This LoRA adapter was fine-tuned on the bitext/Bitext-customer-support-llm-chatbot-training-dataset, specifically by:
category column values: ACCOUNT, CANCEL, SUBSCRIPTIONintent and response columns into a new single column called response_json that is a JSON object consisting of two keys: intent and response.This is what the dataset looks like once it is preprared:
It also has the following token distribution (without the prompt template being merged into the input)
This adapter was instruction tuned using the following prompt template:
You are a support agent for a company and you receive requests from customers.
Your job is to reply to the customer by providing both the intent, which you
should determine from the customer's request, as well as an appropriate response.
Please note that the intent can only be one of the following: registration_problems, newsletter_subscription, recover_password, check_cancellation_fee, create_account, switch_account, edit_account, delete_account.
Please package your reply in the JSON format.
Request: {instruction}
Reply:
At inference time, just replace/insert {instruction} with an actual instruction.
This adapter was fine-tuned using Predibase. You can sign up for a free trial and follow along using this notebook to reproduce this adapter: https://colab.research.google.com/drive/1Zzkrr40NRylUnq-pztaypVOoDPS2s9Vr
Input:
You are a support agent for a company and you receive requests from customers.
Your job is to reply to the customer by providing both the intent, which you
should determine from the customer's request, as well as an appropriate response.
Please note that the intent can only be one of the following: registration_problems, newsletter_subscription, recover_password, check_cancellation_fee, create_account, switch_account, edit_account, delete_account.
Please package your reply in the JSON format.
Request: where can I get information about opening {{Account Category}} accounts?
Reply:
Output (rendered using pprint, but is a proper JSON with full key-value pairs):
{'intent': 'create_account',
'response': "I'm glad you asked! You can find all the information you need "
'about opening {{Account Category}} accounts on our website. '
'Simply visit our homepage and navigate to the account section. '
"There, you'll find detailed information about the benefits, "
'features, and requirements for opening a {{Account Category}} '
'account. If you have any specific questions or need further '
'assistance, feel free to reach out to our customer support team '
"who will be more than happy to help you. We're here to make the "
'process as smooth as possible for you!'}