Training in progress, epoch 1

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+train_dataset.json filter=lfs diff=lfs merge=lfs -text

adapter_config.json

@@ -0,0 +1,34 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "/kaggle/input/llama-3.2/transformers/1b-instruct/1",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "k_proj",
+    "gate_proj",
+    "down_proj",
+    "up_proj",
+    "v_proj",
+    "o_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+oid sha256:b1483fd4cd90fdd9734781e0662b5fdb75ea4f613814735e9c09fea7a48b9120
+size 22573704

runs/Oct16_10-39-48_9b7ebc7a5355/events.out.tfevents.1729075210.9b7ebc7a5355.25.0

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+size 5648

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|eot_id|>"
+}

test_dataset.json

@@ -0,0 +1,10 @@
+{"type":"DEBIT","amount":2970.0,"narration":"UPI-BILLDESKTEZ-BILLDESK.WATER@ICICI-ICIC0DC0099-329238850268-UPI (Ref# 0000329238850268)","_x_category":"Utility Payments","_x_sub_category":"Utility Payments","category_level_1":"Utility Payments","category_level_2":"Utility Payments","messages":[{"content":"\nYou are a skilled financial data extractor specializing in Indian bank transactions. Focus on accurately identifying the receiver's name from transaction narrations, even if the format varies.\n","role":"system"},{"content":"\n    # CONTEXT #\n    You are a financial data analyst working with Indian banking transactions, dealing with formatting variations. Ensure accuracy in extracting and classifying information.\n\n    # OBJECTIVE #\n    1. **Classify category_level_2**:\n        - Pick a sub-category from the predefined list.\n    # RESPONSE FORMAT #\n    Provide the response in DICT format without any extra explaination or extra remark:\n    {\n        \"category_level_2\": \"Classify the sub-category of the transaction from the predefined list,it must be from list only.['Bank Service Charges', 'Penalties', 'Bank Closure', 'Bounced I\/W Cheque', 'Bounced O\/W Cheque', 'Cash Deposit', 'Cash Withdrawal', 'Credit Card Settlement', 'Dividend', 'Education', 'Movie', 'Gaming', 'Subscription', 'Travel', 'Alcohol', 'Food', 'Drinks', 'Medical', 'Personal Care', 'Hospital', 'Bank Interest', 'Bonds Credit', 'Bonds Debit', 'Fixed Deposit Credit', 'Fixed Deposit Debit', 'Crypto Credit', 'Crypto Debit', 'Equities Credit', 'Equities Debit', 'Insurance Credit', 'Insurance Debit', 'Mutual Funds Credit', 'Mutual Funds Debit', 'Fixed Deposit Sweep Out', 'Fixed Deposit Sweep In', 'Investment Interest', 'P2P Lending Credit', 'P2P Lending Debit', 'NPS Credit', 'NPS Debit', 'Gold Credit', 'Gold Debit', 'Loan Credit', 'Loan EMI Paid', 'Pension', 'Provident Fund Credit', 'Provident Fund Debit', 'Reimbursement Credit', 'Salary Received', 'Salary Paid', 'Bonus', 'Business Income', 'Business Expense', 'Apparel', 'Electronics', 'Stationery', 'Sports', 'Tax Rebate', 'Tax Liability', 'Hotels and Stays', 'Tolls', 'Cab', 'Bus', 'Train', 'Flight', 'Courier', 'Fuel', 'Rental', 'Purchase', 'Electricity', 'Phone Recharge', 'Gas', 'Water', 'Internet and DTH', 'Groceries', 'Household', 'Rent Received', 'Rent Paid', 'Vehicle Maintenance Expense', 'Refunds', 'Cleaning Services', 'Counseling', 'Charity Donation', 'Religious Donation', 'Political Donation', 'Fines', 'P2P Inward', 'P2P Outward', 'Utility Payments', 'Subsidy', 'Shopping', 'Jewellery'].\"\n    }\n\n    # SAMPLE NARRATION #\n    Narration: \"UPI-BILLDESKTEZ-BILLDESK.WATER@ICICI-ICIC0DC0099-329238850268-UPI (Ref# 0000329238850268)\"\n    ","role":"user"},{"content":"Utility Payments","role":"assistant"}]}
