Model Card for Command Generator
This is a Dialogue Understanding (DU) model developed by Rasa.
It can be used to power assistants built with the
Conversational AI with Language Models (CALM) approach developed by Rasa.
Model Details
Model Description
This model takes as input a transcript of an ongoing conversation between an AI assistant and a user,
as well as structured information about the assistant's business logic.
As output, it produces a short sequence of
commands
(typically 1-3) from the following list:
StartFlow(flow_name)SetSlot(slot_name, slot_value)CorrectSlot(slot_name, slot_value)Clarify(flow_name_1, flow_name_2, ...)ChitChatKnowledgeAnswerHumanHandoffError
Note that this model can only produce commands to be interpreted by Rasa.
It cannot be used to generate arbitrary text.
The Command Generator translates user messages into this internal grammar, allowing CALM to progress the conversation.
Examples:
StartFlow(transfer_money)
I want to transfer $55 to John
StartFlow(transfer_money), SetSlot(recipient, John), SetSlot(amount, 55)
- Developed by: Rasa Technologies
- Model type: Text Generation
- Language(s) (NLP): English
- License: Apache 2.0
- Finetuned from model [optional]: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
Uses
The Command Generator is used as part of an AI assistant developed with Rasa's CALM paradigm.
Typical use cases include customer-facing chatbots, voice assistants, IVR systems, and internal chatbots in large organizations.
Direct Use
This model can be directly used as part of the command generator component if the flows of your CALM assistant are similar to flows used in the
rasa-calm-demo assistant.
Downstream Use [optional]
The model can also be used as a base model to fine-tune further on your own assistant's data using the
fine-tuning recipe feature available in rasa pro
Out-of-Scope Use
Since the model has been explicitly fine-tuned to output the grammar of commands, it shouldn't be used to generate any other free form content.
Bias, Risks, and Limitations
The Command Generator interprets conversations and translates user messages into commands.
These commands are processed by Rasa to advance the conversations.
This model does not generate text to be sent to and end user, and is incapable of generating problematic
or harmful text.
However, as with any pre-trained model, its predictions are susceptible to bias.
For example, the accuracy of the model varies with the language used. The authors have tested the performance on English but haven't tried the model in any other language.
Training Details
Training Data
Training Procedure
Trained using the notebook available
here. Used a single A100 GPU with 80GB VRAM.
Evaluation
Testing Data, Factors & Metrics
Testing Data
Metrics
F1 score per command type (StartFlow, SetSlot, etc.) is the main metric chosen to evaluate the model on the test split.
This helps us understand which commands the model has learnt well and the ones that the model needs more training on.
Results
To be added
Model Card Contact
If you have questions about the dataset, please reach out to us on the
Rasa forum.
