predibase
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customer_support

Text Generation

PEFT

Safetensors

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----
-library_name: peft
-base_model: mistralai/Mistral-7b-v0.1
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
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-## Uses
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-### Direct Use
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-## Citation [optional]
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-## Glossary [optional]
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-### Framework versions
-- PEFT 0.7.1

+---\
+library_name: peft\
+Base model: mistralai/Mistral-7B-v0.1\
+pipeline_tag: text-generation\
+---\
+Description: Customer support call classification given call transcript\
+Original dataset: customer_support \
+---\
+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\
+---\
+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: <caller> [noise] <agent> [noise] <caller> [noise] <caller> [noise] hello <caller> hello <agent> 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 <caller> hi <caller> oh okay my name is jennifer brown and i need to check my account balance if i could <caller> [noise] <caller> [noise] [noise] [noise] <agent> what account would you like to check <caller> um <caller> [noise] <caller> <unk> <caller> uhm my savings account <caller> please <caller> <unk> <caller> [noise] <caller> [noise] <caller> oh but the way that you're doing <agent> one moment <caller> hello <agent> yeah one moment <caller> uh huh <caller> no problem <caller> [noise] <agent> your account balance is eighty two dollars is there anything else i can help you with <caller> no i don't think so thank you so much you were very helpful <agent> thank you <caller> have a good day bye bye <caller> [noise] <agent> you too \n\n### Task Type:\n\ntest_transcript = \
+---\
+Sample output: check balance\
+---\
+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)
+```