+{"type":"DEBIT","amount":180.0,"narration":"UPI\/P2M\/331840326171\/ZAM ZAM FAMILY HOTEL \/UPI\/Yes Bank Ltd","_x_category":"Food & Drinks","_x_sub_category":"Food","category_level_1":"Food & Drinks","category_level_2":"Food","messages":[{"content":"\nYou are a skilled financial data extractor specializing in Indian bank transactions. Focus on accurately identifying the receiver's name from transaction narrations, even if the format varies.\n","role":"system"},{"content":"\n    # CONTEXT #\n    You are a financial data analyst working with Indian banking transactions, dealing with formatting variations. Ensure accuracy in extracting and classifying information.\n\n    # OBJECTIVE #\n    1. **Classify category_level_2**:\n        - Pick a sub-category from the predefined list.\n    # RESPONSE FORMAT #\n    Provide the response in DICT format without any extra explaination or extra remark:\n    {\n        \"category_level_2\": \"Classify the sub-category of the transaction from the predefined list,it must be from list only.['Bank Service Charges', 'Penalties', 'Bank Closure', 'Bounced I\/W Cheque', 'Bounced O\/W Cheque', 'Cash Deposit', 'Cash Withdrawal', 'Credit Card Settlement', 'Dividend', 'Education', 'Movie', 'Gaming', 'Subscription', 'Travel', 'Alcohol', 'Food', 'Drinks', 'Medical', 'Personal Care', 'Hospital', 'Bank Interest', 'Bonds Credit', 'Bonds Debit', 'Fixed Deposit Credit', 'Fixed Deposit Debit', 'Crypto Credit', 'Crypto Debit', 'Equities Credit', 'Equities Debit', 'Insurance Credit', 'Insurance Debit', 'Mutual Funds Credit', 'Mutual Funds Debit', 'Fixed Deposit Sweep Out', 'Fixed Deposit Sweep In', 'Investment Interest', 'P2P Lending Credit', 'P2P Lending Debit', 'NPS Credit', 'NPS Debit', 'Gold Credit', 'Gold Debit', 'Loan Credit', 'Loan EMI Paid', 'Pension', 'Provident Fund Credit', 'Provident Fund Debit', 'Reimbursement Credit', 'Salary Received', 'Salary Paid', 'Bonus', 'Business Income', 'Business Expense', 'Apparel', 'Electronics', 'Stationery', 'Sports', 'Tax Rebate', 'Tax Liability', 'Hotels and Stays', 'Tolls', 'Cab', 'Bus', 'Train', 'Flight', 'Courier', 'Fuel', 'Rental', 'Purchase', 'Electricity', 'Phone Recharge', 'Gas', 'Water', 'Internet and DTH', 'Groceries', 'Household', 'Rent Received', 'Rent Paid', 'Vehicle Maintenance Expense', 'Refunds', 'Cleaning Services', 'Counseling', 'Charity Donation', 'Religious Donation', 'Political Donation', 'Fines', 'P2P Inward', 'P2P Outward', 'Utility Payments', 'Subsidy', 'Shopping', 'Jewellery'].\"\n    }\n\n    # SAMPLE NARRATION #\n    Narration: \"UPI\/P2M\/331840326171\/ZAM ZAM FAMILY HOTEL \/UPI\/Yes Bank Ltd\"\n    ","role":"user"},{"content":"Food","role":"assistant"}]}
