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  1. README.md +38 -148
  2. config.json +59 -59
  3. label_encoder.joblib +2 -2
  4. model.safetensors +1 -1
  5. training_args.bin +2 -2
README.md CHANGED
@@ -1,147 +1,33 @@
1
  ---
 
 
2
  tags:
3
  - generated_from_trainer
4
- - banking
5
- - finance
6
- - internet banking
7
- - mobile banking
8
- - Natural User Interface
9
- - NUI
10
- - Voice Banking
11
  model-index:
12
  - name: TenaliAI-FinTech-v1
13
  results: []
14
- widget:
15
- - text: Can you pls tell me what is the latest balance in my account number 1001
16
- example_title: Balance Enquiry
17
- - text: I want to send money abroad. What is the process
18
- example_title: Outward Remittance
19
- - text: What is the Fixed Deposit rate for a bulk amount of more than 2 crore INR?
20
- example_title: Bulk FD Rate
21
- - text: What is the total interest paid on my loan till date?
22
- example_title: Loan Details
23
- - text: Can I set a daily spending limit on my debit card? How do I do that?
24
- example_title: Transaction Limit
25
- - text : Tell me the status of the funds transfer I initiated last Wednesday.
26
- example_title: Funds Transfer Status
27
- - text : Can you provide a list of all the Fixed Deposits linked to my customer ID?
28
- example_title: Fixed Deposit List
29
- - text : How can I change my email address associated with the account?
30
- example_title: Change Contact Info
31
- - text : Please send the loan account statement for the month of June to my mailing address.
32
- example_title: Account Statement
33
- - text : Show me last 15 transactions of my saving account
34
- example_title: Transaction History
35
- license: apache-2.0
36
- language:
37
- - en
38
- metrics:
39
- - accuracy
40
  ---
41
 
42
-
43
-
44
 
45
  # TenaliAI-FinTech-v1
46
 
47
- This model was trained from scratch on banking dataset.
48
  It achieves the following results on the evaluation set:
49
- - Loss: 0.1350
50
 
51
  ## Model description
52
 
53
- This project is integral to the development of a Natural User Interface(NUI) within the Banking and Finance Industry [BFSI].
54
-
55
- The TenaliAI-FinTech model is specifically designed to tackle the intricate task of deciphering the intent behind customer queries in the BFSI sector.
56
-
57
- The underlying technology behind TenaliAI-FinTech employs advanced natural language processing and machine learning algorithms. These technologies enhance the model's ability to accurately classify and understand the diverse range of customer queries. By leveraging sophisticated classification techniques, the model ensures a more precise interpretation of user intent, regardless of whether the query originates from the bank's net banking portal, mobile banking portal, or other communication channels.
58
-
59
- Furthermore, the model excels in query tokenization, making it proficient in breaking down customer queries into meaningful components. This capability not only streamlines the processing of customer requests but also enables a more efficient and targeted response.
60
-
61
- Ultimately, the technology powering TenaliAI-FinTech contributes to an enhanced customer service experience by providing quicker and more accurate responses to inquiries across multiple banking platforms.
62
 
63
  ## Intended uses & limitations
64
 
65
- This model is meant to generate "Intent" for a given customer query on bank's netbanking portal or mobile banking. Following is the list of intents :
66
-
67
- <pre>
68
- {
69
- 'add_beneficiary': 0,
70
- 'balance_enquiry': 1,
71
- 'beneficiary_details': 2,
72
- 'bill_payment': 3,
73
- 'block_card': 4,
74
- 'bulk_payments': 5,
75
- 'bulk_payments_status': 6,
76
- 'change_contact_info': 7,
77
- 'debit_card_details': 8,
78
- 'delete_beneficiary': 9,
79
- 'fd_details': 10,
80
- 'fd_rate': 11,
81
- 'fd_rate_large_amount': 12,
82
- 'funds_transfer_other_bank': 13,
83
- 'funds_transfer_own_account': 14,
84
- 'funds_transfer_status': 15,
85
- 'funds_transfer_third_party': 16,
86
- 'gst_payment': 17,
87
- 'investment_details': 18,
88
- 'list_accounts': 19,
89
- 'list_beneficiary': 20,
90
- 'list_billers': 21,
91
- 'list_fd': 22,
92
- 'list_investments': 23,
93
- 'list_loans': 24,
94
- 'loan_details': 25,
95
- 'nrv_details': 26,
96
- 'open_account': 27,
97
- 'pending_authorization': 28,
98
- 'pin_change': 29,
99
- 'raise_request': 30,
100
- 'request_status': 31,
101
- 'saving_interest_rate': 32,
102
- 'send_money_abroad': 33,
103
- 'ss_fd_rate': 34,
104
- 'transaction_history': 35,
105
- 'transaction_limit': 36,
106
- 'update_beneficiary': 37}
107
- </pre>
108
-
109
- How to use :
110
-
111
- 1. Type a query such as
112
- - "Tell me my last 10 transactions"
113
- - "I am senior citizen. What is FD rates"
114
- - "I want to send money to my brother"
115
- - "I want Fixed Deposit rate for 2 Crore INR"
116
- - "What is the outstanding EMI or my loan"
117
- - "How many active loans do I have ?"
118
- - "I want to add a new beneficiary"
119
- 3. This engine will understand the "intent" behind the query and return the value of LABEL_0 to LABEL_50.
120
- 4. The LABEL having maximum value (which will be at the top in the result) will be the identified "intent"
121
- 5. Use above mapping table and convert LABEL to Code. So, for example, LABEL_34 means "Senior Citizen Fixed Deposit Rate" and so on.
122
 
