,608.24",
+      "total_sgst": "₹5,608.24",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00135.json

@@ -0,0 +1,56 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2024-9870",
+      "invoice_date": "01/09/2025",
+      "seller": "GADGET GALAXY H.No. 278, Kalla Nagar, Pune-311142",
+      "client": "DEV PRADHAN 16, Warrior Street, Siwan-811361",
+      "shipping_address": "H.No. 08, Dixit Chowk, Rajpur Sonarpur 883642",
+      "seller_tax_id": "13OESXA5795Q1ZK",
+      "client_tax_id": "30OWMHX7900V1ZL",
+      "client_phone": "4773732713"
+    },
+    "items": [
+      {
+        "item_desc": "JBL Speaker JBL-951X",
+        "item_qty": "2",
+        "item_net_price": "70,395.69",
+        "item_net_worth": "140,791.38",
+        "item_vat": "18%",
+        "item_hsn": "8518"
+      },
+      {
+        "item_desc": "Samsung Refrigerator SAM-182D",
+        "item_qty": "1",
+        "item_net_price": "17,909.78",
+        "item_net_worth": "17,909.78",
+        "item_vat": "18%",
+        "item_hsn": "8418"
+      },
+      {
+        "item_desc": "LG Washing Machine LG-187H",
+        "item_qty": "1",
+        "item_net_price": "56,157.07",
+        "item_net_worth": "56,157.07",
+        "item_vat": "18%",
+        "item_hsn": "8450"
+      },
+      {
+        "item_desc": "Godrej Refrigerator GOD-931F",
+        "item_qty": "1",
+        "item_net_price": "30,386.58",
+        "item_net_worth": "30,386.58",
+        "item_vat": "18%",
+        "item_hsn": "8418"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹245,244.81",
+      "total_vat": "₹44,144.07",
+      "total_gross_worth": "₹289,388.88",
+      "total_cgst": "₹22,072.03",
+      "total_sgst": "₹22,072.03",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00136.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "UWN/240",
+      "invoice_date": "28/05/2025",
+      "seller": "PATEL MOBILES 946, Gulati Nagar, Ulhasnagar-959987",
+      "client": "PANINI KADAKIA 43/590, Lata Ganj, Tirunelveli-525490",
+      "shipping_address": "43/590, Lata Ganj, Tirunelveli-525490",
+      "seller_tax_id": "32EYNHQ6092D1Z1",
+      "client_tax_id": "26DCSPE9721T1ZZ",
+      "client_phone": "4239847766"
+    },
+    "items": [
+      {
+        "item_desc": "LG Washing Machine LG-576I",
+        "item_qty": "1",
+        "item_net_price": "79,711.34",
+        "item_net_worth": "79,711.34",
+        "item_vat": "18%",
+        "item_hsn": "8450"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹79,711.34",
+      "total_vat": "₹14,348.04",
+      "total_gross_worth": "₹94,059.38",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹14,348.04"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00137.json

@@ -0,0 +1,56 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2024-6321",
+      "invoice_date": "13/06/2025",
+      "seller": "CITY ELECTRONICS 16/818, Raghavan Chowk, South Dumdum-881687",
+      "client": "QUINCY GOEL 909, Nagy Chowk, Bhatpara-600704",
+      "shipping_address": "909, Nagy Chowk, Bhatpara-600704",
+      "seller_tax_id": "20IJYFX3795E1Z8",
+      "client_tax_id": "16HNTBI3613L1ZE",
+      "client_phone": "2962299465"
+    },
+    "items": [
+      {
+        "item_desc": "TCL Television TCL-549T",
+        "item_qty": "1",
+        "item_net_price": "32,370.78",
+        "item_net_worth": "32,370.78",
+        "item_vat": "12%",
+        "item_hsn": "8528"
+      },
+      {
+        "item_desc": "Samsung Tablets SAM-443F",
+        "item_qty": "1",
+        "item_net_price": "12,472.01",
+        "item_net_worth": "12,472.01",
+        "item_vat": "12%",
+        "item_hsn": "8471"
+      },
+      {
+        "item_desc": "LG Refrigerator LG-950V",
+        "item_qty": "2",
+        "item_net_price": "60,855.35",
+        "item_net_worth": "121,710.70",
+        "item_vat": "12%",
+        "item_hsn": "8418"
+      },
+      {
+        "item_desc": "Sony Camera SON-162D",
+        "item_qty": "2",
+        "item_net_price": "50,584.96",
+        "item_net_worth": "101,169.92",
+        "item_vat": "12%",
+        "item_hsn": "8525"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹267,723.41",
+      "total_vat": "₹32,126.81",
+      "total_gross_worth": "₹299,850.22",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹32,126.81"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00138.json

@@ -0,0 +1,40 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "VXY/564",
+      "invoice_date": "18/01/2025",
+      "seller": "CITY ELECTRONICS 59/485, Murthy Marg, Sasaram 311435",
+      "client": "DARIKA SHUKLA 285, Nigam Nagar, Khandwa 131785",
+      "shipping_address": "285, Nigam Nagar, Khandwa 131785",
+      "seller_tax_id": "21WWCPB9677D1ZV",
+      "client_tax_id": "32JMYQJ1747N1ZB",
+      "client_phone": "08814034520"
+    },
+    "items": [
+      {
+        "item_desc": "IFB Microwave IFB-975D",
+        "item_qty": "1",
+        "item_net_price": "51,054.39",
+        "item_net_worth": "51,054.39",
+        "item_vat": "12%",
+        "item_hsn": "8516"
+      },
+      {
+        "item_desc": "Boat Headphones BOA-202L",
+        "item_qty": "1",
+        "item_net_price": "92,792.24",
+        "item_net_worth": "92,792.24",
+        "item_vat": "12%",
+        "item_hsn": "8518"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹143,846.63",
+      "total_vat": "₹17,261.60",
+      "total_gross_worth": "₹161,108.23",
+      "total_cgst": "₹8,630.80",
+      "total_sgst": "₹8,630.80",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00139.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "25/861",
+      "invoice_date": "28/09/2025",
+      "seller": "STAR ELECTRONICS H.No. 869, Kapadia Ganj, Amravati 900154",
+      "client": "CHAMPAK SINGH 81/10, Anand Zila, Rajkot 526366",
+      "shipping_address": "81/10, Anand Zila, Rajkot 526366",
+      "seller_tax_id": "37HXSTH8871M1ZU",
+      "client_tax_id": "16UPOZW0868H1ZA",
+      "client_phone": "+913094212657"
+    },
+    "items": [
+      {
+        "item_desc": "JBL Headphones JBL-673V",
+        "item_qty": "1",
+        "item_net_price": "28,417.40",
+        "item_net_worth": "28,417.40",
+        "item_vat": "18%",
+        "item_hsn": "8518"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹28,417.40",
+      "total_vat": "₹5,115.13",
+      "total_gross_worth": "₹33,532.53",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹5,115.13"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00140.json

@@ -0,0 +1,48 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "INV-2024-2220",
+      "invoice_date": "22/02/2025",
+      "seller": "MOBILE POINT 95/02, Mani Nagar, Jammu-868295",
+      "client": "ORINDER AHUJA 52, Date Chowk, Katni-682052",
+      "shipping_address": "52, Date Chowk, Katni-682052",
+      "seller_tax_id": "33ZXMMO3593E1Z6",
+      "client_tax_id": "23JTRZN8240Q1ZJ",
+      "client_phone": "04474139368"
+    },
+    "items": [
+      {
+        "item_desc": "Fujifilm Camera FUJ-762Y",
+        "item_qty": "2",
+        "item_net_price": "52,471.59",
+        "item_net_worth": "104,943.18",
+        "item_vat": "12%",
+        "item_hsn": "8525"
+      },
+      {
+        "item_desc": "Samsung Refrigerator SAM-280D",
+        "item_qty": "2",
+        "item_net_price": "93,010.51",
+        "item_net_worth": "186,021.02",
+        "item_vat": "12%",
+        "item_hsn": "8418"
+      },
+      {
+        "item_desc": "Apple Smartwatch APP-162L",
+        "item_qty": "1",
+        "item_net_price": "61,111.51",
+        "item_net_worth": "61,111.51",
+        "item_vat": "12%",
+        "item_hsn": "8517"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹352,075.71",
+      "total_vat": "₹42,249.09",
+      "total_gross_worth": "₹394,324.80",
+      "total_cgst": "₹21,124.54",
+      "total_sgst": "₹21,124.54",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00141.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "24/789",
+      "invoice_date": "24/05/2025",
+      "seller": "STAR ELECTRONICS 90, Shah Zila, Bulandshahr 019215",
+      "client": "REVA SUBRAMANIAM 04, Chakraborty Street, Jehanabad 046777",
+      "shipping_address": "04, Chakraborty Street, Jehanabad 046777",
+      "seller_tax_id": "20MTTXD7792H1Z8",
+      "client_tax_id": "04NWMFZ2015K1ZA",
+      "client_phone": "00315747316"
+    },
+    "items": [
+      {
+        "item_desc": "Bajaj Bike BAJ-928D",
+        "item_qty": "1",
+        "item_net_price": "70,241.19",
+        "item_net_worth": "70,241.19",
+        "item_vat": "18%",
+        "item_hsn": "8711"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹70,241.19",
+      "total_vat": "₹12,643.41",
+      "total_gross_worth": "₹82,884.60",
+      "total_cgst": "₹6,321.71",
+      "total_sgst": "₹6,321.71",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00142.json