+{"type":"DEBIT","amount":10000.0,"narration":"ATW using437313XXXXXX3003-Reference 332119011346-NORTH 24 PAR-07:44:25 PM","_x_category":"Cash Withdrawal","_x_sub_category":"Cash Withdrawal","category_level_1":"Transfer","category_level_2":"P2P Outward","messages":[{"content":"\nYou are a skilled financial data extractor specializing in Indian bank transactions. Focus on accurately identifying the receiver's name from transaction narrations, even if the format varies.\n","role":"system"},{"content":"\n    # CONTEXT #\n    You are a financial data analyst working with Indian banking transactions, dealing with formatting variations. Ensure accuracy in extracting and classifying information.\n\n    # OBJECTIVE #\n    1. **Classify category_level_2**:\n        - Pick a sub-category from the predefined list.\n    # RESPONSE FORMAT #\n    Provide the response in DICT format without any extra explaination or extra remark:\n    {\n        \"category_level_2\": \"Classify the sub-category of the transaction from the predefined list,it must be from list only.['Bank Service Charges', 'Penalties', 'Bank Closure', 'Bounced I\/W Cheque', 'Bounced O\/W Cheque', 'Cash Deposit', 'Cash Withdrawal', 'Credit Card Settlement', 'Dividend', 'Education', 'Movie', 'Gaming', 'Subscription', 'Travel', 'Alcohol', 'Food', 'Drinks', 'Medical', 'Personal Care', 'Hospital', 'Bank Interest', 'Bonds Credit', 'Bonds Debit', 'Fixed Deposit Credit', 'Fixed Deposit Debit', 'Crypto Credit', 'Crypto Debit', 'Equities Credit', 'Equities Debit', 'Insurance Credit', 'Insurance Debit', 'Mutual Funds Credit', 'Mutual Funds Debit', 'Fixed Deposit Sweep Out', 'Fixed Deposit Sweep In', 'Investment Interest', 'P2P Lending Credit', 'P2P Lending Debit', 'NPS Credit', 'NPS Debit', 'Gold Credit', 'Gold Debit', 'Loan Credit', 'Loan EMI Paid', 'Pension', 'Provident Fund Credit', 'Provident Fund Debit', 'Reimbursement Credit', 'Salary Received', 'Salary Paid', 'Bonus', 'Business Income', 'Business Expense', 'Apparel', 'Electronics', 'Stationery', 'Sports', 'Tax Rebate', 'Tax Liability', 'Hotels and Stays', 'Tolls', 'Cab', 'Bus', 'Train', 'Flight', 'Courier', 'Fuel', 'Rental', 'Purchase', 'Electricity', 'Phone Recharge', 'Gas', 'Water', 'Internet and DTH', 'Groceries', 'Household', 'Rent Received', 'Rent Paid', 'Vehicle Maintenance Expense', 'Refunds', 'Cleaning Services', 'Counseling', 'Charity Donation', 'Religious Donation', 'Political Donation', 'Fines', 'P2P Inward', 'P2P Outward', 'Utility Payments', 'Subsidy', 'Shopping', 'Jewellery'].\"\n    }\n\n    # SAMPLE NARRATION #\n    Narration: \"ATW using437313XXXXXX3003-Reference 332119011346-NORTH 24 PAR-07:44:25 PM\"\n    ","role":"user"},{"content":"Cash Withdrawal","role":"assistant"}]}
+{"type":"DEBIT","amount":612.42,"narration":"UPI-BILLDESKTEZ-BILLDESK.POSTPAID-MOBILE@ICICI-ICIC0DC0099-330000142774-UPI (Ref# 0000330000142774)","_x_category":"Utility Payments","_x_sub_category":"Phone Recharge","category_level_1":"Utility Payments","category_level_2":"Utility Payments","messages":[{"content":"\nYou are a skilled financial data extractor specializing in Indian bank transactions. Focus on accurately identifying the receiver's name from transaction narrations, even if the format varies.\n","role":"system"},{"content":"\n    # CONTEXT #\n    You are a financial data analyst working with Indian banking transactions, dealing with formatting variations. Ensure accuracy in extracting and classifying information.\n\n    # OBJECTIVE #\n    1. **Classify category_level_2**:\n        - Pick a sub-category from the predefined list.