Update README.md

README.md

@@ -1,48 +1,127 @@
 ---
-license: apache-2.0
-base_model: bert-base-uncased
 tags:
 - generated_from_trainer
+- banking
+- finance
+- internet banking
+- mobile banking
+- Natural User Interface
+- NUI
+- Voice Banking
 model-index:
 - name: TenaliAI-FinTech-v1
   results: []
+widget:
+- text: Can you pls tell me what is the latest balance in my account number 1001
+  example_title: Balance Enquiry
+- text: I want to send money abroad. What is the process
+  example_title: Outward Remittance
+- text: What is the Fixed Deposit rate for a bulk amount of more than 2 crore INR?
+  example_title: Bulk FD Rate
+- text: What is the total interest paid on my loan till date?
+  example_title: Loan Details
+- text: Can I set a daily spending limit on my debit card? How do I do that?
+  example_title: Transaction Limit
+- text : Tell me the status of the funds transfer I initiated last Wednesday.
+  example_title: Funds Transfer Status
+- text : Can you provide a list of all the Fixed Deposits linked to my customer ID?
+  example_title: Fixed Deposit List
+- text : How can I change my email address associated with the account?
+  example_title: Change Contact Info
+- text : Please send the loan account statement for the month of June to my mailing address.
+  example_title: Account Statement
+- text : Show me last 15 transactions of my saving account
+  example_title: Transaction History
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+
+
 
 # TenaliAI-FinTech-v1
 
-This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
+This model was trained from scratch on banking dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1025
+- Loss: 0.1350
 
 ## Model description
 
-More information needed
+This project is integral to the development of a Natural User Interface(NUI) within the Banking and Finance Industry [BFSI].
 
-## Intended uses & limitations
+The TenaliAI-FinTech model is specifically designed to tackle the intricate task of deciphering the intent behind customer queries in the BFSI sector.
 
-More information needed
+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.
 
-## Training and evaluation data
+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.
 
-More information needed
+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.
 
-## Training procedure
+## Intended uses & limitations
 
-### Training hyperparameters
+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 :
+
+<pre>
+{
+    'add_beneficiary': 0, 
+    'balance_enquiry': 1, 
+    'beneficiary_details': 2, 
+    'bill_payment': 3, 
+    'block_card': 4, 
+    'bulk_payments': 5, 
+    'bulk_payments_status': 6, 
+    'change_contact_info': 7, 
+    'debit_card_details': 8, 
+    'delete_beneficiary': 9, 
+    'fd_details': 10, 
+    'fd_rate': 11, 
+    'fd_rate_large_amount': 12, 
+    'funds_transfer_other_bank': 13, 
+    'funds_transfer_own_account': 14, 
+    'funds_transfer_status': 15, 
+    'funds_transfer_third_party': 16, 
+    'gst_payment': 17, 
+    'investment_details': 18, 
+    'list_accounts': 19, 
+    'list_beneficiary': 20, 
+    'list_billers': 21, 
+    'list_fd': 22, 
+    'list_investments': 23, 
+    'list_loans': 24, 
+    'loan_details': 25, 
+    'nrv_details': 26, 
+    'open_account': 27, 
+    'pending_authorization': 28, 
+    'pin_change': 29, 
+    'raise_request': 30, 
+    'request_status': 31, 
+    'saving_interest_rate': 32, 
+    'send_money_abroad': 33, 
+    'ss_fd_rate': 34, 
+    'transaction_history': 35, 
+    'transaction_limit': 36, 
+    'update_beneficiary': 37}
+</pre>
+
+How to use :
+
+1. Type a query such as
+   - "Tell me my last 10 transactions"
+   - "I am senior citizen. What is FD rates"
+   - "I want to send money to my brother"
+   - "I want Fixed Deposit rate for 2 Crore INR"
+   - "What is the outstanding EMI or my loan"
+   - "How many active loans do I have ?"
+   - "I want to add a new beneficiary"
+3. This engine will understand the "intent" behind the query and return the value of LABEL_0 to LABEL_50.
+4. The LABEL having maximum value (which will be at the top in the result) will be the identified "intent"
+5. Use above mapping table and convert LABEL to Code. So, for example, LABEL_34 means "Senior Citizen Fixed Deposit Rate" and so on.
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 10
+## Training and evaluation data
 
-### Training results
 
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
@@ -63,3 +142,49 @@ The following hyperparameters were used during training:
 - Transformers 4.41.1
 - Pytorch 2.3.0
 - Tokenizers 0.19.1
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| No log        | 1.0   | 229  | 1.9891          |
+| No log        | 2.0   | 458  | 0.6549          |
+| 2.1005        | 3.0   | 687  | 0.1826          |
+| 2.1005        | 4.0   | 916  | 0.0937          |
+| 0.2019        | 5.0   | 1145 | 0.0764          |
+| 0.2019        | 6.0   | 1374 | 0.0788          |
+| 0.0251        | 7.0   | 1603 | 0.0759          |
+| 0.0251        | 8.0   | 1832 | 0.0758          |
+| 0.0115        | 9.0   | 2061 | 0.0773          |
+| 0.0115        | 10.0  | 2290 | 0.0777          |
+| 0.0073        | 11.0  | 2519 | 0.0787          |
+| 0.0073        | 12.0  | 2748 | 0.0805          |
+| 0.0073        | 13.0  | 2977 | 0.0815          |
+| 0.0053        | 14.0  | 3206 | 0.0816          |
+| 0.0053        | 15.0  | 3435 | 0.0824          |
+| 0.0041        | 16.0  | 3664 | 0.0838          |
+| 0.0041        | 17.0  | 3893 | 0.0828          |
+| 0.0035        | 18.0  | 4122 | 0.0836          |
+| 0.0035        | 19.0  | 4351 | 0.0836          |
+| 0.0031        | 20.0  | 4580 | 0.0837          |
+
+
+### Framework versions
+
+- Transformers 4.30.0
+- Pytorch 2.0.1
+- Datasets 2.12.0
+- Tokenizers 0.13.3