123
  ## Training and evaluation data
124
 
125
-
126
- | Training Loss | Epoch | Step | Validation Loss |
127
- |:-------------:|:-----:|:----:|:---------------:|
128
- | No log | 1.0 | 222 | 2.3696 |
129
- | No log | 2.0 | 444 | 1.0432 |
130
- | 2.3371 | 3.0 | 666 | 0.3821 |
131
- | 2.3371 | 4.0 | 888 | 0.1731 |
132
- | 0.3285 | 5.0 | 1110 | 0.1151 |
133
- | 0.3285 | 6.0 | 1332 | 0.1089 |
134
- | 0.0443 | 7.0 | 1554 | 0.1107 |
135
- | 0.0443 | 8.0 | 1776 | 0.1083 |
136
- | 0.0443 | 9.0 | 1998 | 0.1025 |
137
- | 0.0153 | 10.0 | 2220 | 0.1048 |
138
-
139
-
140
- ### Framework versions
141
-
142
- - Transformers 4.41.1
143
- - Pytorch 2.3.0
144
- - Tokenizers 0.19.1
145
 
146
  ## Training procedure
147
 
@@ -154,37 +40,41 @@ The following hyperparameters were used during training:
154
  - seed: 42
155
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
156
  - lr_scheduler_type: linear
157
- - num_epochs: 20
158
 
159
  ### Training results
160
 
161
  | Training Loss | Epoch | Step | Validation Loss |
162
  |:-------------:|:-----:|:----:|:---------------:|
163
- | No log | 1.0 | 229 | 1.9891 |
164
- | No log | 2.0 | 458 | 0.6549 |
165
- | 2.1005 | 3.0 | 687 | 0.1826 |
166
- | 2.1005 | 4.0 | 916 | 0.0937 |
167
- | 0.2019 | 5.0 | 1145 | 0.0764 |
168
- | 0.2019 | 6.0 | 1374 | 0.0788 |
169
- | 0.0251 | 7.0 | 1603 | 0.0759 |
170
- | 0.0251 | 8.0 | 1832 | 0.0758 |
171
- | 0.0115 | 9.0 | 2061 | 0.0773 |
172
- | 0.0115 | 10.0 | 2290 | 0.0777 |
173
- | 0.0073 | 11.0 | 2519 | 0.0787 |
174
- | 0.0073 | 12.0 | 2748 | 0.0805 |
175
- | 0.0073 | 13.0 | 2977 | 0.0815 |
176
- | 0.0053 | 14.0 | 3206 | 0.0816 |
177
- | 0.0053 | 15.0 | 3435 | 0.0824 |
178
- | 0.0041 | 16.0 | 3664 | 0.0838 |
179
- | 0.0041 | 17.0 | 3893 | 0.0828 |
180
- | 0.0035 | 18.0 | 4122 | 0.0836 |
181
- | 0.0035 | 19.0 | 4351 | 0.0836 |
182
- | 0.0031 | 20.0 | 4580 | 0.0837 |
 
 
 
 
 
183
 
184
 
185
  ### Framework versions
186
 
187
- - Transformers 4.30.0
188
- - Pytorch 2.0.1
189
- - Datasets 2.12.0
190
- - Tokenizers 0.13.3
 
1
  ---
2
+ license: apache-2.0
3
+ base_model: bert-base-uncased
4
  tags:
5
  - generated_from_trainer
 
 
 
 
 
 
 
6
  model-index:
7
  - name: TenaliAI-FinTech-v1
8
  results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9
  ---
10
 
