promptsource VS rasa

Bigscience Promptsource

Quick 5 Min Read | Paper|Github

Code for https://arxiv.org/abs/2110.08207 found: https://github.com/bigscience-workshop/promptsource/

I'd use a prompted dataset then, as well as explore the TO model framework.

We fine-tuned the model on a dozens of different NLP datasets and tasks in a prompted style. You can read all the prompts in the appendix or get them all here: https://github.com/bigscience-workshop/promptsource . Most NLP tasks are not particularly freeform, or they are naturally length limited like summary (XSum is very short). As a consequence, the model mostly defaults to short responses. Your "trick" is not that unreasonable though! Many of the training prompts that want long responses, ask for them explicitly.

Support Rasa on GitHub ⭐

It itself is not a GPT. It is a a framework of a framework project built on top of Rasa (https://github.com/RasaHQ/rasa) and Langchain which by default uses gpt3.5-turbo (change it in the .env file) or any foundation model you wish.

There was already a GitHub issue https://github.com/RasaHQ/rasa/issues/11084 but it seems like there was no solution provided.

Rasa - also an open source platform. You can use it to build chatbots that are more in line with customer service and sales. Reading that, it’s kind of a big yikes for people under this kind of job knowing you can automate a part of their job via a chatbot. I think they also have a Pro version which is an extension that has additional analytics and probably more security features. I probably will check out the Open source one first and maybe ask about what the Pro version is.

At the moment it can recognise the type of statements in the training data set [1] and transpile them to Python, Java or C++ using the mappings defined here [2].

This is very different from how Codex/Autopilot work as it is trained using an NLU framework [3] which is usually used for training chatbots.

[1]: https://github.com/Flex-lang/transpiler/tree/master/transpil...

[2]: https://github.com/Flex-lang/transpiler/tree/master/transpil...

[3]: https://github.com/RasaHQ/rasa

Check out Rasa

It looks like it is built upon a Chatbot platform called “messengerx.io”, which itself seems to be built upon an open-source/machine-learning framework for voice/text conversations called “Rasa”

Use Python and use the Rasa framework to make a chat bot. https://rasa.com/docs/

This example is sourced from RasaHQ