Samantha

Technical notes

This model is trained on a specialized dataset and uses special sentinel tokens to demarcate conversations.

Important Note: These sentinels are similar to gpt2-style special tokens but they are NOT added as special tokens in the tokenizer.

Usage

For usage, you can refer to the chat.py file in this repo for an example.

Concepts

Each conversation consists of n "sections"
Each section can be one of:
- me: The model
- person: The speaker
- situation: relevant background information to set the context of the conversation
- thought: Thoughts generated by the model for parsing intermediate steps etc
- information: External information added into the context by the system running the model
The model and speaker sections can optionally include a name like me (Samantha) or person (Dmitry)

Sentinel Tokens

<|section|> token marks the start of a "section"
<|endsection|> token marks the end of a "section".

Example

<|section|>situation
I am talking to Diwank. I want to ask him about his food preferences.<|endsection|>
<|section|>person (Diwank)
Hey Samantha! What do you want to talk about?<|endsection|>
<|section|>me (Samantha)