@@ -0,0 +1,48 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "25/701",
+      "invoice_date": "20/03/2025",
+      "seller": "STAR ELECTRONICS 42, Dutt Chowk, Sangli-Miraj & Kupwad-049543",
+      "client": "ONVEER RASTOGI 14/03, Choudhary Nagar, Agra 072817",
+      "shipping_address": "14/03, Choudhary Nagar, Agra 072817",
+      "seller_tax_id": "14PQVMZ4030H1ZP",
+      "client_tax_id": "14TURZX1781K1ZM",
+      "client_phone": "7873315903"
+    },
+    "items": [
+      {
+        "item_desc": "Samsung Tablets SAM-711Z",
+        "item_qty": "2",
+        "item_net_price": "27,392.83",
+        "item_net_worth": "54,785.66",
+        "item_vat": "12%",
+        "item_hsn": "8471"
+      },
+      {
+        "item_desc": "Bosch Washing Machine BOS-616K",
+        "item_qty": "2",
+        "item_net_price": "33,824.06",
+        "item_net_worth": "67,648.12",
+        "item_vat": "12%",
+        "item_hsn": "8450"
+      },
+      {
+        "item_desc": "Whirlpool Microwave WHI-517O",
+        "item_qty": "1",
+        "item_net_price": "43,760.02",
+        "item_net_worth": "43,760.02",
+        "item_vat": "12%",
+        "item_hsn": "8516"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹166,193.80",
+      "total_vat": "₹19,943.26",
+      "total_gross_worth": "₹186,137.06",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹19,943.26"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00143.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "YWC/849",
+      "invoice_date": "25/09/2025",
+      "seller": "VISHWA ELECTRONICS 86, Mital Street, Gandhinagar-664138",
+      "client": "LAJITA TARA 62/58, Sethi Chowk, Dewas 550514",
+      "shipping_address": "18/774, Rao, Ujjain-831229",
+      "seller_tax_id": "06IZDWB5662Q1ZE",
+      "client_tax_id": "06DDPYT9810B1ZM",
+      "client_phone": "+916930975916"
+    },
+    "items": [
+      {
+        "item_desc": "Sony Television SON-190P",
+        "item_qty": "1",
+        "item_net_price": "24,151.92",
+        "item_net_worth": "24,151.92",
+        "item_vat": "28%",
+        "item_hsn": "8528"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹24,151.92",
+      "total_vat": "₹6,762.54",
+      "total_gross_worth": "₹30,914.46",
+      "total_cgst": "₹3,381.27",
+      "total_sgst": "₹3,381.27",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00144.json

@@ -0,0 +1,32 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "KNM/654",
+      "invoice_date": "14/10/2025",
+      "seller": "PATEL MOBILES H.No. 02, Golla Ganj, Jamalpur-115045",
+      "client": "IRYA MUNSHI H.No. 20, Issac Chowk, Tirupati 729303",
+      "shipping_address": "H.No. 20, Issac Chowk, Tirupati 729303",
+      "seller_tax_id": "21ROBIF6603W1Z6",
+      "client_tax_id": "27ZJDAO3462U1Z1",
+      "client_phone": "5026534416"
+    },
+    "items": [
+      {
+        "item_desc": "Apple Smartwatch APP-991U",
+        "item_qty": "1",
+        "item_net_price": "87,249.21",
+        "item_net_worth": "87,249.21",
+        "item_vat": "18%",
+        "item_hsn": "8517"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹87,249.21",
+      "total_vat": "₹15,704.86",
+      "total_gross_worth": "₹102,954.07",
+      "total_cgst": "₹7,852.43",
+      "total_sgst": "₹7,852.43",
+      "total_igst": "₹0.00"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00145.json

@@ -0,0 +1,56 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "QFG/601",
+      "invoice_date": "09/02/2025",
+      "seller": "PRADHAN MOBILE SHOP 35/83, Chakraborty, Surat-079291",
+      "client": "KRISHNA DALAL 01/16, Bava Zila, Noida-256678",
+      "shipping_address": "01/16, Bava Zila, Noida-256678",
+      "seller_tax_id": "34CMWVZ4351C1ZL",
+      "client_tax_id": "29YRTPV5955Z1ZL",
+      "client_phone": "05279804922"
+    },
+    "items": [
+      {
+        "item_desc": "Haier Refrigerator HAI-976J",
+        "item_qty": "2",
+        "item_net_price": "67,570.91",
+        "item_net_worth": "135,141.82",
+        "item_vat": "28%",
+        "item_hsn": "8418"
+      },
+      {
+        "item_desc": "Lenovo Tablets LEN-321B",
+        "item_qty": "1",
+        "item_net_price": "78,541.31",
+        "item_net_worth": "78,541.31",
+        "item_vat": "28%",
+        "item_hsn": "8471"
+      },
+      {
+        "item_desc": "Samsung Air Conditioner SAM-106W",
+        "item_qty": "1",
+        "item_net_price": "62,985.89",
+        "item_net_worth": "62,985.89",
+        "item_vat": "28%",
+        "item_hsn": "8415"
+      },
+      {
+        "item_desc": "JBL Speaker JBL-815P",
+        "item_qty": "2",
+        "item_net_price": "44,406.28",
+        "item_net_worth": "88,812.56",
+        "item_vat": "28%",
+        "item_hsn": "8518"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹365,481.58",
+      "total_vat": "₹102,334.84",
+      "total_gross_worth": "₹467,816.42",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹102,334.84"
+    }
+  }
+}

durable_invoices_dataset_v9_fixed_finalrun/annotations/invoice_00146.json

@@ -0,0 +1,40 @@
+{
+  "gt_parse": {
+    "header": {
+      "invoice_number": "25/831",
+      "invoice_date": "06/05/2025",
+      "seller": "GADGET GALAXY H.No. 62, Kar Nagar, Satara-877170",
+      "client": "DIPTA MODY H.No. 483, Dey Nagar, Bhilwara 618153",
+      "shipping_address": "H.No. 483, Dey Nagar, Bhilwara 618153",
+      "seller_tax_id": "31ZOIBF3703A1Z2",
+      "client_tax_id": "30WGANS3834R1ZY",
+      "client_phone": "05779497696"
+    },
+    "items": [
+      {
+        "item_desc": "Bosch Washing Machine BOS-583Z",
+        "item_qty": "1",
+        "item_net_price": "40,700.73",
+        "item_net_worth": "40,700.73",
+        "item_vat": "12%",
+        "item_hsn": "8450"
+      },
+      {
+        "item_desc": "Samsung Smartwatch SAM-952A",
+        "item_qty": "1",
+        "item_net_price": "35,453.00",
+        "item_net_worth": "35,453.00",
+        "item_vat": "12%",
+        "item_hsn": "8517"
+      }
+    ],
+    "summary": {
+      "total_net_worth": "₹76,153.73",
+      "total_vat": "₹9,138.45",
+      "total_gross_worth": "₹85,292.18",
+      "total_cgst": "₹0.00",
+      "total_sgst": "₹0.00",
+      "total_igst": "₹9,138.45"
+    }
+  }
+}

layoutaware_ner.ipynb

@@ -0,0 +1,1408 @@
+{
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+  "metadata": {
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+      "provenance": [],
+      "gpuType": "T4"
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+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
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+    "language_info": {
+      "name": "python"
+    },
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+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
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+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
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+            "box_style": "",
+            "children": [
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+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
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+            "description_tooltip": null,
+            "layout": "IPY_MODEL_b790751bb9284c91920ac87606388ca9",
+            "placeholder": "​",
+            "style": "IPY_MODEL_484383d87bdc4126bf966a94f68aff93",
+            "value": "Map: 100%"
+          }
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+        "12c0bac81f6c433597b9049d65ef3622": {
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+      "execution_count": 3,
+      "metadata": {
+        "id": "AVRjhwEW3M7s"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "PROJECT_PATH = '/content/drive/MyDrive/durable_invoices_dataset_v9_fixed_finalrun'\n",
+        "sys.path.append(PROJECT_PATH)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "os.environ[\"WANDB_DISABLED\"] = \"true\""
+      ],
+      "metadata": {
+        "id": "4KD1SwaF6ur9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import json\n",
+        "import torch\n",
+        "from datasets import load_dataset\n",
+        "from transformers import AutoTokenizer, Trainer, TrainingArguments, HfArgumentParser\n",
+        "from transformers.data.data_collator import DataCollatorForTokenClassification\n",
+        "import numpy as np\n",
+        "from seqeval.metrics import classification_report\n",
+        "\n",
+        "# Import your custom model and its config from the other file\n",
+        "from layout_roberta import LayoutRobertaForTokenClassification, RobertaConfig\n",
+        "\n",
+        "# ===================================================================\n",
+        "# 1. CONFIGURATION\n",
+        "# ===================================================================\n",
+        "class NERConfig:\n",
