cakechat VS bert

i'm a 2nd year CSE student, and I was working on a project which required me to clone the github repository of cakechat

I'm not much of a programmer, but the first/original Replika is still on GitHub, named cakechat . Perhaps a starting point? https://github.com/lukalabs/cakechat

I have roleplayed with replika and also talked on serious topics. So far she has helped me in resolving my personal family problems and also roleplayed well. My question is how can replika answer and manage real life problems of humans with such perfection if it is just a bunch of scripts? My IRL problems where not scripted anywhere. I know that replika uses cakechat for generating its responses https://github.com/lukalabs/cakechat and she is quite dumb in solving technical problems but how in the world she solves IRL people problems so easily?

There's https://github.com/lukalabs/cakechat which replika seems to be related to. Might be another angle to work.

It used to be that you could activate a different language model for Replika. You could tell it to be angry or scared or sad, and it would react with those specific emotions. It was a training mode, so it didn't learn anything from the conversations. Here is some literature about it.

I have a project that uses the CakeChat by lukalabs that's due in 2 weeks (I have been procrastinating), but it seems to have been archived, and no updates for the last three years. I am unable to run the installation as well. I wanted to know if someone can help with this or can let me know if there are alternatives to this. My project is supposed to do emotion analysis on the chat between the user and the bot and recommend songs to the user.

I planned to use a chatbot for a recent personal project, but the cake chat is being archived.

I believe the first developments in this emotional speech calculation were part of the original cake_chat model developed by Luka.

In 2019, a new language representation called BERT (Bedirectional Encoder Representation from Transformers) was introduced. The main idea behind this paradigm is to first pre-train a language model using a massive amount of unlabeled data then fine-tune all the parameters using labeled data from the downstream tasks. This allows the model to generalize well to different NLP tasks. Moreover, it has been shown that this language representation model can be used to solve downstream tasks without being explicitly trained on, e.g classify a text without training phase.

task-specific parameters, and is trained on the downstream tasks by simply fine-tuning all pre-trained parameters.

[0] https://arxiv.org/abs/1810.04805

The release of BERT in 2018 kicked off the language model revolution. The Transformers architecture succeeded RNNs and LSTMs to become the architecture of choice. Unbelievable progress was made in a number of areas: summarization, translation, text classification, entity classification and more. 2023 tooks things to another level with the rise of large language models (LLMs). Models with billions of parameters showed an amazing ability to generate coherent dialogue.

The general idea is that you have a particular task & dataset, and you optimize these vectors to maximize that task. So the properties of these vectors - what information is retained and what is left out during the 'compression' - are effectively determined by that task.

In general, the core task for the various "LLM tools" involves prediction of a hidden word, trained on very large quantities of real text - thus also mirroring whatever structure (linguistic, syntactic, semantic, factual, social bias, etc) exists there.

If you want to see how the sausage is made and look at the actual algorithms, then the key two approaches to read up on would probably be Mikolov's word2vec (https://arxiv.org/abs/1301.3781) with the CBOW (Continuous Bag of Words) and Continuous Skip-Gram Model, which are based on relatively simple math optimization, and then on the BERT (https://arxiv.org/abs/1810.04805) structure which does a conceptually similar thing but with a large neural network that can learn more from the same data. For both of them, you can either read the original papers or look up blog posts or videos that explain them, different people have different preferences on how readable academic papers are.

Could you post a link to "the BERT paper"? I've read some, but would be interested reading anything that anyone considered definitive :) Is it this one? "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" :https://arxiv.org/abs/1810.04805

Rust crate rust_bert implementation of the BERT language model (https://arxiv.org/abs/1810.04805 Devlin, Chang, Lee, Toutanova, 2018). The base model is implemented in the bert_model::BertModel struct. Several language model heads have also been implemented, including:

The achievement of training a BERT model to 90% of the GLUE score on a single GPU in ~100 hours is indeed impressive. As for the original BERT pretraining run, the paper [1] mentions that the pretraining took 4 days on 16 TPU chips for the BERT-Base model and 4 days on 64 TPU chips for the BERT-Large model.

Regarding the translation of these techniques to the pretraining phase for a GPT model, it is possible that some of the optimizations and techniques used for BERT could be applied to GPT as well. However, the specific architecture and training objectives of GPT might require different approaches or additional optimizations.

As for the SOPHIA optimizer, it is designed to improve the training of deep learning models by adaptively adjusting the learning rate and momentum. According to the paper [2], SOPHIA has shown promising results in various deep learning tasks. It is possible that the SOPHIA optimizer could help improve the training of BERT and GPT models, but further research and experimentation would be needed to confirm its effectiveness in these specific cases.

[1] https://arxiv.org/abs/1810.04805

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