\n    # RESPONSE FORMAT #\n    Provide the response in DICT format without any extra explaination or extra remark:\n    {\n        \"category_level_2\": \"Classify the sub-category of the transaction from the predefined list,it must be from list only.['Bank Service Charges', 'Penalties', 'Bank Closure', 'Bounced I\/W Cheque', 'Bounced O\/W Cheque', 'Cash Deposit', 'Cash Withdrawal', 'Credit Card Settlement', 'Dividend', 'Education', 'Movie', 'Gaming', 'Subscription', 'Travel', 'Alcohol', 'Food', 'Drinks', 'Medical', 'Personal Care', 'Hospital', 'Bank Interest', 'Bonds Credit', 'Bonds Debit', 'Fixed Deposit Credit', 'Fixed Deposit Debit', 'Crypto Credit', 'Crypto Debit', 'Equities Credit', 'Equities Debit', 'Insurance Credit', 'Insurance Debit', 'Mutual Funds Credit', 'Mutual Funds Debit', 'Fixed Deposit Sweep Out', 'Fixed Deposit Sweep In', 'Investment Interest', 'P2P Lending Credit', 'P2P Lending Debit', 'NPS Credit', 'NPS Debit', 'Gold Credit', 'Gold Debit', 'Loan Credit', 'Loan EMI Paid', 'Pension', 'Provident Fund Credit', 'Provident Fund Debit', 'Reimbursement Credit', 'Salary Received', 'Salary Paid', 'Bonus', 'Business Income', 'Business Expense', 'Apparel', 'Electronics', 'Stationery', 'Sports', 'Tax Rebate', 'Tax Liability', 'Hotels and Stays', 'Tolls', 'Cab', 'Bus', 'Train', 'Flight', 'Courier', 'Fuel', 'Rental', 'Purchase', 'Electricity', 'Phone Recharge', 'Gas', 'Water', 'Internet and DTH', 'Groceries', 'Household', 'Rent Received', 'Rent Paid', 'Vehicle Maintenance Expense', 'Refunds', 'Cleaning Services', 'Counseling', 'Charity Donation', 'Religious Donation', 'Political Donation', 'Fines', 'P2P Inward', 'P2P Outward', 'Utility Payments', 'Subsidy', 'Shopping', 'Jewellery'].\"\n    }\n\n    # SAMPLE NARRATION #\n    Narration: \"UPI-BILLDESKTEZ-BILLDESK.POSTPAID-MOBILE@ICICI-ICIC0DC0099-330000142774-UPI (Ref# 0000330000142774)\"\n    ","role":"user"},{"content":"Phone Recharge","role":"assistant"}]}
+{"type":"DEBIT","amount":100.0,"narration":"UPI-IRCTCUTS-PAYTM-8796946@PAYTM-PYTM0123456-441005209280-OID802130548027005 (Ref# 0000441005209280)","_x_category":"Travel and Transport","_x_sub_category":"Train","category_level_1":"Transfer","category_level_2":"P2P Outward","messages":[{"content":"\nYou are a skilled financial data extractor specializing in Indian bank transactions. Focus on accurately identifying the receiver's name from transaction narrations, even if the format varies.\n","role":"system"},{"content":"\n    # CONTEXT #\n    You are a financial data analyst working with Indian banking transactions, dealing with formatting variations. Ensure accuracy in extracting and classifying information.\n\n    # OBJECTIVE #\n    1. **Classify category_level_2**:\n        - Pick a sub-category from the predefined list.\n    # RESPONSE FORMAT #\n    Provide the response in DICT format without any extra explaination or extra remark:\n    {\n        \"category_level_2\": \"Classify the sub-category of the transaction from the predefined list,it must be from list only.