+        "    # --- Paths ---\n",
+        "    ANNOTATION_FILE = \"/content/drive/MyDrive/durable_invoices_dataset_v9_fixed_finalrun/layout_ner_annotations.jsonl\"\n",
+        "    OUTPUT_DIR = \"/content/drive/MyDrive/durable_invoices_dataset_v9_fixed_finalrun/layout-aware-invoice-ner-model-final\"\n",
+        "\n",
+        "    # --- Model ---\n",
+        "    BASE_MODEL = \"roberta-base\"\n",
+        "\n",
+        "    # --- Training Hyperparameters ---\n",
+        "    LEARNING_RATE = 3e-5\n",
+        "    NUM_EPOCHS = 8 # Layout-aware models can learn quickly\n",
+        "    BATCH_SIZE = 4 # Adjust based on your memory (4 is a safe start)\n",
+        "    WEIGHT_DECAY = 0.01\n",
+        "\n",
+        "config = NERConfig()\n",
+        "\n",
+        "# ===================================================================\n",
+        "# 2. DATA LOADING AND PREPARATION\n",
+        "# ===================================================================\n",
+        "print(\"Loading dataset from:\", config.ANNOTATION_FILE)\n",
+        "full_dataset = load_dataset(\"json\", data_files=config.ANNOTATION_FILE, split=\"train\")\n",
+        "\n",
+        "# Create the mapping from tag names to integers\n",
+        "tags = sorted(list(set([tag for example in full_dataset for tag in example[\"ner_tags\"]])))\n",
+        "tag2id = {tag: i for i, tag in enumerate(tags)}\n",
+        "id2tag = {i: tag for tag, i in tag2id.items()}\n",
+        "label_names = list(tag2id.keys())\n",
+        "\n",
+        "print(f\"Found {len(tags)} unique NER tags.\")\n",
+        "\n",
+        "# Initialize tokenizer. `add_prefix_space` is important for RoBERTa.\n",
+        "tokenizer = AutoTokenizer.from_pretrained(config.BASE_MODEL, add_prefix_space=True)\n",
+        "\n",
+        "def preprocess_data(examples, max_length=512):\n",
+        "    \"\"\"\n",
+        "    Prepares words, bboxes, and tags for the LayoutRoBERTa model.\n",
+        "    \"\"\"\n",
+        "    words = examples['words']\n",
+        "    boxes = examples['bboxes']\n",
+        "    ner_tags = examples['ner_tags']\n",
+        "\n",
+        "    # Tokenize words, this will handle subword splitting\n",
+        "    tokenized_inputs = tokenizer(\n",
+        "        words,\n",
+        "        truncation=True,\n",
+        "        is_split_into_words=True,\n",
+        "        padding=\"max_length\",\n",
+        "        max_length=max_length\n",
+        "    )\n",
+        "\n",
+        "    labels = []\n",
+        "    bboxes_aligned = []\n",
+        "    for i, label_sequence in enumerate(ner_tags):\n",
+        "        word_ids = tokenized_inputs.word_ids(batch_index=i)\n",
+        "        previous_word_idx = None\n",
+        "        label_ids = []\n",
+        "        bbox_sequence = []\n",
+        "\n",
+        "        for word_idx in word_ids:\n",
+        "            if word_idx is None: # Special token like [CLS], [SEP], [PAD]\n",
+        "                label_ids.append(-100)\n",
+        "                bbox_sequence.append([0, 0, 0, 0]) # Use a \"null\" bounding box\n",
+        "            elif word_idx != previous_word_idx: # First subword of a new word\n",
+        "                label_ids.append(tag2id[label_sequence[word_idx]])\n",
+        "                bbox_sequence.append(boxes[i][word_idx])\n",
+        "            else: # Subsequent subword of the same word\n",
+        "                label_ids.append(-100) # Only label the first subword\n",
+        "                bbox_sequence.append(boxes[i][word_idx]) # Propagate the bbox\n",
+        "            previous_word_idx = word_idx\n",
+        "\n",
+        "        labels.append(label_ids)\n",
+        "        bboxes_aligned.append(bbox_sequence)\n",
+        "\n",
+        "    tokenized_inputs[\"labels\"] = labels\n",
+        "    tokenized_inputs[\"bbox\"] = bboxes_aligned\n",
+        "    return tokenized_inputs\n",
+        "\n",
+        "print(\"Preprocessing dataset...\")\n",
+        "processed_dataset = full_dataset.map(preprocess_data, batched=True, remove_columns=full_dataset.column_names)\n",
+        "split_dataset = processed_dataset.train_test_split(test_size=0.15, seed=42)\n",
+        "train_dataset = split_dataset[\"train\"]\n",
+        "eval_dataset = split_dataset[\"test\"]\n",
+        "\n",
+        "# ===================================================================\n",
+        "# 3. METRICS COMPUTATION\n",
+        "# ===================================================================\n",
+        "def compute_metrics(p):\n",
+        "    \"\"\"Computes precision, recall, F1, and accuracy for token classification.\"\"\"\n",
+        "    predictions, labels = p\n",
+        "    predictions = np.argmax(predictions, axis=2)\n",
+        "\n",
+        "    # Remove ignored index (-100)\n",
+        "    true_predictions = [\n",
+        "        [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n",
+        "        for prediction, label in zip(predictions, labels)\n",
+        "    ]\n",
+        "    true_labels = [\n",
+        "        [label_names[l] for (p, l) in zip(prediction, label) if l != -100]\n",
+        "        for prediction, label in zip(predictions, labels)\n",
+        "    ]\n",
+        "\n",
+        "    report = classification_report(true_labels, true_predictions, output_dict=True)\n",
+        "    return {\n",
+        "        \"precision\": report[\"micro avg\"][\"precision\"],\n",
+        "        \"recall\": report[\"micro avg\"][\"recall\"],\n",
+        "        \"f1\": report[\"micro avg\"][\"f1-score\"],\n",
+        "        \"accuracy\": report[\"micro avg\"][\"precision\"], # For seqeval, precision is accuracy\n",
+        "    }\n",
+        "\n",
+        "# ===================================================================\n",
+        "# 4. MODEL TRAINING\n",
+        "# ===================================================================\n",
+        "# Data collator handles dynamic padding for batches\n",
+        "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)\n",
+        "\n",
+        "# Load the custom config and add our layout-specific parameters\n",
+        "model_config = RobertaConfig.from_pretrained(\n",
+        "    config.BASE_MODEL,\n",
+        "    num_labels=len(tags),\n",
+        "    id2label=id2tag,\n",
+        "    label2id=tag2id\n",
+        ")\n",
+        "model_config.max_2d_position_embeddings = 1024 # Assumes coordinates are normalized to [0, 1000]\n",
+        "\n",
+        "# Instantiate our custom model\n",
+        "# `ignore_mismatched_sizes=True` is crucial because we are replacing the embedding layer.\n",
+        "model = LayoutRobertaForTokenClassification.from_pretrained(\n",
+        "    config.BASE_MODEL,\n",
+        "    config=model_config,\n",
+        "    ignore_mismatched_sizes=True\n",
+        ")\n",
+        "\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir=config.OUTPUT_DIR,\n",
+        "    learning_rate=config.LEARNING_RATE,\n",
+        "    per_device_train_batch_size=config.BATCH_SIZE,\n",
+        "    per_device_eval_batch_size=config.BATCH_SIZE,\n",
+        "    num_train_epochs=config.NUM_EPOCHS,\n",
+        "    weight_decay=config.WEIGHT_DECAY,\n",
+        "    evaluation_strategy=\"epoch\",\n",
+        "    save_strategy=\"epoch\",\n",
+        "    load_best_model_at_end=True, # Automatically save the best model\n",
+        "    metric_for_best_model=\"f1\",\n",
+        "    push_to_hub=False,\n",
+        "    logging_steps=20,\n",
+        "    save_total_limit=1, # Only keep the best checkpoint\n",
+        ")\n",
+        "\n",
+        "trainer = Trainer(\n",
+        "    model=model,\n",
+        "    args=training_args,\n",
+        "    train_dataset=train_dataset,\n",
+        "    eval_dataset=eval_dataset,\n",
+        "    tokenizer=tokenizer,\n",
+        "    data_collator=data_collator,\n",
+        "    compute_metrics=compute_metrics, # Add our metrics function\n",
+        ")\n",
+        "\n",
+        "print(\"Starting Layout-Aware NER model training...\")\n",
+        "trainer.train()\n",
+        "\n",
+        "# Save the final best model to a clean directory\n",
+        "final_model_path = os.path.join(config.OUTPUT_DIR, \"best_model\")\n",
+        "trainer.save_model(final_model_path)\n",
+        "print(f\"✓ Training complete! Best model saved to: {final_model_path}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
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+            "1346c30a4e334dd9a153ab10b1fff76a",
+            "8212317092c04d118da6c8f5c623906d"
+          ]
+        },
+        "id": "ygc3ibH03xQA",
