rasa VS unilm

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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

+1 On this, the real proof would have been testing both models side-by-side.

It seems that it may be published on GitHub [1] according to HuggingFace [2].

[1] https://github.com/microsoft/unilm/tree/master/bitnet

[2] https://huggingface.co/papers/2402.17764

e5-mistral is essentially a distillation from gpt-4 to a smaller model. You can see here https://github.com/microsoft/unilm/blob/16da2f193b9c1dab0a69...

they actually have custom prompts for each dataset being tested.

Question would be, if you haven't seen the task before, what is a good prompt to prepend for your task?

IMO e5-mistral is overfit to MTEB

Layout LM v1, v2 and v3 models [ Github ] DocBERT [ Github ]

The repository is available here.

It is missing kosmos-2. I remember its image captioning was(demo currently down) really good and it's almost as fast as llava and lavin.

Should be this: https://github.com/microsoft/unilm/

This is from Microsoft Research (Asia). https://aka.ms/GeneralAI