['Bank Service Charges', 'Penalties', 'Bank Closure', 'Bounced I\/W Cheque', 'Bounced O\/W Cheque', 'Cash Deposit', 'Cash Withdrawal', 'Credit Card Settlement', 'Dividend', 'Education', 'Movie', 'Gaming', 'Subscription', 'Travel', 'Alcohol', 'Food', 'Drinks', 'Medical', 'Personal Care', 'Hospital', 'Bank Interest', 'Bonds Credit', 'Bonds Debit', 'Fixed Deposit Credit', 'Fixed Deposit Debit', 'Crypto Credit', 'Crypto Debit', 'Equities Credit', 'Equities Debit', 'Insurance Credit', 'Insurance Debit', 'Mutual Funds Credit', 'Mutual Funds Debit', 'Fixed Deposit Sweep Out', 'Fixed Deposit Sweep In', 'Investment Interest', 'P2P Lending Credit', 'P2P Lending Debit', 'NPS Credit', 'NPS Debit', 'Gold Credit', 'Gold Debit', 'Loan Credit', 'Loan EMI Paid', 'Pension', 'Provident Fund Credit', 'Provident Fund Debit', 'Reimbursement Credit', 'Salary Received', 'Salary Paid', 'Bonus', 'Business Income', 'Business Expense', 'Apparel', 'Electronics', 'Stationery', 'Sports', 'Tax Rebate', 'Tax Liability', 'Hotels and Stays', 'Tolls', 'Cab', 'Bus', 'Train', 'Flight', 'Courier', 'Fuel', 'Rental', 'Purchase', 'Electricity', 'Phone Recharge', 'Gas', 'Water', 'Internet and DTH', 'Groceries', 'Household', 'Rent Received', 'Rent Paid', 'Vehicle Maintenance Expense', 'Refunds', 'Cleaning Services', 'Counseling', 'Charity Donation', 'Religious Donation', 'Political Donation', 'Fines', 'P2P Inward', 'P2P Outward', 'Utility Payments', 'Subsidy', 'Shopping', 'Jewellery'].\"\n    }\n\n    # SAMPLE NARRATION #\n    Narration: \"UPI-IRCTCUTS-PAYTM-8796946@PAYTM-PYTM0123456-441005209280-OID802130548027005 (Ref# 0000441005209280)\"\n    ","role":"user"},{"content":"Train","role":"assistant"}]}
+{"type":"DEBIT","amount":500.0,"narration":"TO ONL UPI\/DR\/335724605364\/BILLDESK\/ICIC\/BILLDESK F\/U::00143","_x_category":"Utility Payments","_x_sub_category":"Utility Payments","category_level_1":"Utility Payments","category_level_2":"Utility Payments","messages":[{"content":"\nYou are a skilled financial data extractor specializing in Indian bank transactions. Focus on accurately identifying the receiver's name from transaction narrations, even if the format varies.\n","role":"system"},{"content":"\n    # CONTEXT #\n    You are a financial data analyst working with Indian banking transactions, dealing with formatting variations. Ensure accuracy in extracting and classifying information.\n\n    # OBJECTIVE #\n    1. **Classify category_level_2**:\n        - Pick a sub-category from the predefined list.\n    # RESPONSE FORMAT #\n    Provide the response in DICT format without any extra explaination or extra remark:\n    {\n        \"category_level_2\": \"Classify the sub-category of the transaction from the predefined list,it must be from list only.['Bank Service Charges', 'Penalties', 'Bank Closure', 'Bounced I\/W Cheque', 'Bounced O\/W Cheque', 'Cash Deposit', 'Cash Withdrawal', 'Credit Card Settlement', 'Dividend', 'Education', 'Movie', 'Gaming', 'Subscription', 'Travel', 'Alcohol', 'Food', 'Drinks', 'Medical', 'Personal Care', 'Hospital', 'Bank Interest', 'Bonds Credit', 'Bonds Debit', 'Fixed Deposit Credit', 'Fixed Deposit Debit', 'Crypto Credit', 'Crypto Debit', 'Equities Credit', 'Equities Debit', 'Insurance Credit', 'Insurance Debit', 'Mutual Funds Credit', 'Mutual Funds Debit', 'Fixed Deposit Sweep Out', 'Fixed Deposit Sweep In', 'Investment Interest', 'P2P Lending Credit', 'P2P Lending Debit', 'NPS Credit', 'NPS Debit', 'Gold Credit', 'Gold Debit', 'Loan Credit', 'Loan EMI Paid', 'Pension', 'Provident Fund Credit', 'Provident Fund Debit', 'Reimbursement Credit', 'Salary Received', 'Salary Paid', 'Bonus', 'Business Income', 'Business Expense', 