+        "outputId": "34d5400f-fd6a-42d8-d364-8983ed6e0963"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/transformers/utils/generic.py:441: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.\n",
+            "  _torch_pytree._register_pytree_node(\n",
+            "/usr/local/lib/python3.12/dist-packages/transformers/utils/generic.py:309: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.\n",
+            "  _torch_pytree._register_pytree_node(\n",
+            "/usr/local/lib/python3.12/dist-packages/transformers/utils/generic.py:309: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.\n",
+            "  _torch_pytree._register_pytree_node(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Loading dataset from: /content/drive/MyDrive/durable_invoices_dataset_v9_fixed_finalrun/layout_ner_annotations.jsonl\n",
+            "Found 26 unique NER tags.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:942: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Preprocessing dataset...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/700 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f288b0483e30432ca828f6a8fa83c170"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some weights of LayoutRobertaForTokenClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['roberta.embeddings.y0_position_embeddings.weight', 'roberta.embeddings.x0_position_embeddings.weight', 'classifier.bias', 'classifier.weight', 'roberta.embeddings.y1_position_embeddings.weight', 'roberta.embeddings.x1_position_embeddings.weight']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
+            "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n",
+            "/usr/local/lib/python3.12/dist-packages/accelerate/accelerator.py:432: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
+            "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False)\n",
+            "  warnings.warn(\n",
+            "You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Starting Layout-Aware NER model training...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='1192' max='1192' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [1192/1192 10:53, Epoch 8/8]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Epoch</th>\n",
+              "      <th>Training Loss</th>\n",
+              "      <th>Validation Loss</th>\n",
+              "      <th>Precision</th>\n",
+              "      <th>Recall</th>\n",
+              "      <th>F1</th>\n",
+              "      <th>Accuracy</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>1</td>\n",
+              "      <td>0.064600</td>\n",
+              "      <td>0.058615</td>\n",
+              "      <td>0.956479</td>\n",
+              "      <td>0.968291</td>\n",
+              "      <td>0.962349</td>\n",
+              "      <td>0.956479</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>2</td>\n",
+              "      <td>0.029700</td>\n",
+              "      <td>0.015070</td>\n",
+              "      <td>0.993336</td>\n",
+              "      <td>0.994993</td>\n",
+              "      <td>0.994164</td>\n",
+              "      <td>0.993336</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>3</td>\n",
+              "      <td>0.016100</td>\n",
+              "      <td>0.010866</td>\n",
+              "      <td>0.994337</td>\n",
+              "      <td>0.996328</td>\n",
+              "      <td>0.995332</td>\n",
+              "      <td>0.994337</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>4</td>\n",
+              "      <td>0.009300</td>\n",
+              "      <td>0.009888</td>\n",
+              "      <td>0.995336</td>\n",
+              "      <td>0.997330</td>\n",
+              "      <td>0.996332</td>\n",
+              "      <td>0.995336</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>5</td>\n",
+              "      <td>0.005500</td>\n",
+              "      <td>0.009936</td>\n",
+              "      <td>0.995670</td>\n",
+              "      <td>0.997664</td>\n",
+              "      <td>0.996666</td>\n",
+              "      <td>0.995670</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>6</td>\n",
+              "      <td>0.006200</td>\n",
+              "      <td>0.008643</td>\n",
+              "      <td>0.996335</td>\n",
+              "      <td>0.997997</td>\n",
+              "      <td>0.997165</td>\n",
+              "      <td>0.996335</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>7</td>\n",
+              "      <td>0.004200</td>\n",
+              "      <td>0.008044</td>\n",
+              "      <td>0.996003</td>\n",
+              "      <td>0.997997</td>\n",
+              "      <td>0.996999</td>\n",
+              "      <td>0.996003</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>8</td>\n",
+              "      <td>0.001400</td>\n",
+              "      <td>0.008021</td>\n",
+              "      <td>0.996336</td>\n",
+              "      <td>0.998331</td>\n",
+              "      <td>0.997332</td>\n",
+              "      <td>0.996336</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table><p>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✓ Training complete! Best model saved to: /content/drive/MyDrive/durable_invoices_dataset_v9_fixed_finalrun/layout-aware-invoice-ner-model-final/best_model\n",
+            "\n",
+            "--- RUNNING A QUICK INFERENCE EXAMPLE ---\n",
+            "Running model prediction...\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "RuntimeError",
+          "evalue": "Expected all tensors to be on the same device, but got index is on cpu, different from other tensors on cuda:0 (when checking argument in method wrapper_CUDA__index_select)",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-4013341825.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Running model prediction...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m     \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1771\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1772\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1773\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1774\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1775\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1782\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1783\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1784\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1785\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1786\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/content/drive/MyDrive/durable_invoices_dataset_v9_fixed_finalrun/layout_roberta.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, bbox, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    171\u001b[0m     ):\n\u001b[1;32m    172\u001b[0m         \u001b[0;31m# Call the forward pass of our custom LayoutRobertaModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m         outputs = self.roberta(\n\u001b[0m\u001b[1;32m    174\u001b[0m             \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m             \u001b[0mbbox\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1771\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1772\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1773\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1774\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1775\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1782\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1783\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1784\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1785\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1786\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/content/drive/MyDrive/durable_invoices_dataset_v9_fixed_finalrun/layout_roberta.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, bbox, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    101\u001b[0m         \u001b[0;31m# Generate embeddings using our custom embedding layer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minputs_embeds\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m             embedding_output = self.embeddings(\n\u001b[0m\u001b[1;32m    104\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m                 \u001b[0mbbox\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1771\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1772\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1773\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1774\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1775\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1782\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1783\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1784\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1785\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1786\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/content/drive/MyDrive/durable_invoices_dataset_v9_fixed_finalrun/layout_roberta.