11
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
12
+ should probably proofread and complete it, then remove this comment. -->
13
 
14
  # TenaliAI-FinTech-v1
15
 
16
+ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 0.1134
19
 
20
  ## Model description
21
 
22
+ More information needed
 
 
 
 
 
 
 
 
23
 
24
  ## Intended uses & limitations
25
 
26
+ More information needed
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
27
 
28
  ## Training and evaluation data
29
 
30
+ More information needed
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
31
 
32
  ## Training procedure
33
 
 
40
  - seed: 42
41
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
42
  - lr_scheduler_type: linear
43
+ - num_epochs: 25
44
 
45
  ### Training results
46
 
47
  | Training Loss | Epoch | Step | Validation Loss |
48
  |:-------------:|:-----:|:----:|:---------------:|
49
+ | No log | 1.0 | 224 | 2.5812 |
50
+ | No log | 2.0 | 448 | 1.2454 |
51
+ | 2.5217 | 3.0 | 672 | 0.4337 |
52
+ | 2.5217 | 4.0 | 896 | 0.1572 |
53
+ | 0.4296 | 5.0 | 1120 | 0.1191 |
54
+ | 0.4296 | 6.0 | 1344 | 0.1208 |
55
+ | 0.0629 | 7.0 | 1568 | 0.1140 |
56
+ | 0.0629 | 8.0 | 1792 | 0.1212 |
57
+ | 0.0254 | 9.0 | 2016 | 0.1215 |
58
+ | 0.0254 | 10.0 | 2240 | 0.1213 |
59
+ | 0.0254 | 11.0 | 2464 | 0.1136 |
60
+ | 0.0118 | 12.0 | 2688 | 0.1134 |
61
+ | 0.0118 | 13.0 | 2912 | 0.1161 |
62
+ | 0.0092 | 14.0 | 3136 | 0.1191 |
63
+ | 0.0092 | 15.0 | 3360 | 0.1305 |
64
+ | 0.0056 | 16.0 | 3584 | 0.1332 |
65
+ | 0.0056 | 17.0 | 3808 | 0.1343 |
66
+ | 0.0055 | 18.0 | 4032 | 0.1362 |
67
+ | 0.0055 | 19.0 | 4256 | 0.1362 |
68
+ | 0.0055 | 20.0 | 4480 | 0.1450 |
69
+ | 0.0037 | 21.0 | 4704 | 0.1421 |
70
+ | 0.0037 | 22.0 | 4928 | 0.1460 |
71
+ | 0.003 | 23.0 | 5152 | 0.1472 |
72
+ | 0.003 | 24.0 | 5376 | 0.1428 |
73
+ | 0.0026 | 25.0 | 5600 | 0.1493 |
74
 
75
 
76
  ### Framework versions
77
 
78
+ - Transformers 4.43.4
79
+ - Pytorch 2.4.0
80
+ - Tokenizers 0.19.1
 
config.json CHANGED
@@ -25,35 +25,35 @@
25
  "12": "fd_rate",
26
  "13": "fd_rate_large_amount",
27
  "14": "funds_transfer",
28
- "15": "funds_transfer_other_bank",
29
- "16": "funds_transfer_own_account",
30
- "17": "funds_transfer_status",
31
- "18": "funds_transfer_third_party",
32
- "19": "gst_payment",
33
- "20": "investment_details",
34
- "21": "list_accounts",
35
- "22": "list_beneficiary",
36
- "23": "list_billers",
37
- "24": "list_fd",
38
- "25": "list_investments",
39
- "26": "list_loans",
40
- "27": "loan_details",
41
- "28": "nrv_details",
42
- "29": "open_account",
43
- "30": "pending_authorization",
44
- "31": "pin_change",
45
- "32": "raise_request",
46
- "33": "rd_details",
47
- "34": "rd_rate",
48
- "35": "request_status",
49
- "36": "saving_interest_rate",
50
- "37": "send_money_abroad",
51
- "38": "ss_fd_rate",
52
- "39": "ss_rd_rate",
53
- "40": "transaction_history",
54
- "41": "transaction_limit",
55
- "42": "update_beneficiary",
56
- "43": "zzzLABEL_43",
57
  "44": "zzzLABEL_44",
58
  "45": "zzzLABEL_45",
59
  "46": "zzzLABEL_46",
@@ -79,35 +79,35 @@
79
  "fd_rate": 12,
80
  "fd_rate_large_amount": 13,
81
  "funds_transfer": 14,
82
- "funds_transfer_other_bank": 15,
83
- "funds_transfer_own_account": 16,
84
- "funds_transfer_status": 17,
85
- "funds_transfer_third_party": 18,
86
- "gst_payment": 19,
87
- "investment_details": 20,
88
- "list_accounts": 21,
89
- "list_beneficiary": 22,
90
- "list_billers": 23,
91
- "list_fd": 24,
92
- "list_investments": 25,
93
- "list_loans": 26,
94
- "loan_details": 27,
95
- "nrv_details": 28,
96
- "open_account": 29,
97
- "pending_authorization": 30,
98
- "pin_change": 31,
99
- "raise_request": 32,
100
- "rd_details": 33,
101
- "rd_rate": 34,
102
- "request_status": 35,
103
- "saving_interest_rate": 36,
104
- "send_money_abroad": 37,
105
- "ss_fd_rate": 38,
106
- "ss_rd_rate": 39,
107
- "transaction_history": 40,
108
- "transaction_limit": 41,
109
- "update_beneficiary": 42,
110
- "zzzLABEL_43": 43,
111
  "zzzLABEL_44": 44,
112
  "zzzLABEL_45": 45,
113
  "zzzLABEL_46": 46,
@@ -124,7 +124,7 @@
124
  "position_embedding_type": "absolute",
125
  "problem_type": "single_label_classification",
126
  "torch_dtype": "float32",
127
- "transformers_version": "4.41.1",
128
  "type_vocab_size": 2,
129
  "use_cache": true,
130
  "vocab_size": 30522
 