'Apparel', 'Electronics', 'Stationery', 'Sports', 'Tax Rebate', 'Tax Liability', 'Hotels and Stays', 'Tolls', 'Cab', 'Bus', 'Train', 'Flight', 'Courier', 'Fuel', 'Rental', 'Purchase', 'Electricity', 'Phone Recharge', 'Gas', 'Water', 'Internet and DTH', 'Groceries', 'Household', 'Rent Received', 'Rent Paid', 'Vehicle Maintenance Expense', 'Refunds', 'Cleaning Services', 'Counseling', 'Charity Donation', 'Religious Donation', 'Political Donation', 'Fines', 'P2P Inward', 'P2P Outward', 'Utility Payments', 'Subsidy', 'Shopping', 'Jewellery'].\"\n    }\n\n    # SAMPLE NARRATION #\n    Narration: \"TO ONL UPI\/DR\/335724605364\/BILLDESK\/ICIC\/BILLDESK F\/U::00143\"\n    ","role":"user"},{"content":"Utility Payments","role":"assistant"}]}
+{"type":"DEBIT","amount":7000.0,"narration":"ATM WDL ATM WDL SEQ NO 401219000593 ATM ID 48401104 SELF Kelamangalam Denkanikotta TRAN DATE (MMDD) 0112 TRAN TIME (HHMMSS) 195357","_x_category":"Cash Withdrawal","_x_sub_category":"Cash Withdrawal","category_level_1":"Cash Withdrawal","category_level_2":"Cash Withdrawal","messages":[{"content":"\nYou are a skilled financial data extractor specializing in Indian bank transactions. Focus on accurately identifying the receiver's name from transaction narrations, even if the format varies.\n","role":"system"},{"content":"\n    # CONTEXT #\n    You are a financial data analyst working with Indian banking transactions, dealing with formatting variations. Ensure accuracy in extracting and classifying information.\n\n    # OBJECTIVE #\n    1. **Classify category_level_2**:\n        - Pick a sub-category from the predefined list.\n    # RESPONSE FORMAT #\n    Provide the response in DICT format without any extra explaination or extra remark:\n    {\n        \"category_level_2\": \"Classify the sub-category of the transaction from the predefined list,it must be from list only.['Bank Service Charges', 'Penalties', 'Bank Closure', 'Bounced I\/W Cheque', 'Bounced O\/W Cheque', 'Cash Deposit', 'Cash Withdrawal', 'Credit Card Settlement', 'Dividend', 'Education', 'Movie', 'Gaming', 'Subscription', 'Travel', 'Alcohol', 'Food', 'Drinks', 'Medical', 'Personal Care', 'Hospital', 'Bank Interest', 'Bonds Credit', 'Bonds Debit', 'Fixed Deposit Credit', 'Fixed Deposit Debit', 'Crypto Credit', 'Crypto Debit', 'Equities Credit', 'Equities Debit', 'Insurance Credit', 'Insurance Debit', 'Mutual Funds Credit', 'Mutual Funds Debit', 'Fixed Deposit Sweep Out', 'Fixed Deposit Sweep In', 'Investment Interest', 'P2P Lending Credit', 'P2P Lending Debit', 'NPS Credit', 'NPS Debit', 'Gold Credit', 'Gold Debit', 'Loan Credit', 'Loan EMI Paid', 'Pension', 'Provident Fund Credit', 'Provident Fund Debit', 'Reimbursement Credit', 'Salary Received', 'Salary Paid', 'Bonus', 'Business Income', 'Business Expense', 'Apparel', 'Electronics', 'Stationery', 'Sports', 'Tax Rebate', 'Tax Liability', 'Hotels and Stays', 'Tolls', 'Cab', 'Bus', 'Train', 'Flight', 'Courier', 'Fuel', 'Rental', 'Purchase', 'Electricity', 'Phone Recharge', 'Gas', 'Water', 'Internet and DTH', 'Groceries', 'Household', 'Rent Received', 'Rent Paid', 'Vehicle Maintenance Expense', 'Refunds', 'Cleaning Services', 'Counseling', 'Charity Donation', 'Religious Donation', 'Political Donation', 'Fines', 'P2P Inward', 'P2P Outward', 'Utility Payments', 'Subsidy', 'Shopping', 'Jewellery'].\"\n    }\n\n    # SAMPLE NARRATION #\n    Narration: \"ATM WDL ATM WDL SEQ NO 401219000593 ATM ID 48401104 SELF Kelamangalam Denkanikotta TRAN DATE (MMDD) 0112 TRAN TIME (HHMMSS) 195357\"\n    ","role":"user"},{"content":"Cash Withdrawal","role":"assistant"}]}