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, bbox, token_type_ids, position_ids)\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;31m# --- Get Embeddings ---\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0;31m# 1. Word embeddings from token IDs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m         \u001b[0mword_embeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_embeddings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m         \u001b[0;31m# 2. 1D Position embeddings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1771\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1772\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1773\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1774\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1775\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1782\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1783\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1784\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1785\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1786\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/sparse.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 192\u001b[0;31m         return F.embedding(\n\u001b[0m\u001b[1;32m    193\u001b[0m             \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36membedding\u001b[0;34m(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)\u001b[0m\n\u001b[1;32m   2544\u001b[0m         \u001b[0;31m# remove once script supports set_grad_enabled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2545\u001b[0m         \u001b[0m_no_grad_embedding_renorm_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_norm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2546\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale_grad_by_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msparse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2547\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mRuntimeError\u001b[0m: Expected all tensors to be on the same device, but got index is on cpu, different from other tensors on cuda:0 (when checking argument in method wrapper_CUDA__index_select)"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ===================================================================\n",
+        "# 5. INFERENCE EXAMPLE\n",
+        "# ===================================================================\n",
+        "print(\"\\n--- RUNNING A QUICK INFERENCE EXAMPLE ---\")\n",
+        "\n",
+        "# Get the device the model is on\n",
+        "device = model.device\n",
+        "\n",
+        "# Get an example from the evaluation dataset\n",
+        "example = eval_dataset[0]\n",
+        "words = tokenizer.convert_ids_to_tokens(example['input_ids'])\n",
+        "bboxes = example['bbox']\n",
+        "\n",
+        "# Manually prepare inputs and move them to the same device as the model\n",
+        "inputs = {\n",
+        "    \"input_ids\": torch.tensor([example['input_ids']]).to(device),\n",
+        "    \"attention_mask\": torch.tensor([example['attention_mask']]).to(device),\n",
+        "    \"bbox\": torch.tensor([example['bbox']]).to(device),\n",
+        "    \"return_dict\": True  # Force return_dict to True\n",
+        "}\n",
+        "\n",
+        "print(\"Running model prediction...\")\n",
+        "model.eval()  # Set model to evaluation mode\n",
+        "with torch.no_grad():\n",
+        "    outputs = model(**inputs)\n",
+        "    # Handle both tuple and dict outputs\n",
+        "    if isinstance(outputs, tuple):\n",
+        "        logits = outputs[0]  # First element is logits\n",
+        "    else:\n",
+        "        logits = outputs.logits\n",
+        "    predictions = torch.argmax(logits, dim=2)\n",
+        "\n",
+        "print(\"\\n--- PARSED ENTITIES (EXAMPLE) ---\")\n",
+        "predicted_labels = predictions[0].cpu().tolist()  # Move predictions back to CPU\n",
+        "true_labels = example['labels']\n",
+        "\n",
+        "for token, pred_id, true_label_id in zip(words, predicted_labels, true_labels):\n",
+        "    if token not in [tokenizer.bos_token, tokenizer.eos_token, tokenizer.pad_token]:\n",
+        "        if true_label_id != -100:  # Skip padding tokens\n",
+        "            predicted_label = id2tag[pred_id]\n",
+        "            true_label = id2tag[true_label_id]\n",
+        "            match = \"✓\" if predicted_label == true_label else \"✗\"\n",
+        "            if predicted_label != 'O' or true_label != 'O':  # Only show non-O tags\n",
+        "                print(f\"{match} Token: {token:15s} | Predicted: {predicted_label:20s} | True: {true_label}\")\n",
+        "\n",
+        "print(\"\\n✓ Inference example complete!\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Vtnhqy0n4ZcJ",
+        "outputId": "089d8d3e-f2e1-4881-d668-9672240f2913"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "--- RUNNING A QUICK INFERENCE EXAMPLE ---\n",
+            "Running model prediction...\n",
+            "\n",
+            "--- PARSED ENTITIES (EXAMPLE) ---\n",
+            "✓ Token: Ġ25             | Predicted: B-INV_NO             | True: B-INV_NO\n",
+            "✓ Token: ĠS              | Predicted: B-SELLER_NAME        | True: B-SELLER_NAME\n",
+            "✓ Token: ĠELECT          | Predicted: I-SELLER_NAME        | True: I-SELLER_NAME\n",
+            "✓ Token: Ġ16             | Predicted: B-DATE               | True: B-DATE\n",
+            "✓ Token: Ġ05             | Predicted: B-SELLER_ADDR        | True: B-SELLER_ADDR\n",
+            "✓ Token: ĠMoh            | Predicted: I-SELLER_ADDR        | True: I-SELLER_ADDR\n",
+            "✓ Token: ĠRoad           | Predicted: I-SELLER_ADDR        | True: I-SELLER_ADDR\n",
+            "✓ Token: ĠHaj            | Predicted: I-SELLER_ADDR        | True: I-SELLER_ADDR\n",
+            "✓ Token: Ġ06             | Predicted: B-SELLER_TAX_ID      | True: B-SELLER_TAX_ID\n",
+            "✓ Token: ĠR              | Predicted: B-CLIENT_NAME        | True: B-CLIENT_NAME\n",
+            "✓ Token: ĠNAD            | Predicted: I-CLIENT_NAME        | True: I-CLIENT_NAME\n",
+            "✓ Token: ĠH              | Predicted: B-CLIENT_ADDR        | True: B-CLIENT_ADDR\n",
+            "✓ Token: Ġ7              | Predicted: I-CLIENT_ADDR        | True: I-CLIENT_ADDR\n",
+            "✓ Token: ĠBo             | Predicted: I-CLIENT_ADDR        | True: I-CLIENT_ADDR\n",
+            "✓ Token: ĠNag            | Predicted: I-CLIENT_ADDR        | True: I-CLIENT_ADDR\n",
+            "✓ Token: ĠBh             | Predicted: I-CLIENT_ADDR        | True: I-CLIENT_ADDR\n",
+            "✓ Token: Ġ6              | Predicted: B-CLIENT_PHONE       | True: B-CLIENT_PHONE\n",
+            "✓ Token: Ġ31             | Predicted: B-CLIENT_TAX_ID      | True: B-CLIENT_TAX_ID\n",
+            "✓ Token: ĠB              | Predicted: B-ITEM_DESC          | True: B-ITEM_DESC\n",
+            "✓ Token: ĠSpeaker        | Predicted: I-ITEM_DESC          | True: I-ITEM_DESC\n",
+            "✓ Token: ĠModel          | Predicted: I-ITEM_DESC          | True: I-ITEM_DESC\n",
+            "✓ Token: ĠB              | Predicted: I-ITEM_DESC          | True: I-ITEM_DESC\n",
+            "✓ Token: Ġ85             | Predicted: B-ITEM_HSN           | True: B-ITEM_HSN\n",
+            "✓ Token: Ġ2              | Predicted: B-ITEM_QTY           | True: B-ITEM_QTY\n",
+            "✓ Token: Ġ50             | Predicted: B-ITEM_PRICE         | True: B-ITEM_PRICE\n",
+            "✓ Token: Ġ100            | Predicted: B-ITEM_AMOUNT        | True: B-ITEM_AMOUNT\n",
+            "✓ Token: ĠTV             | Predicted: B-ITEM_DESC          | True: B-ITEM_DESC\n",
+            "✓ Token: ĠBike           | Predicted: I-ITEM_DESC          | True: I-ITEM_DESC\n",
+            "✓ Token: Ġ2              | Predicted: B-ITEM_QTY           | True: B-ITEM_QTY\n",
+            "✓ Token: ĠModel          | Predicted: I-ITEM_DESC          | True: I-ITEM_DESC\n",
+            "✓ Token: ĠTV             | Predicted: I-ITEM_DESC          | True: I-ITEM_DESC\n",
+            "✓ Token: Ġ87             | Predicted: B-ITEM_HSN           | True: B-ITEM_HSN\n",
+            "✓ Token: Ġ1              | Predicted: B-ITEM_QTY           | True: B-ITEM_QTY\n",
+            "✓ Token: Ġ66             | Predicted: B-ITEM_PRICE         | True: B-ITEM_PRICE\n",
+            "✓ Token: Ġ66             | Predicted: B-ITEM_PRICE         | True: B-ITEM_PRICE\n",
+            "✓ Token: ĠNikon          | Predicted: B-ITEM_DESC          | True: B-ITEM_DESC\n",
+            "✓ Token: ĠCamera         | Predicted: I-ITEM_DESC          | True: I-ITEM_DESC\n",
