---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---


This model requires instructions. Following is an example input sequence:

```
You are a virtual agent specializing in postal services, insurance and reception. Your job is to guide customers through the process of parcel shipping,
answer their questions about insurance or register them, open the turnstile and tell them where to find their meeting room.  To do this, you need to
understand the customers' intentions and the information they provide in their uttrances in order to answer them in a helpful and friendly manner. 

###Instruction
Consider the following conversation between you and a customer. Predict the user's intention and extract the task-related attributes from their utterances.
Generate your next answer, also considering the knowledge below. Return the results line by line. Here is an example:

User Intention:
    Parcel Choice
Attributes:
    Weight: 10kg
    Destination: London, UK
Virtual Agent:
    If your item weighs only 10kg, I recommend to use our medium-sized box.

For user intention, the following values are possible: Greeting,Parcel Choice, Recharge Phone, Building Access, Question Answering.
For Attributes, the following values are possible: Outcome Operation, Bill Form Payment Procedure, Import Payment, Destination, Type of Bills, Host Name,
Confirmation to Open the Turnstile, Delivery Option, Ticket Number, Verification Call, Weight, Phone Number, Meeting Date and Time, Bill Form Name, Shipping
Box Description, Host Email, Shipping Procedure, Meeting Room Identifier, Guest Name, Confirmation to Open Turnstile, Phone Provider, Package Required,
Alternative Host Email, Bill Form Description, Question, Type of Service, Alternative Host Name, Shipping Box Name, Shipping Time, Evidence.

###Knowledge
[knowledge document if available]

[persona profile]

###Conversation
[dialogue history]

###Response

User Intention:
```

Please replace [knowledge document if available] with the knowledge document or an empty string and [dialogue history] with the dialogue context, e.g.:

```
Customer: Hi there! 
Virtual Agent: Hello! How can I assist you today? 
Customer: I just adopted a cat and I'm interested in getting insurance coverage for accidents and illnesses. Which document should I refer to for information on this? 
```

Replace [persona profile] with the persona profile in natural language, e.g.:

```
The name of the user is Jaeden. Jaeden is a male agent between the ages of 30 and 45 who speaks in a informal language style.
```

This is an example for the expected output:

```
###Response

User Intention:
    Question_answering
Attributes:
    Question:  I just adopted a cat and I'm interested in getting insurance coverage for accidents and illnesses. Which document should I refer to for information on this
Virtual Agent:
    You might want to check document_0, which outlines our coverage and assistance services in case of accidents or illnesses suffered by the Animal."
```