25
  "12": "fd_rate",
26
  "13": "fd_rate_large_amount",
27
  "14": "funds_transfer",
28
+ "15": "funds_transfer_direct",
29
+ "16": "funds_transfer_other_bank",
30
+ "17": "funds_transfer_own_account",
31
+ "18": "funds_transfer_status",
32
+ "19": "funds_transfer_third_party",
33
+ "20": "gst_payment",
34
+ "21": "investment_details",
35
+ "22": "list_accounts",
36
+ "23": "list_beneficiary",
37
+ "24": "list_billers",
38
+ "25": "list_fd",
39
+ "26": "list_investments",
40
+ "27": "list_loans",
41
+ "28": "loan_details",
42
+ "29": "nrv_details",
43
+ "30": "open_account",
44
+ "31": "pending_authorization",
45
+ "32": "pin_change",
46
+ "33": "raise_request",
47
+ "34": "rd_details",
48
+ "35": "rd_rate",
49
+ "36": "request_status",
50
+ "37": "saving_interest_rate",
51
+ "38": "send_money_abroad",
52
+ "39": "ss_fd_rate",
53
+ "40": "ss_rd_rate",
54
+ "41": "transaction_history",
55
+ "42": "transaction_limit",
56
+ "43": "update_beneficiary",
57
  "44": "zzzLABEL_44",
58
  "45": "zzzLABEL_45",
59
  "46": "zzzLABEL_46",
 
79
  "fd_rate": 12,
80
  "fd_rate_large_amount": 13,
81
  "funds_transfer": 14,
82
+ "funds_transfer_direct": 15,
83
+ "funds_transfer_other_bank": 16,
84
+ "funds_transfer_own_account": 17,
85
+ "funds_transfer_status": 18,
86
+ "funds_transfer_third_party": 19,
87
+ "gst_payment": 20,
88
+ "investment_details": 21,
89
+ "list_accounts": 22,
90
+ "list_beneficiary": 23,
91
+ "list_billers": 24,
92
+ "list_fd": 25,
93
+ "list_investments": 26,
94
+ "list_loans": 27,
95
+ "loan_details": 28,
96
+ "nrv_details": 29,
97
+ "open_account": 30,
98
+ "pending_authorization": 31,
99
+ "pin_change": 32,
100
+ "raise_request": 33,
101
+ "rd_details": 34,
102
+ "rd_rate": 35,
103
+ "request_status": 36,
104
+ "saving_interest_rate": 37,
105
+ "send_money_abroad": 38,
106
+ "ss_fd_rate": 39,
107
+ "ss_rd_rate": 40,
108
+ "transaction_history": 41,
109
+ "transaction_limit": 42,
110
+ "update_beneficiary": 43,
111
  "zzzLABEL_44": 44,
112
  "zzzLABEL_45": 45,
113
  "zzzLABEL_46": 46,
 
124
  "position_embedding_type": "absolute",
125
  "problem_type": "single_label_classification",
126
  "torch_dtype": "float32",
127
+ "transformers_version": "4.43.4",
128
  "type_vocab_size": 2,
129
  "use_cache": true,
130
  "vocab_size": 30522
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