+            "✓ Token: ĠModel          | Predicted: I-ITEM_DESC          | True: I-ITEM_DESC\n",
+            "✓ Token: ĠN              | Predicted: I-ITEM_DESC          | True: I-ITEM_DESC\n",
+            "✓ Token: Ġ85             | Predicted: B-ITEM_HSN           | True: B-ITEM_HSN\n",
+            "✓ Token: Ġ1              | Predicted: B-ITEM_QTY           | True: B-ITEM_QTY\n",
+            "✓ Token: Ġ60             | Predicted: B-ITEM_PRICE         | True: B-ITEM_PRICE\n",
+            "✓ Token: Ġ60             | Predicted: B-ITEM_PRICE         | True: B-ITEM_PRICE\n",
+            "✓ Token: Ġ227            | Predicted: B-SUBTOTAL           | True: B-SUBTOTAL\n",
+            "✓ Token: Ġ13             | Predicted: B-CGST               | True: B-CGST\n",
+            "✓ Token: Ġ13             | Predicted: B-CGST               | True: B-CGST\n",
+            "✓ Token: Ġ255            | Predicted: B-TOTAL              | True: B-TOTAL\n",
+            "\n",
+            "✓ Inference example complete!\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install paddlepaddle-gpu==2.6.2 paddleocr==2.10.0"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8neo7QNT-Su1",
+        "outputId": "1c7b0cf5-48fe-4986-b4af-deff9d0b01bf"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting paddlepaddle-gpu==2.6.2\n",
+            "  Downloading paddlepaddle_gpu-2.6.2-cp312-cp312-manylinux1_x86_64.whl.metadata (8.6 kB)\n",
+            "Collecting paddleocr==2.10.0\n",
+            "  Downloading paddleocr-2.10.0-py3-none-any.whl.metadata (12 kB)\n",
+            "Requirement already satisfied: httpx in /usr/local/lib/python3.12/dist-packages (from paddlepaddle-gpu==2.6.2) (0.28.1)\n",
+            "Requirement already satisfied: numpy>=1.13 in /usr/local/lib/python3.12/dist-packages (from paddlepaddle-gpu==2.6.2) (2.0.2)\n",
+            "Requirement already satisfied: Pillow in /usr/local/lib/python3.12/dist-packages (from paddlepaddle-gpu==2.6.2) (11.3.0)\n",
+            "Requirement already satisfied: decorator in /usr/local/lib/python3.12/dist-packages (from paddlepaddle-gpu==2.6.2) (4.4.2)\n",
+            "Collecting astor (from paddlepaddle-gpu==2.6.2)\n",
+            "  Downloading astor-0.8.1-py2.py3-none-any.whl.metadata (4.2 kB)\n",
+            "Collecting opt-einsum==3.3.0 (from paddlepaddle-gpu==2.6.2)\n",
+            "  Downloading opt_einsum-3.3.0-py3-none-any.whl.metadata (6.5 kB)\n",
+            "Requirement already satisfied: protobuf>=3.20.2 in /usr/local/lib/python3.12/dist-packages (from paddlepaddle-gpu==2.6.2) (5.29.5)\n",
+            "Requirement already satisfied: shapely in /usr/local/lib/python3.12/dist-packages (from paddleocr==2.10.0) (2.1.2)\n",
+            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.12/dist-packages (from paddleocr==2.10.0) (0.25.2)\n",
+            "Collecting pyclipper (from paddleocr==2.10.0)\n",
+            "  Downloading pyclipper-1.3.0.post6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.0 kB)\n",
+            "Collecting lmdb (from paddleocr==2.10.0)\n",
+            "  Downloading lmdb-1.7.5-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (1.4 kB)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from paddleocr==2.10.0) (4.67.1)\n",
+            "Collecting rapidfuzz (from paddleocr==2.10.0)\n",
+            "  Downloading rapidfuzz-3.14.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (12 kB)\n",
+            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.12/dist-packages (from paddleocr==2.10.0) (4.12.0.88)\n",
+            "Requirement already satisfied: opencv-contrib-python in /usr/local/lib/python3.12/dist-packages (from paddleocr==2.10.0) (4.12.0.88)\n",
+            "Requirement already satisfied: cython in /usr/local/lib/python3.12/dist-packages (from paddleocr==2.10.0) (3.0.12)\n",
+            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.12/dist-packages (from paddleocr==2.10.0) (6.0.3)\n",
+            "Collecting python-docx (from paddleocr==2.10.0)\n",
+            "  Downloading python_docx-1.2.0-py3-none-any.whl.metadata (2.0 kB)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.12/dist-packages (from paddleocr==2.10.0) (4.13.5)\n",
+            "Requirement already satisfied: fonttools>=4.24.0 in /usr/local/lib/python3.12/dist-packages (from paddleocr==2.10.0) (4.60.1)\n",
+            "Collecting fire>=0.3.0 (from paddleocr==2.10.0)\n",
+            "  Downloading fire-0.7.1-py3-none-any.whl.metadata (5.8 kB)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from paddleocr==2.10.0) (2.32.4)\n",
+            "Requirement already satisfied: albumentations in /usr/local/lib/python3.12/dist-packages (from paddleocr==2.10.0) (2.0.8)\n",
+            "Requirement already satisfied: albucore in /usr/local/lib/python3.12/dist-packages (from paddleocr==2.10.0) (0.0.24)\n",
+            "Requirement already satisfied: termcolor in /usr/local/lib/python3.12/dist-packages (from fire>=0.3.0->paddleocr==2.10.0) (3.2.0)\n",
+            "Requirement already satisfied: stringzilla>=3.10.4 in /usr/local/lib/python3.12/dist-packages (from albucore->paddleocr==2.10.0) (4.2.3)\n",
+            "Requirement already satisfied: simsimd>=5.9.2 in /usr/local/lib/python3.12/dist-packages (from albucore->paddleocr==2.10.0) (6.5.3)\n",
+            "Requirement already satisfied: opencv-python-headless>=4.9.0.80 in /usr/local/lib/python3.12/dist-packages (from albucore->paddleocr==2.10.0) (4.12.0.88)\n",
+            "Requirement already satisfied: scipy>=1.10.0 in /usr/local/lib/python3.12/dist-packages (from albumentations->paddleocr==2.10.0) (1.16.3)\n",
+            "Requirement already satisfied: pydantic>=2.9.2 in /usr/local/lib/python3.12/dist-packages (from albumentations->paddleocr==2.10.0) (2.11.10)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4->paddleocr==2.10.0) (2.8)\n",
+            "Requirement already satisfied: typing-extensions>=4.0.0 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4->paddleocr==2.10.0) (4.15.0)\n",
+            "Requirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx->paddlepaddle-gpu==2.6.2) (4.11.0)\n",
+            "Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx->paddlepaddle-gpu==2.6.2) (2025.10.5)\n",
+            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx->paddlepaddle-gpu==2.6.2) (1.0.9)\n",
+            "Requirement already satisfied: idna in /usr/local/lib/python3.12/dist-packages (from httpx->paddlepaddle-gpu==2.6.2) (3.11)\n",
+            "Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx->paddlepaddle-gpu==2.6.2) (0.16.0)\n",
+            "Requirement already satisfied: lxml>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from python-docx->paddleocr==2.10.0) (5.4.0)\n",
+            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->paddleocr==2.10.0) (3.4.4)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->paddleocr==2.10.0) (2.5.0)\n",
+            "Requirement already satisfied: networkx>=3.0 in /usr/local/lib/python3.12/dist-packages (from scikit-image->paddleocr==2.10.0) (3.5)\n",
+            "Requirement already satisfied: imageio!=2.35.0,>=2.33 in /usr/local/lib/python3.12/dist-packages (from scikit-image->paddleocr==2.10.0) (2.37.0)\n",
+            "Requirement already satisfied: tifffile>=2022.8.12 in /usr/local/lib/python3.12/dist-packages (from scikit-image->paddleocr==2.10.0) (2025.10.16)\n",
+            "Requirement already satisfied: packaging>=21 in /usr/local/lib/python3.12/dist-packages (from scikit-image->paddleocr==2.10.0) (25.0)\n",
+            "Requirement already satisfied: lazy-loader>=0.4 in /usr/local/lib/python3.12/dist-packages (from scikit-image->paddleocr==2.10.0) (0.4)\n",
+            "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.12/dist-packages (from pydantic>=2.9.2->albumentations->paddleocr==2.10.0) (0.7.0)\n",
+            "Requirement already satisfied: pydantic-core==2.33.2 in /usr/local/lib/python3.12/dist-packages (from pydantic>=2.9.2->albumentations->paddleocr==2.10.0) (2.33.2)\n",
+            "Requirement already satisfied: typing-inspection>=0.4.0 in /usr/local/lib/python3.12/dist-packages (from pydantic>=2.9.2->albumentations->paddleocr==2.10.0) (0.4.2)\n",
+            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.12/dist-packages (from anyio->httpx->paddlepaddle-gpu==2.6.2) (1.3.1)\n",
+            "Downloading paddlepaddle_gpu-2.6.2-cp312-cp312-manylinux1_x86_64.whl (758.9 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m758.9/758.9 MB\u001b[0m \u001b[31m603.1 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading paddleocr-2.10.0-py3-none-any.whl (2.4 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m92.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading opt_einsum-3.3.0-py3-none-any.whl (65 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.5/65.5 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading fire-0.7.1-py3-none-any.whl (115 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.9/115.9 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading astor-0.8.1-py2.py3-none-any.whl (27 kB)\n",
