Failed trainning with entity roles

Rasa Open Source
1

Hi!

I’m trying to build a form (motor_form) with three slots (amount_of_potencia, amount_of_velocidad, alimentacion) and two of the three slots are numbers (amount_of_potencia, amount_of_velocidad), so I’m using entities roles as I saw in this post. But the trainning failed. I’m using rasa 2.6.2 and rasa-sdk 2.6.0.

Here’s the form

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Here’s the trainning error

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It failed when i declared the entities in this part.

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Here’s how I declared the slots

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I tried to update rasa to 2.8.6 but that wasn’t the problem.

I hope someone could help me :slight_smile: , thanks !!

2

Hi @tomimartin01 remove - before intent in first screenshoot under required_slots and also see this link: Forms

@tomimartin01 After intent you mention type but where in entity ? Further type should have -type again check the ref: Forms

I guess you only need to do a proper syntax and synchronised the flow of type, entity and intent you are good to go. Good Luck!

3

Hi @nik202 ! Thanks for reply me. Sorry, but I could understand your first correction → " remove - before intent in first screenshoot under required_slots and also see this link: Forms ".

Also, I belive that your second correction means this

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I remove the ignored intents flags, because I wrote a rule for stop the form.

The problem isn’t solve, and if I comment the code of third photo of mi last post the trainning starts ok.

Thanks!

4

@tomimartin01 Yes, I can see you had updated the code as per the reference link I shared with you, but you did not followed my second comment, please again check the links and how they have mention the type, entity, roles and intent and then entity.

@tomimartin01 What is your third photograph reflect is it slots? if yes try mention type to text

5

This is the third photo, in the entities section of the domain.

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If I comment line 326 to 329 the trainning begins,

6

@tomimartin01 Right, Can you please share what is the training example of these roles velocidad & potency | Please see this also for your ref: NLU Training Data

@tomimartin01 Even add more training data, in same format as shown in the ref link.