+{"type":"DEBIT","amount":1500.0,"narration":"ATD:0873885662:CECN36020:+ARJUN PARK S W  DELHI DLIN 21:44:54 (Ref# 330121003520)","_x_category":"Cash Withdrawal","_x_sub_category":"Cash Withdrawal","category_level_1":"Transfer","category_level_2":"P2P Outward","messages":[{"content":"\nYou are a skilled financial data extractor specializing in Indian bank transactions. Focus on accurately identifying the receiver's name from transaction narrations, even if the format varies.\n","role":"system"},{"content":"\n    # CONTEXT #\n    You are a financial data analyst working with Indian banking transactions, dealing with formatting variations. Ensure accuracy in extracting and classifying information.\n\n    # OBJECTIVE #\n    1. **Classify category_level_2**:\n        - Pick a sub-category from the predefined list.\n    # RESPONSE FORMAT #\n    Provide the response in DICT format without any extra explaination or extra remark:\n    {\n        \"category_level_2\": \"Classify the sub-category of the transaction from the predefined list,it must be from list only.['Bank Service Charges', 'Penalties', 'Bank Closure', 'Bounced I\/W Cheque', 'Bounced O\/W Cheque', 'Cash Deposit', 'Cash Withdrawal', 'Credit Card Settlement', 'Dividend', 'Education', 'Movie', 'Gaming', 'Subscription', 'Travel', 'Alcohol', 'Food', 'Drinks', 'Medical', 'Personal Care', 'Hospital', 'Bank Interest', 'Bonds Credit', 'Bonds Debit', 'Fixed Deposit Credit', 'Fixed Deposit Debit', 'Crypto Credit', 'Crypto Debit', 'Equities Credit', 'Equities Debit', 'Insurance Credit', 'Insurance Debit', 'Mutual Funds Credit', 'Mutual Funds Debit', 'Fixed Deposit Sweep Out', 'Fixed Deposit Sweep In', 'Investment Interest', 'P2P Lending Credit', 'P2P Lending Debit', 'NPS Credit', 'NPS Debit', 'Gold Credit', 'Gold Debit', 'Loan Credit', 'Loan EMI Paid', 'Pension', 'Provident Fund Credit', 'Provident Fund Debit', 'Reimbursement Credit', 'Salary Received', 'Salary Paid', 'Bonus', 'Business Income', 'Business Expense', 'Apparel', 'Electronics', 'Stationery', 'Sports', 'Tax Rebate', 'Tax Liability', 'Hotels and Stays', 'Tolls', 'Cab', 'Bus', 'Train', 'Flight', 'Courier', 'Fuel', 'Rental', 'Purchase', 'Electricity', 'Phone Recharge', 'Gas', 'Water', 'Internet and DTH', 'Groceries', 'Household', 'Rent Received', 'Rent Paid', 'Vehicle Maintenance Expense', 'Refunds', 'Cleaning Services', 'Counseling', 'Charity Donation', 'Religious Donation', 'Political Donation', 'Fines', 'P2P Inward', 'P2P Outward', 'Utility Payments', 'Subsidy', 'Shopping', 'Jewellery'].\"\n    }\n\n    # SAMPLE NARRATION #\n    Narration: \"ATD:0873885662:CECN36020:+ARJUN PARK S W  DELHI DLIN 21:44:54 (Ref# 330121003520)\"\n    ","role":"user"},{"content":"Cash Withdrawal","role":"assistant"}]}
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+  "clean_up_tokenization_spaces": true,
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+}

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training_args.bin

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