+            "Downloading lmdb-1.7.5-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (299 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m299.4/299.4 kB\u001b[0m \u001b[31m28.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading pyclipper-1.3.0.post6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (963 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m963.8/963.8 kB\u001b[0m \u001b[31m68.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading python_docx-1.2.0-py3-none-any.whl (252 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m253.0/253.0 kB\u001b[0m \u001b[31m28.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading rapidfuzz-3.14.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (3.2 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m101.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: pyclipper, lmdb, rapidfuzz, python-docx, opt-einsum, fire, astor, paddlepaddle-gpu, paddleocr\n",
+            "  Attempting uninstall: opt-einsum\n",
+            "    Found existing installation: opt_einsum 3.4.0\n",
+            "    Uninstalling opt_einsum-3.4.0:\n",
+            "      Successfully uninstalled opt_einsum-3.4.0\n",
+            "Successfully installed astor-0.8.1 fire-0.7.1 lmdb-1.7.5 opt-einsum-3.3.0 paddleocr-2.10.0 paddlepaddle-gpu-2.6.2 pyclipper-1.3.0.post6 python-docx-1.2.0 rapidfuzz-3.14.3\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import json\n",
+        "import re\n",
+        "import torch\n",
+        "import numpy as np\n",
+        "from PIL import Image\n",
+        "from transformers import AutoTokenizer\n",
+        "from paddleocr import PaddleOCR\n",
+        "from collections import defaultdict\n",
+        "import cv2\n",
+        "\n",
+        "from layout_roberta import LayoutRobertaForTokenClassification, RobertaConfig\n",
+        "\n",
+        "\n",
+        "class InferenceConfig:\n",
+        "    MODEL_PATH = r\"/content/drive/MyDrive/durable_invoices_dataset_v9_fixed_finalrun/layout-aware-invoice-ner-model-final/best_model\"\n",
+        "\n",
+        "    # Path to the new invoice image you want to process\n",
+        "    IMAGE_PATH = r\"/content/original-scanned.jpg\"    # Confidence threshold for NER predictions\n",
+        "    SCORE_THRESHOLD = 0.85\n",
+        "\n",
+        "# ===================================================================\n",
+        "# 2. HELPER FUNCTIONS (OCR & Post-Processing)\n",
+        "# ===================================================================\n",
+        "\n",
+        "def run_ocr_on_image(image_path: str, ocr_engine: PaddleOCR) -> (list, list, tuple):\n",
+        "    \"\"\"\n",
+        "    Performs OCR on an image and returns words, normalized bboxes, and image dimensions.\n",
+        "    \"\"\"\n",
+        "    if not os.path.exists(image_path):\n",
+        "        raise FileNotFoundError(f\"Image not found at path: {image_path}\")\n",
+        "\n",
+        "    words = []\n",
+        "    bboxes = []\n",
+        "    try:\n",
+        "        img = cv2.imread(image_path)\n",
+        "        if img is None:\n",
+        "            raise IOError(\"Could not read image with OpenCV.\")\n",
+        "        img_height, img_width = img.shape[:2]\n",
+        "\n",
+        "        result = ocr_engine.ocr(img, cls=True)\n",
+        "        if not result or not result[0]:\n",
+        "            print(\"Warning: OCR did not detect any text.\")\n",
+        "            return [], [], (img_width, img_height)\n",
+        "\n",
+        "        for line in result[0]:\n",
+        "            bbox_points, (text, confidence) = line\n",
+        "            if confidence < 0.5:\n",
+        "                continue\n",
+        "\n",
+        "            text_words = text.split()\n",
+        "            if not text_words:\n",
+        "                continue\n",
+        "\n",
+        "            xs = [p[0] for p in bbox_points]\n",
+        "            ys = [p[1] for p in bbox_points]\n",
+        "            x0, x1 = min(xs), max(xs)\n",
+        "            y0, y1 = min(ys), max(ys)\n",
+        "\n",
+        "            line_width = x1 - x0\n",
+        "            if len(text_words) > 1 and line_width > 0:\n",
+        "                word_width_avg = line_width / len(text_words)\n",
+        "                for idx, word in enumerate(text_words):\n",
+        "                    word_x0 = x0 + (idx * word_width_avg)\n",
+        "                    word_x1 = word_x0 + word_width_avg\n",
+        "                    norm_bbox = [\n",
+        "                        int((word_x0 / img_width) * 1000),\n",
+        "                        int((y0 / img_height) * 1000),\n",
+        "                        int((word_x1 / img_width) * 1000),\n",
+        "                        int((y1 / img_height) * 1000)\n",
+        "                    ]\n",
+        "                    words.append(word)\n",
+        "                    bboxes.append(norm_bbox)\n",
+        "            else:\n",
+        "                norm_bbox = [\n",
+        "                    int((x0 / img_width) * 1000),\n",
+        "                    int((y0 / img_height) * 1000),\n",
+        "                    int((x1 / img_width) * 1000),\n",
+        "                    int((y1 / img_height) * 1000)\n",
+        "                ]\n",
+        "                words.append(text)\n",
+        "                bboxes.append(norm_bbox)\n",
+        "\n",
+        "    except Exception as e:\n",
+        "        print(f\"An error occurred during OCR processing: {e}\")\n",
+        "        return [], [], (0, 0)\n",
+        "\n",
+        "    return words, bboxes, (img_width, img_height)\n",
+        "\n",
+        "\n",
+        "def group_and_clean_entities(ner_results: list, score_threshold: float = 0.85) -> dict:\n",
+        "    \"\"\"\n",
+        "    The definitive post-processing function.\n",
+        "    Cleans, groups, and structures raw NER predictions.\n",
+        "    \"\"\"\n",
+        "    # Step 1: Group B- and I- tags\n",
+        "    grouped_by_tag = []\n",
+        "    current_entity = None\n",
+        "\n",
+        "    for res in ner_results:\n",
+        "        if res['score'] < score_threshold:\n",
+        "            continue\n",
+        "\n",
+        "        tag = res['entity']\n",
+        "        word = res['word'].replace('Ġ', ' ').strip()\n",
+        "\n",
+        "        if tag.startswith(\"B-\"):\n",
+        "            if current_entity:\n",
+        "                grouped_by_tag.append(current_entity)\n",
+        "            current_entity = {\"entity_group\": tag[2:], \"value\": word}\n",
+        "\n",
+        "        elif tag.startswith(\"I-\") and current_entity and tag[2:] == current_entity[\"entity_group\"]:\n",
+        "            if word.startswith(' ') or res['word'].startswith('Ġ'):\n",
+        "                current_entity[\"value\"] += \" \" + word\n",
+        "            else:\n",
+        "                current_entity[\"value\"] += word\n",
+        "        else:\n",
+        "            if current_entity:\n",
+        "                grouped_by_tag.append(current_entity)\n",
+        "            current_entity = None\n",
+        "\n",
+        "    if current_entity:\n",
+        "        grouped_by_tag.append(current_entity)\n",
+        "\n",
+        "    # Step 2: Aggregate fragments and build the final JSON\n",
+        "    aggregated_entities = defaultdict(list)\n",
+        "\n",
+        "    for entity in grouped_by_tag:\n",
+        "        key = entity['entity_group'].lower()\n",
+        "        value = entity['value'].strip().lstrip(':').strip()\n",
+        "\n",
+        "        junk_words = [\n",
+        "            \"invoice\", \"no\", \"date\", \"bill\", \"to\", \"ship\", \"ph\", \"gstin\",\n",
+        "            \"description\", \"hsn\", \"qty\", \"rate\", \"amount\", \"total\",\n",
+        "            \"subtotal\", \"#\", \"@\", \"%\", \":\", \"model\"\n",
+        "        ]\n",
+        "\n",
+        "        if value.lower().strip(': ') in junk_words:\n",
+        "            continue\n",
+        "\n",
+        "        aggregated_entities[key].append(value)\n",
+        "\n",
+        "    final_json = {}\n",
+        "    item_lists = {k: v for k, v in aggregated_entities.items() if k.startswith(\"item_\")}\n",
+        "\n",
+        "    # Add non-item fields\n",
+        "    for key, values in aggregated_entities.items():\n",
