library_name: peft
base_model: mistralai/Mistral-7B-v0.1
pipeline_tag: text-generation
Description: Customer support call classification given call transcript
Original dataset: https://github.com/cricketclub/gridspace-stanford-harper-valley
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Try querying this adapter for free in Lora Land at https://predibase.com/lora-land!
The adapter_category is Topic Identification and the name is Customer Support Automation
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Sample input: Consider the case of a customer contacting the support center.\nThe term "task type" refers to the reason for why the customer contacted support.\n\n### The possible task types are: ### \n- replace card\n- transfer money\n- check balance\n- order checks\n- pay bill\n- reset password\n- schedule appointment\n- get branch hours\n- none of the above\n\nSummarize the issue/question/reason that drove the customer to contact support:\n\n### Transcript: [noise] [noise] [noise] [noise] hello hello hi i'm sorry this this call uh hello this is harper valley national bank my name is dawn how can i help you today hi oh okay my name is jennifer brown and i need to check my account balance if i could [noise] [noise] [noise] [noise] what account would you like to check um [noise] uhm my savings account please [noise] [noise] oh but the way that you're doing one moment hello yeah one moment uh huh no problem [noise] your account balance is eighty two dollars is there anything else i can help you with no i don't think so thank you so much you were very helpful thank you have a good day bye bye [noise] you too \n\n### Task Type:\n\ntest_transcript =
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Sample output: check balance
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Try using this adapter yourself!
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mistral-7B-v0.1"
peft_model_id = "predibase/customer_support"
model = AutoModelForCausalLM.from_pretrained(model_id)
model.load_adapter(peft_model_id)