+        "        if not key.startswith(\"item_\"):\n",
+        "            final_json[key] = \" \".join(values)\n",
+        "\n",
+        "    # Reconstruct line items\n",
+        "    num_items = 0\n",
+        "    if item_lists:\n",
+        "        try:\n",
+        "            num_items = max(len(v) for v in item_lists.values())\n",
+        "        except ValueError:\n",
+        "            num_items = 0\n",
+        "\n",
+        "    items = []\n",
+        "    for i in range(num_items):\n",
+        "        item = {}\n",
+        "        for key, values in item_lists.items():\n",
+        "            if i < len(values):\n",
+        "                item_key = key.replace(\"item_\", \"\")\n",
+        "                item[item_key] = values[i]\n",
+        "        if item:\n",
+        "            items.append(item)\n",
+        "\n",
+        "    if items:\n",
+        "        final_json['items'] = items\n",
+        "\n",
+        "    return final_json\n",
+        "\n",
+        "\n",
+        "# ===================================================================\n",
+        "# 3. MAIN INFERENCE PIPELINE\n",
+        "# ===================================================================\n",
+        "def main():\n",
+        "    config = InferenceConfig()\n",
+        "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "\n",
+        "    # --- Step 1: Initialize OCR and Model ---\n",
+        "    print(\"Initializing PaddleOCR engine...\")\n",
+        "    ocr_engine = PaddleOCR(use_angle_cls=True, lang='en')\n",
+        "\n",
+        "    print(\"Loading fine-tuned Layout-Aware NER model...\")\n",
+        "    tokenizer = AutoTokenizer.from_pretrained(config.MODEL_PATH)\n",
+        "    model = LayoutRobertaForTokenClassification.from_pretrained(config.MODEL_PATH)\n",
+        "    model.to(device)\n",
+        "    model.eval()\n",
+        "\n",
+        "    # --- Step 2: Run OCR on the new image ---\n",
+        "    print(\"\\n--- RUNNING OCR ---\")\n",
+        "    words, bboxes, (img_w, img_h) = run_ocr_on_image(config.IMAGE_PATH, ocr_engine)\n",
+        "\n",
+        "    if not words:\n",
+        "        print(\"Inference aborted as no text was found in the image.\")\n",
+        "        return\n",
+        "\n",
+        "    print(f\"OCR detected {len(words)} words\")\n",
+        "\n",
+        "    # --- Step 3: Preprocess data for the model ---\n",
+        "    tokenized_inputs = tokenizer(\n",
+        "        words,\n",
+        "        is_split_into_words=True,\n",
+        "        return_tensors=\"pt\",\n",
+        "        padding=\"max_length\",\n",
+        "        truncation=True,\n",
+        "        max_length=512\n",
+        "    )\n",
+        "\n",
+        "    aligned_bboxes = []\n",
+        "    word_ids = tokenized_inputs.word_ids()\n",
+        "    previous_word_idx = None\n",
+        "\n",
+        "    for word_idx in word_ids:\n",
+        "        if word_idx is None:\n",
+        "            aligned_bboxes.append([0, 0, 0, 0])\n",
+        "        elif word_idx != previous_word_idx:\n",
+        "            aligned_bboxes.append(bboxes[word_idx])\n",
+        "        else:\n",
+        "            aligned_bboxes.append(bboxes[word_idx])\n",
+        "        previous_word_idx = word_idx\n",
+        "\n",
+        "    # --- Step 4: Run model prediction ---\n",
+        "    print(\"\\n--- RUNNING MODEL PREDICTION ---\")\n",
+        "    inputs = {\n",
+        "        \"input_ids\": tokenized_inputs[\"input_ids\"].to(device),\n",
+        "        \"attention_mask\": tokenized_inputs[\"attention_mask\"].to(device),\n",
+        "        \"bbox\": torch.tensor([aligned_bboxes]).to(device),\n",
+        "        \"return_dict\": True  # Force return_dict\n",
+        "    }\n",
+        "\n",
+        "    with torch.no_grad():\n",
+        "        outputs = model(**inputs)\n",
+        "        # Handle both tuple and dict outputs\n",
+        "        if isinstance(outputs, tuple):\n",
+        "            logits = outputs[0]\n",
+        "        else:\n",
+        "            logits = outputs.logits\n",
+        "        predictions = torch.argmax(logits, dim=2)\n",
+        "\n",
+        "    # --- Step 5: Decode and Post-process the results ---\n",
+        "    print(\"\\n--- PARSING ENTITIES ---\")\n",
+        "    preds = predictions[0].cpu().tolist()\n",
+        "    tokens = tokenizer.convert_ids_to_tokens(tokenized_inputs[\"input_ids\"][0])\n",
+        "\n",
+        "    raw_results = []\n",
+        "    for token, pred_id in zip(tokens, preds):\n",
+        "        label = model.config.id2label[pred_id]\n",
+        "        if label != \"O\":\n",
+        "            raw_results.append({\n",
+        "                \"entity\": label,\n",
+        "                \"word\": token,\n",
+        "                \"score\": 1.0  # Confidence score (could extract from logits if needed)\n",
+        "            })\n",
+        "\n",
+        "    print(f\"Found {len(raw_results)} entity predictions\")\n",
+        "\n",
+        "    # Post-process and structure the results\n",
+        "    final_json = group_and_clean_entities(raw_results, score_threshold=config.SCORE_THRESHOLD)\n",
+        "\n",
+        "    print(\"\\n\" + \"=\"*60)\n",
+        "    print(\"--- FINAL EXTRACTED INVOICE DATA ---\")\n",
+        "    print(\"=\"*60)\n",
+        "    print(json.dumps(final_json, indent=2))\n",
+        "    print(\"=\"*60)\n",
+        "    print(\"\\n✓ Inference complete!\")\n",
+        "\n",
+        "if __name__ == '__main__':\n",
+        "    main()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VuRchHy9DGcS",
+        "outputId": "8a250e8f-21be-4724-97d9-8fc299b9147b"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initializing PaddleOCR engine...\n",
+            "[2025/11/04 07:02:33] ppocr WARNING: The first GPU is used for inference by default, GPU ID: 0\n",
+            "[2025/11/04 07:02:37] ppocr WARNING: The first GPU is used for inference by default, GPU ID: 0\n",
+            "[2025/11/04 07:02:41] ppocr WARNING: The first GPU is used for inference by default, GPU ID: 0\n",
+            "Loading fine-tuned Layout-Aware NER model...\n",
+            "\n",
+            "--- RUNNING OCR ---\n",
+            "OCR detected 69 words\n",
+            "\n",
+            "--- RUNNING MODEL PREDICTION ---\n",
+            "\n",
+            "--- PARSING ENTITIES ---\n",
+            "Found 108 entity predictions\n",
+            "\n",
+            "============================================================\n",
+            "--- FINAL EXTRACTED INVOICE DATA ---\n",
+            "============================================================\n",
+            "{\n",
+            "  \"seller_name\": \"NEW FREEZE LAND Club AHMED <pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>\",\n",
+            "  \"client_name\": \"MANISHIKA\",\n",
+            "  \"inv_no\": \"18 / 0000 15 24 / 76\",\n",
+            "  \"client_phone\": \" 76\",\n",
+            "  \"date\": \"25 / 8 / 17\",\n",
+            "  \"subtotal\": \"257\",\n",
+            "  \"total\": \"303\",\n",
+            "  \"client_tax_id\": \"GST IN - 24 ABC FM 73 13 D 1 Z Q\",\n",
+            "  \"items\": [\n",
+            "    {\n",
+            "      \"hsn\": \"15\",\n",
+            "      \"qty\": \"1\",\n",
+            "      \"price\": \"151\",\n",
+            "      \"desc\": \"Tortas\"\n",
+            "    },\n",
+            "    {\n",
+            "      \"qty\": \"00\",\n",
+            "      \"price\": \"151\",\n",
+            "      \"desc\": \"Tortas\"\n",
+            "    },\n",
+            "    {\n",
+            "      \"qty\": \"00\",\n",
+            "      \"price\": \"53\"\n",
+            "    },\n",
+            "    {\n",
+            "      \"qty\": \"1\",\n",
+            "      \"price\": \"53\"\n",
+            "    },\n",
+            "    {\n",
+            "      \"qty\": \"00\",\n",
+            "      \"price\": \"53\"\n",
+            "    },\n",
+            "    {\n",
+            "      \"qty\": \"00\",\n",
+            "      \"price\": \"53\"\n",
+            "    },\n",
+            "    {\n",
+            "      \"qty\": \"1\"\n",
+            "    },\n",
+            "    {\n",
+            "      \"qty\": \"00\"\n",
+            "    },\n",
+            "    {\n",
+            "      \"qty\": \"00\"\n",
+            "    }\n",
+            "  ]\n",
+            "}\n",
+            "============================================================\n",
+            "\n",
+            "✓ Inference complete!\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "o3oX3W08D58s"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}